diff --git a/.idea/inspectionProfiles/Project_Default.xml b/.idea/inspectionProfiles/Project_Default.xml new file mode 100644 index 0000000..9b95f7e --- /dev/null +++ b/.idea/inspectionProfiles/Project_Default.xml @@ -0,0 +1,81 @@ + + + + \ No newline at end of file diff --git a/.idea/inspectionProfiles/profiles_settings.xml b/.idea/inspectionProfiles/profiles_settings.xml new file mode 100644 index 0000000..105ce2d --- /dev/null +++ b/.idea/inspectionProfiles/profiles_settings.xml @@ -0,0 +1,6 @@ + + + + \ No newline at end of file diff --git a/LICENSE.MIT b/LICENSE.MIT new file mode 100644 index 0000000..8edb8c4 --- /dev/null +++ b/LICENSE.MIT @@ -0,0 +1,21 @@ +MIT License + +Copyright (c) 2019 + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. diff --git a/README.md b/README.md index 3958ad3..1974b36 100644 --- a/README.md +++ b/README.md @@ -1,3 +1,129 @@ -# Retinaface_3point_Detection +# RetinaFace in PyTorch -二维码三点方向检测 \ No newline at end of file +A [PyTorch](https://pytorch.org/) implementation of [RetinaFace: Single-stage Dense Face Localisation in the Wild](https://arxiv.org/abs/1905.00641). Model size only 1.7M, when Retinaface use mobilenet0.25 as backbone net. We also provide resnet50 as backbone net to get better result. The official code in Mxnet can be found [here](https://github.com/deepinsight/insightface/tree/master/RetinaFace). + +## Mobile or Edge device deploy +We also provide a set of Face Detector for edge device in [here](https://github.com/biubug6/Face-Detector-1MB-with-landmark) from python training to C++ inference. + +## WiderFace Val Performance in single scale When using Resnet50 as backbone net. +| Style | easy | medium | hard | +|:-|:-:|:-:|:-:| +| Pytorch (same parameter with Mxnet) | 94.82 % | 93.84% | 89.60% | +| Pytorch (original image scale) | 95.48% | 94.04% | 84.43% | +| Mxnet | 94.86% | 93.87% | 88.33% | +| Mxnet(original image scale) | 94.97% | 93.89% | 82.27% | + +## WiderFace Val Performance in single scale When using Mobilenet0.25 as backbone net. +| Style | easy | medium | hard | +|:-|:-:|:-:|:-:| +| Pytorch (same parameter with Mxnet) | 88.67% | 87.09% | 80.99% | +| Pytorch (original image scale) | 90.70% | 88.16% | 73.82% | +| Mxnet | 88.72% | 86.97% | 79.19% | +| Mxnet(original image scale) | 89.58% | 87.11% | 69.12% | +

+ +## FDDB Performance. +| FDDB(pytorch) | performance | +|:-|:-:| +| Mobilenet0.25 | 98.64% | +| Resnet50 | 99.22% | +

+ +### Contents +- [Installation](#installation) +- [Training](#training) +- [Evaluation](#evaluation) +- [TensorRT](#tensorrt) +- [References](#references) + +## Installation +##### Clone and install +1. git clone https://github.com/biubug6/Pytorch_Retinaface.git + +2. Pytorch version 1.1.0+ and torchvision 0.3.0+ are needed. + +3. Codes are based on Python 3 + +##### Data +1. Download the [WIDERFACE](http://shuoyang1213.me/WIDERFACE/WiderFace_Results.html) dataset. + +2. Download annotations (face bounding boxes & five facial landmarks) from [baidu cloud](https://pan.baidu.com/s/1Laby0EctfuJGgGMgRRgykA) or [dropbox](https://www.dropbox.com/s/7j70r3eeepe4r2g/retinaface_gt_v1.1.zip?dl=0) + +3. Organise the dataset directory as follows: + +```Shell + ./data/widerface/ + train/ + images/ + label.txt + val/ + images/ + wider_val.txt +``` +ps: wider_val.txt only include val file names but not label information. + +##### Data1 +We also provide the organized dataset we used as in the above directory structure. + +Link: from [google cloud](https://drive.google.com/open?id=11UGV3nbVv1x9IC--_tK3Uxf7hA6rlbsS) or [baidu cloud](https://pan.baidu.com/s/1jIp9t30oYivrAvrgUgIoLQ) Password: ruck + +## Training +We provide restnet50 and mobilenet0.25 as backbone network to train model. +We trained Mobilenet0.25 on imagenet dataset and get 46.58% in top 1. If you do not wish to train the model, we also provide trained model. Pretrain model and trained model are put in [google cloud](https://drive.google.com/open?id=1oZRSG0ZegbVkVwUd8wUIQx8W7yfZ_ki1) and [baidu cloud](https://pan.baidu.com/s/12h97Fy1RYuqMMIV-RpzdPg) Password: fstq . The model could be put as follows: +```Shell + ./weights/ + mobilenet0.25_Final.pth + mobilenetV1X0.25_pretrain.tar + Resnet50_Final.pth +``` +1. Before training, you can check network configuration (e.g. batch_size, min_sizes and steps etc..) in ``data/config.py and train.py``. + +2. Train the model using WIDER FACE: + ```Shell + CUDA_VISIBLE_DEVICES=0,1,2,3 python train.py --network resnet50 or + CUDA_VISIBLE_DEVICES=0 python train.py --network mobile0.25 + ``` + + +## Evaluation +### Evaluation widerface val +1. Generate txt file +```Shell +python test_widerface.py --trained_model weight_file --network mobile0.25 or resnet50 +``` +2. Evaluate txt results. Demo come from [Here](https://github.com/wondervictor/WiderFace-Evaluation) +```Shell +cd ./widerface_evaluate +python setup.py build_ext --inplace +python evaluation.py +``` +3. You can also use widerface official Matlab evaluate demo in [Here](http://mmlab.ie.cuhk.edu.hk/projects/WIDERFace/WiderFace_Results.html) +### Evaluation FDDB + +1. Download the images [FDDB](https://drive.google.com/open?id=17t4WULUDgZgiSy5kpCax4aooyPaz3GQH) to: +```Shell +./data/FDDB/images/ +``` + +2. Evaluate the trained model using: +```Shell +python test_fddb.py --trained_model weight_file --network mobile0.25 or resnet50 +``` + +3. Download [eval_tool](https://bitbucket.org/marcopede/face-eval) to evaluate the performance. + +

+ +## TensorRT +-[TensorRT](https://github.com/wang-xinyu/tensorrtx/tree/master/retinaface) + +## References +- [FaceBoxes](https://github.com/zisianw/FaceBoxes.PyTorch) +- [Retinaface (mxnet)](https://github.com/deepinsight/insightface/tree/master/RetinaFace) +``` +@inproceedings{deng2019retinaface, +title={RetinaFace: Single-stage Dense Face Localisation in the Wild}, +author={Deng, Jiankang and Guo, Jia and Yuxiang, Zhou and Jinke Yu and Irene Kotsia and Zafeiriou, Stefanos}, +booktitle={arxiv}, +year={2019} +``` diff --git a/convert_to_onnx.py b/convert_to_onnx.py new file mode 100644 index 0000000..e7f3279 --- /dev/null +++ b/convert_to_onnx.py @@ -0,0 +1,88 @@ +from __future__ import print_function +import os +import argparse +import torch +import torch.backends.cudnn as cudnn +import numpy as np +from data import cfg_mnet, cfg_re50 +from layers.functions.prior_box import PriorBox +from utils.nms.py_cpu_nms import py_cpu_nms +import cv2 +from models.retinaface import RetinaFace +from utils.box_utils import decode, decode_landm +from utils.timer import Timer + + +parser = argparse.ArgumentParser(description='Test') +parser.add_argument('-m', '--trained_model', default='./weights/mobilenet0.25_Final.pth', + type=str, help='Trained state_dict file path to open') +parser.add_argument('--network', default='mobile0.25', help='Backbone network mobile0.25 or resnet50') +parser.add_argument('--long_side', default=640, help='when origin_size is false, long_side is scaled size(320 or 640 for long side)') +parser.add_argument('--cpu', action="store_true", default=True, help='Use cpu inference') + +args = parser.parse_args() + + +def check_keys(model, pretrained_state_dict): + ckpt_keys = set(pretrained_state_dict.keys()) + model_keys = set(model.state_dict().keys()) + used_pretrained_keys = model_keys & ckpt_keys + unused_pretrained_keys = ckpt_keys - model_keys + missing_keys = model_keys - ckpt_keys + print('Missing keys:{}'.format(len(missing_keys))) + print('Unused checkpoint keys:{}'.format(len(unused_pretrained_keys))) + print('Used keys:{}'.format(len(used_pretrained_keys))) + assert len(used_pretrained_keys) > 0, 'load NONE from pretrained checkpoint' + return True + + +def remove_prefix(state_dict, prefix): + ''' Old style model is stored with all names of parameters sharing common prefix 'module.' ''' + print('remove prefix \'{}\''.format(prefix)) + f = lambda x: x.split(prefix, 1)[-1] if x.startswith(prefix) else x + return {f(key): value for key, value in state_dict.items()} + + +def load_model(model, pretrained_path, load_to_cpu): + print('Loading pretrained model from {}'.format(pretrained_path)) + if load_to_cpu: + pretrained_dict = torch.load(pretrained_path, map_location=lambda storage, loc: storage) + else: + device = torch.cuda.current_device() + pretrained_dict = torch.load(pretrained_path, map_location=lambda storage, loc: storage.cuda(device)) + if "state_dict" in pretrained_dict.keys(): + pretrained_dict = remove_prefix(pretrained_dict['state_dict'], 'module.') + else: + pretrained_dict = remove_prefix(pretrained_dict, 'module.') + check_keys(model, pretrained_dict) + model.load_state_dict(pretrained_dict, strict=False) + return model + + +if __name__ == '__main__': + torch.set_grad_enabled(False) + cfg = None + if args.network == "mobile0.25": + cfg = cfg_mnet + 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/dev/null and b/data/widerface/保留/val/images/47--Matador_Bullfighter/47_Matador_Bullfighter_Matador_Bullfighter_47_827.jpg differ diff --git a/data/widerface/保留/val/images/47--Matador_Bullfighter/47_Matador_Bullfighter_Matador_Bullfighter_47_874.jpg b/data/widerface/保留/val/images/47--Matador_Bullfighter/47_Matador_Bullfighter_Matador_Bullfighter_47_874.jpg new file mode 100644 index 0000000..b4708d3 Binary files /dev/null and b/data/widerface/保留/val/images/47--Matador_Bullfighter/47_Matador_Bullfighter_Matador_Bullfighter_47_874.jpg differ diff --git a/data/widerface/保留/val/timer.py b/data/widerface/保留/val/timer.py new file mode 100644 index 0000000..e4b3b80 --- /dev/null +++ b/data/widerface/保留/val/timer.py @@ -0,0 +1,40 @@ +# -------------------------------------------------------- +# Fast R-CNN +# Copyright (c) 2015 Microsoft +# Licensed under The MIT License [see LICENSE for details] +# Written by Ross Girshick +# -------------------------------------------------------- + +import time + + +class Timer(object): + """A simple timer.""" + def __init__(self): + self.total_time = 0. + self.calls = 0 + self.start_time = 0. + self.diff = 0. + self.average_time = 0. + + def tic(self): + # using time.time instead of time.clock because time time.clock + # does not normalize for multithreading + self.start_time = time.time() + + def toc(self, average=True): + self.diff = time.time() - self.start_time + self.total_time += self.diff + self.calls += 1 + self.average_time = self.total_time / self.calls + if average: + return self.average_time + else: + return self.diff + + def clear(self): + self.total_time = 0. + self.calls = 0 + self.start_time = 0. + self.diff = 0. + self.average_time = 0. diff --git a/data/widerface/保留/val/wider_val.txt b/data/widerface/保留/val/wider_val.txt new file mode 100644 index 0000000..04d6e87 --- /dev/null +++ b/data/widerface/保留/val/wider_val.txt @@ -0,0 +1,3226 @@ +/24--Soldier_Firing/24_Soldier_Firing_Soldier_Firing_24_329.jpg 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+/9--Press_Conference/9_Press_Conference_Press_Conference_9_828.jpg +/9--Press_Conference/9_Press_Conference_Press_Conference_9_849.jpg +/9--Press_Conference/9_Press_Conference_Press_Conference_9_872.jpg +/9--Press_Conference/9_Press_Conference_Press_Conference_9_883.jpg +/9--Press_Conference/9_Press_Conference_Press_Conference_9_89.jpg +/9--Press_Conference/9_Press_Conference_Press_Conference_9_907.jpg +/9--Press_Conference/9_Press_Conference_Press_Conference_9_924.jpg +/9--Press_Conference/9_Press_Conference_Press_Conference_9_930.jpg +/9--Press_Conference/9_Press_Conference_Press_Conference_9_933.jpg +/9--Press_Conference/9_Press_Conference_Press_Conference_9_945.jpg diff --git a/data/widerface/备份/3个关键点/label.txt b/data/widerface/备份/3个关键点/label.txt new file mode 100644 index 0000000..7cf6889 --- /dev/null +++ b/data/widerface/备份/3个关键点/label.txt @@ -0,0 +1,10 @@ +# 0--Parade/0_Parade_marchingband_1_849.jpg +449 330 122 149 488.906 373.643 0.0 542.089 376.442 0.0 515.031 412.83 0.0 0.82 +# 0--Parade/0_Parade_Parade_0_904.jpg +361 98 263 339 424.143 251.656 0.0 547.134 232.571 0.0 494.121 325.875 0.0 0.89 +# 0--Parade/0_Parade_marchingband_1_799.jpg +78 238 14 17 84.188 244.607 1.0 89.527 244.491 1.0 86.973 247.857 1.0 0.36 +# 0--Parade/0_Parade_marchingband_1_117.jpg +69 359 50 36 92.232 391.397 1.0 94.451 377.451 1.0 103.326 386.326 0.0 0.53 +# 0--Parade/0_Parade_marchingband_1_778.jpg +293 161 52 59 306.875 185.125 1.0 326.0 184.75 1.0 315.125 196.0 0.0 0.57 \ No newline at end of file diff --git a/data/widerface/备份/label.txt b/data/widerface/备份/label.txt new file mode 100644 index 0000000..7cf6889 --- /dev/null +++ b/data/widerface/备份/label.txt @@ -0,0 +1,10 @@ +# 0--Parade/0_Parade_marchingband_1_849.jpg +449 330 122 149 488.906 373.643 0.0 542.089 376.442 0.0 515.031 412.83 0.0 0.82 +# 0--Parade/0_Parade_Parade_0_904.jpg +361 98 263 339 424.143 251.656 0.0 547.134 232.571 0.0 494.121 325.875 0.0 0.89 +# 0--Parade/0_Parade_marchingband_1_799.jpg +78 238 14 17 84.188 244.607 1.0 89.527 244.491 1.0 86.973 247.857 1.0 0.36 +# 0--Parade/0_Parade_marchingband_1_117.jpg +69 359 50 36 92.232 391.397 1.0 94.451 377.451 1.0 103.326 386.326 0.0 0.53 +# 0--Parade/0_Parade_marchingband_1_778.jpg +293 161 52 59 306.875 185.125 1.0 326.0 184.75 1.0 315.125 196.0 0.0 0.57 \ No newline at end of file diff --git a/data/备份/__init__.py b/data/备份/__init__.py new file mode 100644 index 0000000..9764abf --- /dev/null +++ b/data/备份/__init__.py @@ -0,0 +1 @@ +# -*- coding: UTF-8 -*- \ No newline at end of file diff --git a/data/备份/data_augment.py b/data/备份/data_augment.py new file mode 100644 index 0000000..c1b52ae --- /dev/null +++ b/data/备份/data_augment.py @@ -0,0 +1,237 @@ +import cv2 +import numpy as np +import random +from utils.box_utils import matrix_iof + + +def _crop(image, boxes, labels, landm, img_dim): + height, width, _ = image.shape + pad_image_flag = True + + for _ in range(250): + """ + if random.uniform(0, 1) <= 0.2: + scale = 1.0 + else: + scale = random.uniform(0.3, 1.0) + """ + PRE_SCALES = [0.3, 0.45, 0.6, 0.8, 1.0] + scale = random.choice(PRE_SCALES) + short_side = min(width, height) + w = int(scale * short_side) + h = w + + if width == w: + l = 0 + else: + l = random.randrange(width - w) + if height == h: + t = 0 + else: + t = random.randrange(height - h) + roi = np.array((l, t, l + w, t + h)) + + value = matrix_iof(boxes, roi[np.newaxis]) + flag = (value >= 1) + if not flag.any(): + continue + + centers = (boxes[:, :2] + boxes[:, 2:]) / 2 + mask_a = np.logical_and(roi[:2] < centers, centers < roi[2:]).all(axis=1) + boxes_t = boxes[mask_a].copy() + labels_t = labels[mask_a].copy() + landms_t = landm[mask_a].copy() + landms_t = landms_t.reshape([-1, 5, 2]) + + if boxes_t.shape[0] == 0: + continue + + image_t = image[roi[1]:roi[3], roi[0]:roi[2]] + + boxes_t[:, :2] = np.maximum(boxes_t[:, :2], roi[:2]) + boxes_t[:, :2] -= roi[:2] + boxes_t[:, 2:] = np.minimum(boxes_t[:, 2:], roi[2:]) + boxes_t[:, 2:] -= roi[:2] + + # landm + landms_t[:, :, :2] = landms_t[:, :, :2] - roi[:2] + landms_t[:, :, :2] = np.maximum(landms_t[:, :, :2], np.array([0, 0])) + landms_t[:, :, :2] = np.minimum(landms_t[:, :, :2], roi[2:] - roi[:2]) + landms_t = landms_t.reshape([-1, 10]) + + + # make sure that the cropped image contains at least one face > 16 pixel at training image scale + b_w_t = (boxes_t[:, 2] - boxes_t[:, 0] + 1) / w * img_dim + b_h_t = (boxes_t[:, 3] - boxes_t[:, 1] + 1) / h * img_dim + mask_b = np.minimum(b_w_t, b_h_t) > 0.0 + boxes_t = boxes_t[mask_b] + labels_t = labels_t[mask_b] + landms_t = landms_t[mask_b] + + if boxes_t.shape[0] == 0: + continue + + pad_image_flag = False + + return image_t, boxes_t, labels_t, landms_t, pad_image_flag + return image, boxes, labels, landm, pad_image_flag + + +def _distort(image): + + def _convert(image, alpha=1, beta=0): + tmp = image.astype(float) * alpha + beta + tmp[tmp < 0] = 0 + tmp[tmp > 255] = 255 + image[:] = tmp + + image = image.copy() + + if random.randrange(2): + + #brightness distortion + if random.randrange(2): + _convert(image, beta=random.uniform(-32, 32)) + + #contrast distortion + if random.randrange(2): + _convert(image, alpha=random.uniform(0.5, 1.5)) + + image = cv2.cvtColor(image, cv2.COLOR_BGR2HSV) + + #saturation distortion + if random.randrange(2): + _convert(image[:, :, 1], alpha=random.uniform(0.5, 1.5)) + + #hue distortion + if random.randrange(2): + tmp = image[:, :, 0].astype(int) + random.randint(-18, 18) + tmp %= 180 + image[:, :, 0] = tmp + + image = cv2.cvtColor(image, cv2.COLOR_HSV2BGR) + + else: + + #brightness distortion + if random.randrange(2): + _convert(image, beta=random.uniform(-32, 32)) + + image = cv2.cvtColor(image, cv2.COLOR_BGR2HSV) + + #saturation distortion + if random.randrange(2): + _convert(image[:, :, 1], alpha=random.uniform(0.5, 1.5)) + + #hue distortion + if random.randrange(2): + tmp = image[:, :, 0].astype(int) + random.randint(-18, 18) + tmp %= 180 + image[:, :, 0] = tmp + + image = cv2.cvtColor(image, cv2.COLOR_HSV2BGR) + + #contrast distortion + if random.randrange(2): + _convert(image, alpha=random.uniform(0.5, 1.5)) + + return image + + +def _expand(image, boxes, fill, p): + if random.randrange(2): + return image, boxes + + height, width, depth = image.shape + + scale = random.uniform(1, p) + w = int(scale * width) + h = int(scale * height) + + left = random.randint(0, w - width) + top = random.randint(0, h - height) + + boxes_t = boxes.copy() + boxes_t[:, :2] += (left, top) + boxes_t[:, 2:] += (left, top) + expand_image = np.empty( + (h, w, depth), + dtype=image.dtype) + expand_image[:, :] = fill + expand_image[top:top + height, left:left + width] = image + image = expand_image + + return image, boxes_t + + +def _mirror(image, boxes, landms): + _, width, _ = image.shape + if random.randrange(2): + image = image[:, ::-1] + boxes = boxes.copy() + boxes[:, 0::2] = width - boxes[:, 2::-2] + + # landm + landms = landms.copy() + landms = landms.reshape([-1, 5, 2]) + landms[:, :, 0] = width - landms[:, :, 0] + tmp = landms[:, 1, :].copy() + landms[:, 1, :] = landms[:, 0, :] + landms[:, 0, :] = tmp + tmp1 = landms[:, 4, :].copy() + landms[:, 4, :] = landms[:, 3, :] + landms[:, 3, :] = tmp1 + landms = landms.reshape([-1, 10]) + + return image, boxes, landms + + +def _pad_to_square(image, rgb_mean, pad_image_flag): + if not pad_image_flag: + return image + height, width, _ = image.shape + long_side = max(width, height) + image_t = np.empty((long_side, long_side, 3), dtype=image.dtype) + image_t[:, :] = rgb_mean + image_t[0:0 + height, 0:0 + width] = image + return image_t + + +def _resize_subtract_mean(image, insize, rgb_mean): + interp_methods = [cv2.INTER_LINEAR, cv2.INTER_CUBIC, cv2.INTER_AREA, cv2.INTER_NEAREST, cv2.INTER_LANCZOS4] + interp_method = interp_methods[random.randrange(5)] + image = cv2.resize(image, (insize, insize), interpolation=interp_method) + image = image.astype(np.float32) + image -= rgb_mean + return image.transpose(2, 0, 1) + + +class preproc(object): + + def __init__(self, img_dim, rgb_means): + self.img_dim = img_dim + self.rgb_means = rgb_means + + def __call__(self, image, targets): + assert targets.shape[0] > 0, "this image does not have gt" + + boxes = targets[:, :4].copy() + labels = targets[:, -1].copy() + landm = targets[:, 4:-1].copy() + + image_t, boxes_t, labels_t, landm_t, pad_image_flag = _crop(image, boxes, labels, landm, self.img_dim) + image_t = _distort(image_t) + image_t = _pad_to_square(image_t,self.rgb_means, pad_image_flag) + image_t, boxes_t, landm_t = _mirror(image_t, boxes_t, landm_t) + height, width, _ = image_t.shape + image_t = _resize_subtract_mean(image_t, self.img_dim, self.rgb_means) + boxes_t[:, 0::2] /= width + boxes_t[:, 1::2] /= height + + landm_t[:, 0::2] /= width + landm_t[:, 1::2] /= height + + labels_t = np.expand_dims(labels_t, 1) + targets_t = np.hstack((boxes_t, landm_t, labels_t)) + + return image_t, targets_t diff --git a/data/备份/data_augment___.py b/data/备份/data_augment___.py new file mode 100644 index 0000000..a9b7d47 --- /dev/null +++ b/data/备份/data_augment___.py @@ -0,0 +1,242 @@ +import cv2 +import numpy as np +import random +from utils.box_utils import matrix_iof + + +def _crop(image, boxes, labels, landm, img_dim): + height, width, _ = image.shape + pad_image_flag = True + + for _ in range(250): + """ + if random.uniform(0, 1) <= 0.2: + scale = 1.0 + else: + scale = random.uniform(0.3, 1.0) + """ + PRE_SCALES = [0.3, 0.45, 0.6, 0.8, 1.0] + scale = random.choice(PRE_SCALES) + short_side = min(width, height) + w = int(scale * short_side) + h = w + + if width == w: + l = 0 + else: + l = random.randrange(width - w) + if height == h: + t = 0 + else: + t = random.randrange(height - h) + roi = np.array((l, t, l + w, t + h)) + + value = matrix_iof(boxes, roi[np.newaxis]) + flag = (value >= 1) + if not flag.any(): + continue + + centers = (boxes[:, :2] + boxes[:, 2:]) / 2 + mask_a = np.logical_and(roi[:2] < centers, centers < roi[2:]).all(axis=1) + boxes_t = boxes[mask_a].copy() + labels_t = labels[mask_a].copy() + landms_t = landm[mask_a].copy() + # landms_t = landms_t.reshape([-1, 5, 2]) + landms_t = landms_t.reshape([-1, 3, 2]) + + if boxes_t.shape[0] == 0: + continue + + image_t = image[roi[1]:roi[3], roi[0]:roi[2]] + + boxes_t[:, :2] = np.maximum(boxes_t[:, :2], roi[:2]) + boxes_t[:, :2] -= roi[:2] + boxes_t[:, 2:] = np.minimum(boxes_t[:, 2:], roi[2:]) + boxes_t[:, 2:] -= roi[:2] + + # landm + landms_t[:, :, :2] = landms_t[:, :, :2] - roi[:2] + landms_t[:, :, :2] = np.maximum(landms_t[:, :, :2], np.array([0, 0])) + landms_t[:, :, :2] = np.minimum(landms_t[:, :, :2], roi[2:] - roi[:2]) + # landms_t = landms_t.reshape([-1, 10]) + landms_t = landms_t.reshape([-1, 6]) + + + # make sure that the cropped image contains at least one face > 16 pixel at training image scale + b_w_t = (boxes_t[:, 2] - boxes_t[:, 0] + 1) / w * img_dim + b_h_t = (boxes_t[:, 3] - boxes_t[:, 1] + 1) / h * img_dim + mask_b = np.minimum(b_w_t, b_h_t) > 0.0 + boxes_t = boxes_t[mask_b] + labels_t = labels_t[mask_b] + landms_t = landms_t[mask_b] + + if boxes_t.shape[0] == 0: + continue + + pad_image_flag = False + + return image_t, boxes_t, labels_t, landms_t, pad_image_flag + return image, boxes, labels, landm, pad_image_flag + + +def _distort(image): + + def _convert(image, alpha=1, beta=0): + tmp = image.astype(float) * alpha + beta + tmp[tmp < 0] = 0 + tmp[tmp > 255] = 255 + image[:] = tmp + + image = image.copy() + + if random.randrange(2): + + #brightness distortion + if random.randrange(2): + _convert(image, beta=random.uniform(-32, 32)) + + #contrast distortion + if random.randrange(2): + _convert(image, alpha=random.uniform(0.5, 1.5)) + + image = cv2.cvtColor(image, cv2.COLOR_BGR2HSV) + + #saturation distortion + if random.randrange(2): + _convert(image[:, :, 1], alpha=random.uniform(0.5, 1.5)) + + #hue distortion + if random.randrange(2): + tmp = image[:, :, 0].astype(int) + random.randint(-18, 18) + tmp %= 180 + image[:, :, 0] = tmp + + image = cv2.cvtColor(image, cv2.COLOR_HSV2BGR) + + else: + + #brightness distortion + if random.randrange(2): + _convert(image, beta=random.uniform(-32, 32)) + + image = cv2.cvtColor(image, cv2.COLOR_BGR2HSV) + + #saturation distortion + if random.randrange(2): + _convert(image[:, :, 1], alpha=random.uniform(0.5, 1.5)) + + #hue distortion + if random.randrange(2): + tmp = image[:, :, 0].astype(int) + random.randint(-18, 18) + tmp %= 180 + image[:, :, 0] = tmp + + image = cv2.cvtColor(image, cv2.COLOR_HSV2BGR) + + #contrast distortion + if random.randrange(2): + _convert(image, alpha=random.uniform(0.5, 1.5)) + + return image + + +def _expand(image, boxes, fill, p): + if random.randrange(2): + return image, boxes + + height, width, depth = image.shape + + scale = random.uniform(1, p) + w = int(scale * width) + h = int(scale * height) + + left = random.randint(0, w - width) + top = random.randint(0, h - height) + + boxes_t = boxes.copy() + boxes_t[:, :2] += (left, top) + boxes_t[:, 2:] += (left, top) + expand_image = np.empty( + (h, w, depth), + dtype=image.dtype) + expand_image[:, :] = fill + expand_image[top:top + height, left:left + width] = image + image = expand_image + + return image, boxes_t + + +def _mirror(image, boxes, landms): + _, width, _ = image.shape + if random.randrange(2): + image = image[:, ::-1] + boxes = boxes.copy() + boxes[:, 0::2] = width - boxes[:, 2::-2] + + # landm + landms = landms.copy() + # landms = landms.reshape([-1, 5, 2]) + landms = landms.reshape([-1, 3, 2]) + landms[:, :, 0] = width - landms[:, :, 0] + tmp = landms[:, 1, :].copy() + landms[:, 1, :] = landms[:, 0, :] + landms[:, 0, :] = tmp + tmp1 = landms[:, 4, :].copy() + landms[:, 4, :] = landms[:, 3, :] + landms[:, 3, :] = tmp1 + # landms = landms.reshape([-1, 10]) + landms = landms.reshape([-1, 6]) + + return image, boxes, landms + + +def _pad_to_square(image, rgb_mean, pad_image_flag): + if not pad_image_flag: + return image + height, width, _ = image.shape + long_side = max(width, height) + image_t = np.empty((long_side, long_side, 3), dtype=image.dtype) + image_t[:, :] = rgb_mean + image_t[0:0 + height, 0:0 + width] = image + return image_t + + +def _resize_subtract_mean(image, insize, rgb_mean): + interp_methods = [cv2.INTER_LINEAR, cv2.INTER_CUBIC, cv2.INTER_AREA, cv2.INTER_NEAREST, cv2.INTER_LANCZOS4] + interp_method = interp_methods[random.randrange(5)] + image = cv2.resize(image, (insize, insize), interpolation=interp_method) + image = image.astype(np.float32) + image -= rgb_mean + return image.transpose(2, 0, 1) + + +class preproc(object): + + def __init__(self, img_dim, rgb_means): + self.img_dim = img_dim + self.rgb_means = rgb_means + + def __call__(self, image, targets): + assert targets.shape[0] > 0, "this image does not have gt" + + boxes = targets[:, :4].copy() + labels = targets[:, -1].copy() + # landm = targets[:, 4:-1].copy() + landm = targets[:, 4:-1].copy() + + image_t, boxes_t, labels_t, landm_t, pad_image_flag = _crop(image, boxes, labels, landm, self.img_dim) + image_t = _distort(image_t) + image_t = _pad_to_square(image_t,self.rgb_means, pad_image_flag) + image_t, boxes_t, landm_t = _mirror(image_t, boxes_t, landm_t) + height, width, _ = image_t.shape + image_t = _resize_subtract_mean(image_t, self.img_dim, self.rgb_means) + boxes_t[:, 0::2] /= width + boxes_t[:, 1::2] /= height + + landm_t[:, 0::2] /= width + landm_t[:, 1::2] /= height + + labels_t = np.expand_dims(labels_t, 1) + targets_t = np.hstack((boxes_t, landm_t, labels_t)) + + return image_t, targets_t diff --git a/data/备份/wider_face.py b/data/备份/wider_face.py new file mode 100644 index 0000000..22f56ef --- /dev/null +++ b/data/备份/wider_face.py @@ -0,0 +1,101 @@ +import os +import os.path +import sys +import torch +import torch.utils.data as data +import cv2 +import numpy as np + +class WiderFaceDetection(data.Dataset): + def __init__(self, txt_path, preproc=None): + self.preproc = preproc + self.imgs_path = [] + self.words = [] + f = open(txt_path,'r') + lines = f.readlines() + isFirst = True + labels = [] + for line in lines: + line = line.rstrip() + if line.startswith('#'): + if isFirst is True: + isFirst = False + else: + labels_copy = labels.copy() + self.words.append(labels_copy) + labels.clear() + path = line[2:] + path = txt_path.replace('label.txt','images/') + path + self.imgs_path.append(path) + else: + line = line.split(' ') + label = [float(x) for x in line] + labels.append(label) + + self.words.append(labels) + + def __len__(self): + return len(self.imgs_path) + + def __getitem__(self, index): + img = cv2.imread(self.imgs_path[index]) + height, width, _ = img.shape + + labels = self.words[index] + annotations = np.zeros((0, 15)) + if len(labels) == 0: + return annotations + for idx, label in enumerate(labels): + annotation = np.zeros((1, 15)) + # bbox + annotation[0, 0] = label[0] # x1 + annotation[0, 1] = label[1] # y1 + annotation[0, 2] = label[0] + label[2] # x2 + annotation[0, 3] = label[1] + label[3] # y2 + + # landmarks + annotation[0, 4] = label[4] # l0_x + annotation[0, 5] = label[5] # l0_y + annotation[0, 6] = label[7] # l1_x + annotation[0, 7] = label[8] # l1_y + annotation[0, 8] = label[10] # l2_x + annotation[0, 9] = label[11] # l2_y + annotation[0, 10] = label[13] # l3_x + annotation[0, 11] = label[14] # l3_y + annotation[0, 12] = label[16] # l4_x + annotation[0, 13] = label[17] # l4_y + if (annotation[0, 4]<0): + annotation[0, 14] = -1 + else: + annotation[0, 14] = 1 + + annotations = np.append(annotations, annotation, axis=0) + target = np.array(annotations) + if self.preproc is not None: + img, target = self.preproc(img, target) + + return torch.from_numpy(img), target + +def detection_collate(batch): + """Custom collate fn for dealing with batches of images that have a different + number of associated object annotations (bounding boxes). + + Arguments: + batch: (tuple) A tuple of tensor images and lists of annotations + + Return: + A tuple containing: + 1) (tensor) batch of images stacked on their 0 dim + 2) (list of tensors) annotations for a given image are stacked on 0 dim + """ + targets = [] + imgs = [] + for _, sample in enumerate(batch): + for _, tup in enumerate(sample): + if torch.is_tensor(tup): + imgs.append(tup) + elif isinstance(tup, type(np.empty(0))): + annos = torch.from_numpy(tup).float() + targets.append(annos) + + return (torch.stack(imgs, 0), targets) diff --git a/data/备份/wider_face___.py b/data/备份/wider_face___.py new file mode 100644 index 0000000..31b9e40 --- /dev/null +++ b/data/备份/wider_face___.py @@ -0,0 +1,108 @@ +import os +import os.path +import sys +import torch +import torch.utils.data as data +import cv2 +import numpy as np + +class WiderFaceDetection(data.Dataset): + def __init__(self, txt_path, preproc=None): + self.preproc = preproc + self.imgs_path = [] + self.words = [] + f = open(txt_path,'r') + lines = f.readlines() + isFirst = True + labels = [] + for line in lines: + line = line.rstrip() + if line.startswith('#'): + if isFirst is True: + isFirst = False + else: + labels_copy = labels.copy() + self.words.append(labels_copy) + labels.clear() + path = line[2:] + path = txt_path.replace('label.txt','images/') + path + self.imgs_path.append(path) + else: + line = line.split(' ') + label = [float(x) for x in line] + labels.append(label) + + self.words.append(labels) + + def __len__(self): + return len(self.imgs_path) + + def __getitem__(self, index): + img = cv2.imread(self.imgs_path[index]) + height, width, _ = img.shape + + labels = self.words[index] + # annotations = np.zeros((0, 15)) + annotations = np.zeros((0, 11)) + if len(labels) == 0: + return annotations + for idx, label in enumerate(labels): + # annotation = np.zeros((1, 15)) + annotation = np.zeros((1, 11)) + # bbox + annotation[0, 0] = label[0] # x1 + annotation[0, 1] = label[1] # y1 + annotation[0, 2] = label[0] + label[2] # x2 + annotation[0, 3] = label[1] + label[3] # y2 + + # landmarks + annotation[0, 4] = label[4] # l0_x + annotation[0, 5] = label[5] # l0_y + annotation[0, 6] = label[7] # l1_x + annotation[0, 7] = label[8] # l1_y + annotation[0, 8] = label[10] # l2_x + annotation[0, 9] = label[11] # l2_y + # annotation[0, 10] = label[13] # l3_x + # annotation[0, 11] = label[14] # l3_y + # annotation[0, 12] = label[16] # l4_x + # annotation[0, 13] = label[17] # l4_y + # if (annotation[0, 4]<0): + # annotation[0, 14] = -1 + # else: + # annotation[0, 14] = 1 + + if (annotation[0, 4]<0): + annotation[0, 10] = -1 + else: + annotation[0, 10] = 1 + + annotations = np.append(annotations, annotation, axis=0) + target = np.array(annotations) + if self.preproc is not None: + img, target = self.preproc(img, target) + + return torch.from_numpy(img), target + +def detection_collate(batch): + """Custom collate fn for dealing with batches of images that have a different + number of associated object annotations (bounding boxes). + + Arguments: + batch: (tuple) A tuple of tensor images and lists of annotations + + Return: + A tuple containing: + 1) (tensor) batch of images stacked on their 0 dim + 2) (list of tensors) annotations for a given image are stacked on 0 dim + """ + targets = [] + imgs = [] + for _, sample in enumerate(batch): + for _, tup in enumerate(sample): + if torch.is_tensor(tup): + imgs.append(tup) + elif isinstance(tup, type(np.empty(0))): + annos = torch.from_numpy(tup).float() + targets.append(annos) + + return (torch.stack(imgs, 0), targets) diff --git a/detect_20221129_for_images.py b/detect_20221129_for_images.py new file mode 100644 index 0000000..7de3cdd --- /dev/null +++ b/detect_20221129_for_images.py @@ -0,0 +1,180 @@ +from __future__ import print_function +import os +import argparse +import torch +import torch.backends.cudnn as cudnn +import numpy as np +from data import cfg_mnet, cfg_re50 +from layers.functions.prior_box import PriorBox +from utils.nms.py_cpu_nms import py_cpu_nms +import cv2 +from models.retinaface import RetinaFace +from utils.box_utils import decode, decode_landm +import time + +parser = argparse.ArgumentParser(description='Retinaface') + +# parser.add_argument('-m', '--trained_model', default='./weights/Resnet50_Final.pth', +# type=str, help='Trained state_dict file path to open') +parser.add_argument('-m', '--trained_model', default='./weights/mobilenet0.25_Final.pth', + type=str, help='Trained state_dict file path to open') +# parser.add_argument('-m', '--trained_model', default='./weights/mobilenet0.25_epoch_245_3dian.pth', +# type=str, help='Trained state_dict file path to open') +# parser.add_argument('--network', default='resnet50', help='Backbone network mobile0.25 or resnet50') +parser.add_argument('--network', default='mobile0.25', help='Backbone network mobile0.25 or resnet50') +parser.add_argument('--cpu', action="store_true", default=False, help='Use cpu inference') +parser.add_argument('--confidence_threshold', default=0.02, type=float, help='confidence_threshold') +parser.add_argument('--top_k', default=5000, type=int, help='top_k') +parser.add_argument('--nms_threshold', default=0.4, type=float, help='nms_threshold') +parser.add_argument('--keep_top_k', default=750, type=int, help='keep_top_k') +parser.add_argument('-s', '--save_image', action="store_true", default=True, help='show detection results') +parser.add_argument('--vis_thres', default=0.2, type=float, help='visualization_threshold') +args = parser.parse_args() + + +def check_keys(model, pretrained_state_dict): + ckpt_keys = set(pretrained_state_dict.keys()) + model_keys = set(model.state_dict().keys()) + used_pretrained_keys = model_keys & ckpt_keys + unused_pretrained_keys = ckpt_keys - model_keys + missing_keys = model_keys - ckpt_keys + print('Missing keys:{}'.format(len(missing_keys))) + print('Unused checkpoint keys:{}'.format(len(unused_pretrained_keys))) + print('Used keys:{}'.format(len(used_pretrained_keys))) + assert len(used_pretrained_keys) > 0, 'load NONE from pretrained checkpoint' + return True + + +def remove_prefix(state_dict, prefix): + ''' Old style model is stored with all names of parameters sharing common prefix 'module.' ''' + print('remove prefix \'{}\''.format(prefix)) + f = lambda x: x.split(prefix, 1)[-1] if x.startswith(prefix) else x + return {f(key): value for key, value in state_dict.items()} + + +def load_model(model, pretrained_path, load_to_cpu): + print('Loading pretrained model from {}'.format(pretrained_path)) + if load_to_cpu: + pretrained_dict = torch.load(pretrained_path, map_location=lambda storage, loc: storage) + else: + device = torch.cuda.current_device() + pretrained_dict = torch.load(pretrained_path, map_location=lambda storage, loc: storage.cuda(device)) + if "state_dict" in pretrained_dict.keys(): + pretrained_dict = remove_prefix(pretrained_dict['state_dict'], 'module.') + else: + pretrained_dict = remove_prefix(pretrained_dict, 'module.') + check_keys(model, pretrained_dict) + model.load_state_dict(pretrained_dict, strict=False) + return model + + +if __name__ == '__main__': + torch.set_grad_enabled(False) + cfg = None + if args.network == "mobile0.25": + cfg = cfg_mnet + elif args.network == "resnet50": + cfg = cfg_re50 + # net and model + net = RetinaFace(cfg=cfg, phase = 'test') + net = load_model(net, args.trained_model, args.cpu) + net.eval() + print('Finished loading model!') + print(net) + cudnn.benchmark = True + device = torch.device("cpu" if args.cpu else "cuda") + net = net.to(device) + + resize = 1 + + # testing begin + url=r"D:\TH\3_keypoint\0_Pytorch_Retinaface-master\curve\validation" #测试地址 + files = os.listdir(url) + + for file in files: + + yyy=r"D:\TH\3_keypoint\0_Pytorch_Retinaface-master\curve\validation"+r"/"+file + + img_raw = cv2.imread(r"D:\TH\3_keypoint\0_Pytorch_Retinaface-master\curve\validation"+r"/"+file, cv2.IMREAD_COLOR) + + img = np.float32(img_raw) + + im_height, im_width, _ = img.shape + scale = torch.Tensor([img.shape[1], img.shape[0], img.shape[1], img.shape[0]]) + img -= (104, 117, 123) + img = img.transpose(2, 0, 1) + img = torch.from_numpy(img).unsqueeze(0) + img = img.to(device) + scale = scale.to(device) + + tic = time.time() + loc, conf, landms = net(img) # forward pass + print('net forward time: {:.4f}'.format(time.time() - tic)) + + priorbox = PriorBox(cfg, image_size=(im_height, im_width)) + priors = priorbox.forward() + priors = priors.to(device) + prior_data = priors.data + boxes = decode(loc.data.squeeze(0), prior_data, cfg['variance']) + boxes = boxes * scale / resize + boxes = boxes.cpu().numpy() + scores = conf.squeeze(0).data.cpu().numpy()[:, 1] + landms = decode_landm(landms.data.squeeze(0), prior_data, cfg['variance']) + # scale1 = torch.Tensor([img.shape[3], img.shape[2], img.shape[3], img.shape[2], + # img.shape[3], img.shape[2], img.shape[3], img.shape[2], + # img.shape[3], img.shape[2]]) + scale1 = torch.Tensor([img.shape[3], img.shape[2], img.shape[3], img.shape[2], + img.shape[3], img.shape[2]]) + scale1 = scale1.to(device) + landms = landms * scale1 / resize + landms = landms.cpu().numpy() + + # ignore low scores + inds = np.where(scores > args.confidence_threshold)[0] + boxes = boxes[inds] + landms = landms[inds] + scores = scores[inds] + + # keep top-K before NMS + order = scores.argsort()[::-1][:args.top_k] + boxes = boxes[order] + landms = landms[order] + scores = scores[order] + + # do NMS + dets = np.hstack((boxes, scores[:, np.newaxis])).astype(np.float32, copy=False) + keep = py_cpu_nms(dets, args.nms_threshold) + # keep = nms(dets, args.nms_threshold,force_cpu=args.cpu) + dets = dets[keep, :] + landms = landms[keep] + + # keep top-K faster NMS + dets = dets[:args.keep_top_k, :] + landms = landms[:args.keep_top_k, :] + + dets = np.concatenate((dets, landms), axis=1) + + # show image + if args.save_image: + for b in dets: + if b[4] < args.vis_thres: + continue + text = "{:.4f}".format(b[4]) + b = list(map(int, b)) + cv2.rectangle(img_raw, (b[0], b[1]), (b[2], b[3]), (0, 0, 255), 2) + cx = b[0] + cy = b[1] + 12 + cv2.putText(img_raw, text, (cx, cy), + cv2.FONT_HERSHEY_DUPLEX, 0.5, (255, 255, 255)) + + # landms + cv2.circle(img_raw, (b[5], b[6]), 1, (0, 0, 255), 4) + cv2.circle(img_raw, (b[7], b[8]), 1, (0, 255, 255), 4) + cv2.circle(img_raw, (b[9], b[10]), 1, (255, 0, 255), 4) + # cv2.circle(img_raw, (b[11], b[12]), 1, (0, 255, 0), 4) #这里是5个关键点的第4个绘图 + # cv2.circle(img_raw, (b[13], b[14]), 1, (255, 0, 0), 4) #这里是5个关键点的第5个绘图 + # save image + + name = r"D:/TH/3_keypoint/0_Pytorch_Retinaface-master/test_result/"+file + cv2.imwrite(name, img_raw) + diff --git a/detect_20221129_for_one_image.py b/detect_20221129_for_one_image.py new file mode 100644 index 0000000..165840f --- /dev/null +++ b/detect_20221129_for_one_image.py @@ -0,0 +1,177 @@ +from __future__ import print_function +import os +import argparse +import torch +import torch.backends.cudnn as cudnn +import numpy as np +from data import cfg_mnet, cfg_re50 +from layers.functions.prior_box import PriorBox +from utils.nms.py_cpu_nms import py_cpu_nms +import cv2 +from models.retinaface import RetinaFace +from utils.box_utils import decode, decode_landm +import time + +parser = argparse.ArgumentParser(description='Retinaface') + +# parser.add_argument('-m', '--trained_model', default='./weights/Resnet50_Final.pth', +# type=str, help='Trained state_dict file path to open') +parser.add_argument('-m', '--trained_model', default='./weights/mobilenet0.25_Final.pth', + type=str, help='Trained state_dict file path to open') +# parser.add_argument('-m', '--trained_model', default='./weights/mobilenet0.25_epoch_245_3dian.pth', +# type=str, help='Trained state_dict file path to open') +# parser.add_argument('--network', default='resnet50', help='Backbone network mobile0.25 or resnet50') +parser.add_argument('--network', default='mobile0.25', help='Backbone network mobile0.25 or resnet50') +parser.add_argument('--cpu', action="store_true", default=False, help='Use cpu inference') +parser.add_argument('--confidence_threshold', default=0.02, type=float, help='confidence_threshold') +parser.add_argument('--top_k', default=5000, type=int, help='top_k') +parser.add_argument('--nms_threshold', default=0.4, type=float, help='nms_threshold') +parser.add_argument('--keep_top_k', default=750, type=int, help='keep_top_k') +parser.add_argument('-s', '--save_image', action="store_true", default=True, help='show detection results') +parser.add_argument('--vis_thres', default=0.2, type=float, help='visualization_threshold') +args = parser.parse_args() + + +def check_keys(model, pretrained_state_dict): + ckpt_keys = set(pretrained_state_dict.keys()) + model_keys = set(model.state_dict().keys()) + used_pretrained_keys = model_keys & ckpt_keys + unused_pretrained_keys = ckpt_keys - model_keys + missing_keys = model_keys - ckpt_keys + print('Missing keys:{}'.format(len(missing_keys))) + print('Unused checkpoint keys:{}'.format(len(unused_pretrained_keys))) + print('Used keys:{}'.format(len(used_pretrained_keys))) + assert len(used_pretrained_keys) > 0, 'load NONE from pretrained checkpoint' + return True + + +def remove_prefix(state_dict, prefix): + ''' Old style model is stored with all names of parameters sharing common prefix 'module.' ''' + print('remove prefix \'{}\''.format(prefix)) + f = lambda x: x.split(prefix, 1)[-1] if x.startswith(prefix) else x + return {f(key): value for key, value in state_dict.items()} + + +def load_model(model, pretrained_path, load_to_cpu): + print('Loading pretrained model from {}'.format(pretrained_path)) + if load_to_cpu: + pretrained_dict = torch.load(pretrained_path, map_location=lambda storage, loc: storage) + else: + device = torch.cuda.current_device() + pretrained_dict = torch.load(pretrained_path, map_location=lambda storage, loc: storage.cuda(device)) + if "state_dict" in pretrained_dict.keys(): + pretrained_dict = remove_prefix(pretrained_dict['state_dict'], 'module.') + else: + pretrained_dict = remove_prefix(pretrained_dict, 'module.') + check_keys(model, pretrained_dict) + model.load_state_dict(pretrained_dict, strict=False) + return model + + +if __name__ == '__main__': + torch.set_grad_enabled(False) + cfg = None + if args.network == "mobile0.25": + cfg = cfg_mnet + elif args.network == "resnet50": + cfg = cfg_re50 + # net and model + net = RetinaFace(cfg=cfg, phase = 'test') + net = load_model(net, args.trained_model, args.cpu) + net.eval() + print('Finished loading model!') + print(net) + cudnn.benchmark = True + device = torch.device("cpu" if args.cpu else "cuda") + net = net.to(device) + + resize = 1 + + # testing begin + for i in range(1): + # image_path = "./curve/test_mobilenet.jpg" + image_path = "./curve/0_10.jpg" + # image_path = "./curve/renlian.png" + img_raw = cv2.imread(image_path, cv2.IMREAD_COLOR) + + img = np.float32(img_raw) + + im_height, im_width, _ = img.shape + scale = torch.Tensor([img.shape[1], img.shape[0], img.shape[1], img.shape[0]]) + img -= (104, 117, 123) + img = img.transpose(2, 0, 1) + img = torch.from_numpy(img).unsqueeze(0) + img = img.to(device) + scale = scale.to(device) + + tic = time.time() + loc, conf, landms = net(img) # forward pass + print('net forward time: {:.4f}'.format(time.time() - tic)) + + priorbox = PriorBox(cfg, image_size=(im_height, im_width)) + priors = priorbox.forward() + priors = priors.to(device) + prior_data = priors.data + boxes = decode(loc.data.squeeze(0), prior_data, cfg['variance']) + boxes = boxes * scale / resize + boxes = boxes.cpu().numpy() + scores = conf.squeeze(0).data.cpu().numpy()[:, 1] + landms = decode_landm(landms.data.squeeze(0), prior_data, cfg['variance']) + # scale1 = torch.Tensor([img.shape[3], img.shape[2], img.shape[3], img.shape[2], + # img.shape[3], img.shape[2], img.shape[3], img.shape[2], + # img.shape[3], img.shape[2]]) + scale1 = torch.Tensor([img.shape[3], img.shape[2], img.shape[3], img.shape[2], + img.shape[3], img.shape[2]]) + scale1 = scale1.to(device) + landms = landms * scale1 / resize + landms = landms.cpu().numpy() + + # ignore low scores + inds = np.where(scores > args.confidence_threshold)[0] + boxes = boxes[inds] + landms = landms[inds] + scores = scores[inds] + + # keep top-K before NMS + order = scores.argsort()[::-1][:args.top_k] + boxes = boxes[order] + landms = landms[order] + scores = scores[order] + + # do NMS + dets = np.hstack((boxes, scores[:, np.newaxis])).astype(np.float32, copy=False) + keep = py_cpu_nms(dets, args.nms_threshold) + # keep = nms(dets, args.nms_threshold,force_cpu=args.cpu) + dets = dets[keep, :] + landms = landms[keep] + + # keep top-K faster NMS + dets = dets[:args.keep_top_k, :] + landms = landms[:args.keep_top_k, :] + + dets = np.concatenate((dets, landms), axis=1) + + # show image + if args.save_image: + for b in dets: + if b[4] < args.vis_thres: + continue + text = "{:.4f}".format(b[4]) + b = list(map(int, b)) + cv2.rectangle(img_raw, (b[0], b[1]), (b[2], b[3]), (0, 0, 255), 2) + cx = b[0] + cy = b[1] + 12 + cv2.putText(img_raw, text, (cx, cy), + cv2.FONT_HERSHEY_DUPLEX, 0.5, (255, 255, 255)) + + # landms + cv2.circle(img_raw, (b[5], b[6]), 1, (0, 0, 255), 4) + cv2.circle(img_raw, (b[7], b[8]), 1, (0, 255, 255), 4) + cv2.circle(img_raw, (b[9], b[10]), 1, (255, 0, 255), 4) + # cv2.circle(img_raw, (b[11], b[12]), 1, (0, 255, 0), 4) #这里是5个关键点的第4个绘图 + # cv2.circle(img_raw, (b[13], b[14]), 1, (255, 0, 0), 4) #这里是5个关键点的第5个绘图 + # save image + + name = "test.jpg" + cv2.imwrite(name, img_raw) + diff --git a/layers/__pycache__/__init__.cpython-37.pyc b/layers/__pycache__/__init__.cpython-37.pyc new file mode 100644 index 0000000..4d75ee5 Binary files /dev/null and b/layers/__pycache__/__init__.cpython-37.pyc differ diff --git a/layers/functions/__pycache__/prior_box.cpython-37.pyc b/layers/functions/__pycache__/prior_box.cpython-37.pyc new file mode 100644 index 0000000..2267e64 Binary files /dev/null and b/layers/functions/__pycache__/prior_box.cpython-37.pyc differ diff --git a/layers/functions/prior_box.py b/layers/functions/prior_box.py new file mode 100644 index 0000000..80c7f85 --- /dev/null +++ b/layers/functions/prior_box.py @@ -0,0 +1,34 @@ +import torch +from itertools import product as product +import numpy as np +from math import ceil + + +class PriorBox(object): + def __init__(self, cfg, image_size=None, phase='train'): + super(PriorBox, self).__init__() + self.min_sizes = cfg['min_sizes'] + self.steps = cfg['steps'] + self.clip = cfg['clip'] + self.image_size = image_size + self.feature_maps = [[ceil(self.image_size[0]/step), ceil(self.image_size[1]/step)] for step in self.steps] + self.name = "s" + + def forward(self): + anchors = [] + for k, f in enumerate(self.feature_maps): + min_sizes = self.min_sizes[k] + for i, j in product(range(f[0]), range(f[1])): + for min_size in min_sizes: + s_kx = min_size / self.image_size[1] + s_ky = min_size / self.image_size[0] + dense_cx = [x * self.steps[k] / self.image_size[1] for x in [j + 0.5]] + dense_cy = [y * self.steps[k] / self.image_size[0] for y in [i + 0.5]] + for cy, cx in product(dense_cy, dense_cx): + anchors += [cx, cy, s_kx, s_ky] + + # back to torch land + output = torch.Tensor(anchors).view(-1, 4) + if self.clip: + output.clamp_(max=1, min=0) + return output diff --git a/layers/modules/__init__.py b/layers/modules/__init__.py new file mode 100644 index 0000000..cf24bdd --- /dev/null +++ b/layers/modules/__init__.py @@ -0,0 +1,3 @@ +from .multibox_loss import MultiBoxLoss + +__all__ = ['MultiBoxLoss'] diff --git a/layers/modules/__pycache__/__init__.cpython-37.pyc b/layers/modules/__pycache__/__init__.cpython-37.pyc new file mode 100644 index 0000000..843940e Binary files /dev/null and b/layers/modules/__pycache__/__init__.cpython-37.pyc differ diff --git a/layers/modules/__pycache__/multibox_loss.cpython-37.pyc b/layers/modules/__pycache__/multibox_loss.cpython-37.pyc new file mode 100644 index 0000000..60f1beb Binary files /dev/null and b/layers/modules/__pycache__/multibox_loss.cpython-37.pyc differ diff --git a/layers/modules/multibox_loss.py b/layers/modules/multibox_loss.py new file mode 100644 index 0000000..804fc0c --- /dev/null +++ b/layers/modules/multibox_loss.py @@ -0,0 +1,132 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F +from torch.autograd import Variable +from utils.box_utils import match, log_sum_exp +from data import cfg_mnet +GPU = cfg_mnet['gpu_train'] + +class MultiBoxLoss(nn.Module): + """SSD Weighted Loss Function + Compute Targets: + 1) Produce Confidence Target Indices by matching ground truth boxes + with (default) 'priorboxes' that have jaccard index > threshold parameter + (default threshold: 0.5). + 2) Produce localization target by 'encoding' variance into offsets of ground + truth boxes and their matched 'priorboxes'. + 3) Hard negative mining to filter the excessive number of negative examples + that comes with using a large number of default bounding boxes. + (default negative:positive ratio 3:1) + Objective Loss: + L(x,c,l,g) = (Lconf(x, c) + αLloc(x,l,g)) / N + Where, Lconf is the CrossEntropy Loss and Lloc is the SmoothL1 Loss + weighted by α which is set to 1 by cross val. + Args: + c: class confidences, + l: predicted boxes, + g: ground truth boxes + N: number of matched default boxes + See: https://arxiv.org/pdf/1512.02325.pdf for more details. + """ + + def __init__(self, num_classes, overlap_thresh, prior_for_matching, bkg_label, neg_mining, neg_pos, neg_overlap, encode_target): + super(MultiBoxLoss, self).__init__() + self.num_classes = num_classes + self.threshold = overlap_thresh + self.background_label = bkg_label + self.encode_target = encode_target + self.use_prior_for_matching = prior_for_matching + self.do_neg_mining = neg_mining + self.negpos_ratio = neg_pos + self.neg_overlap = neg_overlap + self.variance = [0.1, 0.2] + + def forward(self, predictions, priors, targets): + """Multibox Loss + Args: + predictions (tuple): A tuple containing loc preds, conf preds, + and prior boxes from SSD net. + conf shape: torch.size(batch_size,num_priors,num_classes) + loc shape: torch.size(batch_size,num_priors,4) + priors shape: torch.size(num_priors,4) + + ground_truth (tensor): Ground truth boxes and labels for a batch, + shape: [batch_size,num_objs,5] (last idx is the label). + """ + + loc_data, conf_data, landm_data = predictions + # predictions是嵌套，loc_data是这里是2x16800x4,conf_data是2x16800x2,landm_data是2x16800x10 + # landm_data这里是16800x2x10 + priors = priors + # priors这里是16800x4 + num = loc_data.size(0) + num_priors = (priors.size(0)) + + # match priors (default boxes) and ground truth boxes + loc_t = torch.Tensor(num, num_priors, 4) + # landm_t = torch.Tensor(num, num_priors, 10) + landm_t = torch.Tensor(num, num_priors, 6) + conf_t = torch.LongTensor(num, num_priors) + for idx in range(num): + truths = targets[idx][:, :4].data + labels = targets[idx][:, -1].data + # landms = targets[idx][:, 4:14].data + landms = targets[idx][:, 4:10].data + defaults = priors.data + match(self.threshold, truths, defaults, self.variance, labels, landms, loc_t, conf_t, landm_t, idx) + if GPU: + loc_t = loc_t.cuda() + conf_t = conf_t.cuda() + landm_t = landm_t.cuda() + + zeros = torch.tensor(0).cuda() + # landm Loss (Smooth L1) + # Shape: [batch,num_priors,10] + pos1 = conf_t > zeros + num_pos_landm = pos1.long().sum(1, keepdim=True) + N1 = max(num_pos_landm.data.sum().float(), 1) + pos_idx1 = pos1.unsqueeze(pos1.dim()).expand_as(landm_data) + # landm_p = landm_data[pos_idx1].view(-1, 10) #-1是定参数，保证一致 + landm_p = landm_data[pos_idx1].view(-1, 6) #-1是定参数，保证一致 + # landm_t = landm_t[pos_idx1].view(-1, 10) + landm_t = landm_t[pos_idx1].view(-1, 6) + loss_landm = F.smooth_l1_loss(landm_p, landm_t, reduction='sum') + + + pos = conf_t != zeros + conf_t[pos] = 1 + + # Localization Loss (Smooth L1) + # Shape: [batch,num_priors,4] + pos_idx = pos.unsqueeze(pos.dim()).expand_as(loc_data) + loc_p = loc_data[pos_idx].view(-1, 4) + loc_t = loc_t[pos_idx].view(-1, 4) + loss_l = F.smooth_l1_loss(loc_p, loc_t, reduction='sum') + + # Compute max conf across batch for hard negative mining + batch_conf = conf_data.view(-1, self.num_classes) + loss_c = log_sum_exp(batch_conf) - batch_conf.gather(1, conf_t.view(-1, 1)) + + # Hard Negative Mining + loss_c[pos.view(-1, 1)] = 0 # filter out pos boxes for now + loss_c = loss_c.view(num, -1) + _, loss_idx = loss_c.sort(1, descending=True) + _, idx_rank = loss_idx.sort(1) + num_pos = pos.long().sum(1, keepdim=True) + num_neg = torch.clamp(self.negpos_ratio*num_pos, max=pos.size(1)-1) + neg = idx_rank < num_neg.expand_as(idx_rank) + + # Confidence Loss Including Positive and Negative Examples + pos_idx = pos.unsqueeze(2).expand_as(conf_data) + neg_idx = neg.unsqueeze(2).expand_as(conf_data) + conf_p = conf_data[(pos_idx+neg_idx).gt(0)].view(-1,self.num_classes) + targets_weighted = conf_t[(pos+neg).gt(0)] + loss_c = F.cross_entropy(conf_p, targets_weighted, reduction='sum') + + # Sum of losses: L(x,c,l,g) = (Lconf(x, c) + αLloc(x,l,g)) / N + N = max(num_pos.data.sum().float(), 1) + loss_l /= N + loss_c /= N + loss_landm /= N1 + + return loss_l, loss_c, loss_landm diff --git a/models/__pycache__/__init__.cpython-37.pyc 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31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_703.jpg +# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_638.jpg +# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_315.jpg +# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_581.jpg +# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_51.jpg +# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_744.jpg +# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_457.jpg +# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_474.jpg +# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_520.jpg +# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_807.jpg +# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_511.jpg +# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_290.jpg +# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_405.jpg +# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_639.jpg +# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_935.jpg +# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_357.jpg +# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_874.jpg +# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_107.jpg +# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_672.jpg +# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_737.jpg +# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_468.jpg +# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_112.jpg +# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_178.jpg +# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_748.jpg +# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_897.jpg +# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_14.jpg +# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_404.jpg +# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_895.jpg +# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_603.jpg +# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_577.jpg +# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_840.jpg +# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_63.jpg +# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_828.jpg +# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_380.jpg +# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_456.jpg +# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_768.jpg +# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_248.jpg +# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_880.jpg +# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_16.jpg +# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_656.jpg +# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_395.jpg +# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_370.jpg +# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_580.jpg +# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_394.jpg +# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_143.jpg +# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_204.jpg +# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_698.jpg +# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_383.jpg +# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_750.jpg +# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_944.jpg +# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_435.jpg +# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_279.jpg +# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_556.jpg +# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_893.jpg +# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_433.jpg +# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_217.jpg +# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_610.jpg +# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_663.jpg +# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_901.jpg +# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_821.jpg +# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_470.jpg +# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_498.jpg +# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_924.jpg +# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_7.jpg +# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_775.jpg +# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_784.jpg +# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_669.jpg +# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_759.jpg +# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_494.jpg +# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_566.jpg +# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_813.jpg +# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_42.jpg +# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_866.jpg +# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_90.jpg +# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_113.jpg +# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_641.jpg +# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_476.jpg +# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_739.jpg +# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_19.jpg +# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_75.jpg +# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_699.jpg +# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_443.jpg +# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_56.jpg +# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_849.jpg +# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_761.jpg +# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_421.jpg +# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_793.jpg +# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_146.jpg +# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_427.jpg +# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_39.jpg +# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_509.jpg +# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_193.jpg +# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_191.jpg +# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_754.jpg +# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_288.jpg +# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_487.jpg +# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_552.jpg +# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_344.jpg +# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_694.jpg +# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_642.jpg +# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_441.jpg +# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_567.jpg +# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_171.jpg +# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_130.jpg +# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_469.jpg +# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_83.jpg +# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_903.jpg +# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_964.jpg +# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_483.jpg +# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_224.jpg +# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_933.jpg +# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_378.jpg +# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_960.jpg +# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_472.jpg +# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_32.jpg +# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_160.jpg +# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_763.jpg +# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_634.jpg +# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_125.jpg +# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_150.jpg +# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_575.jpg +# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_762.jpg +# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_534.jpg +# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_105.jpg +# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_527.jpg +# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_707.jpg +# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_756.jpg +# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_387.jpg +# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_333.jpg +# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_329.jpg +# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_619.jpg +# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_384.jpg +# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_235.jpg +# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_29.jpg +# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_305.jpg +# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_827.jpg +# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_452.jpg +# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_679.jpg +# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_902.jpg +# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_418.jpg +# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_506.jpg +# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_338.jpg +# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_282.jpg +# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_379.jpg +# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_185.jpg +# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_11.jpg +# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_926.jpg +# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_948.jpg +# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_412.jpg +# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_194.jpg +# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_600.jpg +# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_621.jpg +# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_35.jpg +# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_294.jpg +# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_558.jpg +# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_23.jpg +# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_172.jpg +# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_801.jpg +# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_666.jpg +# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_795.jpg +# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_116.jpg +# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_940.jpg +# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_692.jpg +# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_247.jpg +# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_274.jpg +# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_381.jpg +# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_811.jpg +# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_467.jpg +# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_41.jpg +# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_524.jpg +# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_537.jpg +# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_796.jpg +# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_671.jpg +# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_799.jpg +# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_229.jpg +# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_269.jpg +# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_714.jpg +# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_732.jpg +# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_912.jpg +# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_868.jpg +# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_209.jpg +# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_409.jpg +# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_312.jpg +# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_585.jpg +# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_689.jpg +# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_326.jpg +# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_117.jpg +# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_814.jpg +# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_12.jpg +# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_819.jpg +# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_541.jpg +# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_340.jpg +# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_426.jpg +# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_835.jpg +# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_208.jpg +# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_497.jpg +# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_826.jpg +# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_637.jpg +# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_24.jpg +# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_830.jpg +# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_141.jpg +# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_930.jpg +# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_738.jpg +# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_355.jpg +# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_428.jpg +# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_374.jpg +# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_785.jpg +# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_286.jpg +# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_9.jpg +# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_682.jpg +# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_10.jpg +# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_240.jpg +# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_492.jpg +# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_746.jpg +# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_538.jpg +# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_894.jpg +# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_716.jpg +# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_425.jpg +# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_515.jpg +# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_594.jpg +# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_693.jpg +# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_752.jpg +# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_477.jpg +# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_563.jpg +# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_674.jpg +# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_5.jpg +# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_845.jpg +# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_55.jpg +# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_74.jpg +# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_202.jpg +# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_287.jpg +# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_887.jpg +# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_571.jpg +# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_502.jpg +# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_99.jpg +# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_700.jpg +# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_388.jpg +# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_79.jpg +# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_265.jpg +# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_800.jpg +# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_891.jpg +# 31--Waiter_Waitress/31_Waiter_Waitress_Waiter_Waitress_31_206.jpg +# 32--Worker_Laborer/32_Worker_Laborer_Worker_Laborer_32_835.jpg +# 32--Worker_Laborer/32_Worker_Laborer_Worker_Laborer_32_582.jpg +# 32--Worker_Laborer/32_Worker_Laborer_Worker_Laborer_32_250.jpg +# 32--Worker_Laborer/32_Worker_Laborer_Worker_Laborer_32_350.jpg +# 32--Worker_Laborer/32_Worker_Laborer_Worker_Laborer_32_343.jpg +# 32--Worker_Laborer/32_Worker_Laborer_Worker_Laborer_32_333.jpg +# 32--Worker_Laborer/32_Worker_Laborer_Worker_Laborer_32_964.jpg +# 32--Worker_Laborer/32_Worker_Laborer_Worker_Laborer_32_348.jpg +# 32--Worker_Laborer/32_Worker_Laborer_Worker_Laborer_32_161.jpg +# 32--Worker_Laborer/32_Worker_Laborer_Worker_Laborer_32_551.jpg +# 32--Worker_Laborer/32_Worker_Laborer_Worker_Laborer_32_885.jpg 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54--Rescue/54_Rescue_rescuepeople_54_769.jpg +# 54--Rescue/54_Rescue_firemanrescue_54_673.jpg +# 54--Rescue/54_Rescue_firemanrescue_54_77.jpg +# 55--Sports_Coach_Trainer/55_Sports_Coach_Trainer_sportcoaching_55_706.jpg +# 55--Sports_Coach_Trainer/55_Sports_Coach_Trainer_sportcoaching_55_159.jpg +# 55--Sports_Coach_Trainer/55_Sports_Coach_Trainer_sportcoaching_55_836.jpg +# 55--Sports_Coach_Trainer/55_Sports_Coach_Trainer_sportcoaching_55_125.jpg +# 55--Sports_Coach_Trainer/55_Sports_Coach_Trainer_sportcoaching_55_267.jpg +# 55--Sports_Coach_Trainer/55_Sports_Coach_Trainer_sportcoaching_55_361.jpg +# 55--Sports_Coach_Trainer/55_Sports_Coach_Trainer_sportcoaching_55_175.jpg +# 55--Sports_Coach_Trainer/55_Sports_Coach_Trainer_sportcoaching_55_11.jpg +# 55--Sports_Coach_Trainer/55_Sports_Coach_Trainer_sportcoaching_55_1028.jpg +# 55--Sports_Coach_Trainer/55_Sports_Coach_Trainer_sportcoaching_55_17.jpg +# 55--Sports_Coach_Trainer/55_Sports_Coach_Trainer_sportcoaching_55_231.jpg +# 55--Sports_Coach_Trainer/55_Sports_Coach_Trainer_sportcoaching_55_418.jpg +# 55--Sports_Coach_Trainer/55_Sports_Coach_Trainer_sportcoaching_55_567.jpg +# 55--Sports_Coach_Trainer/55_Sports_Coach_Trainer_sportcoaching_55_264.jpg +# 55--Sports_Coach_Trainer/55_Sports_Coach_Trainer_sportcoaching_55_141.jpg +# 55--Sports_Coach_Trainer/55_Sports_Coach_Trainer_sportcoaching_55_84.jpg +# 55--Sports_Coach_Trainer/55_Sports_Coach_Trainer_sportcoaching_55_97.jpg +# 55--Sports_Coach_Trainer/55_Sports_Coach_Trainer_sportcoaching_55_889.jpg +# 55--Sports_Coach_Trainer/55_Sports_Coach_Trainer_sportcoaching_55_582.jpg +# 55--Sports_Coach_Trainer/55_Sports_Coach_Trainer_sportcoaching_55_885.jpg +# 55--Sports_Coach_Trainer/55_Sports_Coach_Trainer_sportcoaching_55_287.jpg +# 55--Sports_Coach_Trainer/55_Sports_Coach_Trainer_sportcoaching_55_57.jpg +# 55--Sports_Coach_Trainer/55_Sports_Coach_Trainer_sportcoaching_55_879.jpg +# 55--Sports_Coach_Trainer/55_Sports_Coach_Trainer_sportcoaching_55_151.jpg +# 55--Sports_Coach_Trainer/55_Sports_Coach_Trainer_sportcoaching_55_725.jpg +# 55--Sports_Coach_Trainer/55_Sports_Coach_Trainer_sportcoaching_55_272.jpg +# 55--Sports_Coach_Trainer/55_Sports_Coach_Trainer_sportcoaching_55_205.jpg +# 55--Sports_Coach_Trainer/55_Sports_Coach_Trainer_sportcoaching_55_347.jpg +# 55--Sports_Coach_Trainer/55_Sports_Coach_Trainer_sportcoaching_55_617.jpg +# 55--Sports_Coach_Trainer/55_Sports_Coach_Trainer_sportcoaching_55_202.jpg +# 55--Sports_Coach_Trainer/55_Sports_Coach_Trainer_sportcoaching_55_400.jpg +# 55--Sports_Coach_Trainer/55_Sports_Coach_Trainer_sportcoaching_55_212.jpg +# 55--Sports_Coach_Trainer/55_Sports_Coach_Trainer_sportcoaching_55_129.jpg +# 55--Sports_Coach_Trainer/55_Sports_Coach_Trainer_sportcoaching_55_204.jpg +# 55--Sports_Coach_Trainer/55_Sports_Coach_Trainer_sportcoaching_55_558.jpg +# 55--Sports_Coach_Trainer/55_Sports_Coach_Trainer_sportcoaching_55_268.jpg +# 55--Sports_Coach_Trainer/55_Sports_Coach_Trainer_sportcoaching_55_738.jpg +# 55--Sports_Coach_Trainer/55_Sports_Coach_Trainer_sportcoaching_55_397.jpg +# 55--Sports_Coach_Trainer/55_Sports_Coach_Trainer_sportcoaching_55_442.jpg +# 55--Sports_Coach_Trainer/55_Sports_Coach_Trainer_sportcoaching_55_916.jpg +# 55--Sports_Coach_Trainer/55_Sports_Coach_Trainer_sportcoaching_55_184.jpg +# 55--Sports_Coach_Trainer/55_Sports_Coach_Trainer_sportcoaching_55_586.jpg +# 55--Sports_Coach_Trainer/55_Sports_Coach_Trainer_sportcoaching_55_548.jpg +# 55--Sports_Coach_Trainer/55_Sports_Coach_Trainer_sportcoaching_55_926.jpg +# 55--Sports_Coach_Trainer/55_Sports_Coach_Trainer_sportcoaching_55_142.jpg +# 55--Sports_Coach_Trainer/55_Sports_Coach_Trainer_sportcoaching_55_935.jpg +# 55--Sports_Coach_Trainer/55_Sports_Coach_Trainer_sportcoaching_55_1000.jpg +# 55--Sports_Coach_Trainer/55_Sports_Coach_Trainer_sportcoaching_55_763.jpg +# 55--Sports_Coach_Trainer/55_Sports_Coach_Trainer_sportcoaching_55_1023.jpg +# 55--Sports_Coach_Trainer/55_Sports_Coach_Trainer_sportcoaching_55_642.jpg 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55--Sports_Coach_Trainer/55_Sports_Coach_Trainer_sportcoaching_55_906.jpg +# 55--Sports_Coach_Trainer/55_Sports_Coach_Trainer_sportcoaching_55_723.jpg +# 55--Sports_Coach_Trainer/55_Sports_Coach_Trainer_sportcoaching_55_32.jpg +# 55--Sports_Coach_Trainer/55_Sports_Coach_Trainer_sportcoaching_55_674.jpg +# 55--Sports_Coach_Trainer/55_Sports_Coach_Trainer_sportcoaching_55_600.jpg +# 55--Sports_Coach_Trainer/55_Sports_Coach_Trainer_sportcoaching_55_656.jpg +# 55--Sports_Coach_Trainer/55_Sports_Coach_Trainer_sportcoaching_55_320.jpg +# 55--Sports_Coach_Trainer/55_Sports_Coach_Trainer_sportcoaching_55_172.jpg +# 55--Sports_Coach_Trainer/55_Sports_Coach_Trainer_sportcoaching_55_989.jpg +# 55--Sports_Coach_Trainer/55_Sports_Coach_Trainer_sportcoaching_55_201.jpg +# 55--Sports_Coach_Trainer/55_Sports_Coach_Trainer_sportcoaching_55_1006.jpg +# 55--Sports_Coach_Trainer/55_Sports_Coach_Trainer_sportcoaching_55_444.jpg +# 55--Sports_Coach_Trainer/55_Sports_Coach_Trainer_sportcoaching_55_811.jpg +# 55--Sports_Coach_Trainer/55_Sports_Coach_Trainer_sportcoaching_55_1037.jpg +# 55--Sports_Coach_Trainer/55_Sports_Coach_Trainer_sportcoaching_55_880.jpg +# 55--Sports_Coach_Trainer/55_Sports_Coach_Trainer_sportcoaching_55_608.jpg +# 55--Sports_Coach_Trainer/55_Sports_Coach_Trainer_sportcoaching_55_767.jpg +# 55--Sports_Coach_Trainer/55_Sports_Coach_Trainer_sportcoaching_55_353.jpg +# 55--Sports_Coach_Trainer/55_Sports_Coach_Trainer_sportcoaching_55_622.jpg +# 55--Sports_Coach_Trainer/55_Sports_Coach_Trainer_sportcoaching_55_655.jpg +# 55--Sports_Coach_Trainer/55_Sports_Coach_Trainer_sportcoaching_55_850.jpg +# 55--Sports_Coach_Trainer/55_Sports_Coach_Trainer_sportcoaching_55_722.jpg +# 55--Sports_Coach_Trainer/55_Sports_Coach_Trainer_sportcoaching_55_627.jpg +# 55--Sports_Coach_Trainer/55_Sports_Coach_Trainer_sportcoaching_55_440.jpg +# 55--Sports_Coach_Trainer/55_Sports_Coach_Trainer_sportcoaching_55_457.jpg +# 55--Sports_Coach_Trainer/55_Sports_Coach_Trainer_sportcoaching_55_63.jpg +# 55--Sports_Coach_Trainer/55_Sports_Coach_Trainer_sportcoaching_55_488.jpg +# 55--Sports_Coach_Trainer/55_Sports_Coach_Trainer_sportcoaching_55_186.jpg +# 55--Sports_Coach_Trainer/55_Sports_Coach_Trainer_sportcoaching_55_810.jpg +# 55--Sports_Coach_Trainer/55_Sports_Coach_Trainer_sportcoaching_55_974.jpg +# 55--Sports_Coach_Trainer/55_Sports_Coach_Trainer_sportcoaching_55_449.jpg +# 55--Sports_Coach_Trainer/55_Sports_Coach_Trainer_sportcoaching_55_689.jpg +# 55--Sports_Coach_Trainer/55_Sports_Coach_Trainer_sportcoaching_55_681.jpg +# 55--Sports_Coach_Trainer/55_Sports_Coach_Trainer_sportcoaching_55_170.jpg +# 55--Sports_Coach_Trainer/55_Sports_Coach_Trainer_sportcoaching_55_939.jpg +# 55--Sports_Coach_Trainer/55_Sports_Coach_Trainer_sportcoaching_55_881.jpg +# 55--Sports_Coach_Trainer/55_Sports_Coach_Trainer_sportcoaching_55_830.jpg +# 55--Sports_Coach_Trainer/55_Sports_Coach_Trainer_sportcoaching_55_217.jpg +# 55--Sports_Coach_Trainer/55_Sports_Coach_Trainer_sportcoaching_55_168.jpg +# 55--Sports_Coach_Trainer/55_Sports_Coach_Trainer_sportcoaching_55_664.jpg +# 55--Sports_Coach_Trainer/55_Sports_Coach_Trainer_sportcoaching_55_616.jpg +# 55--Sports_Coach_Trainer/55_Sports_Coach_Trainer_sportcoaching_55_994.jpg +# 55--Sports_Coach_Trainer/55_Sports_Coach_Trainer_sportcoaching_55_562.jpg +# 55--Sports_Coach_Trainer/55_Sports_Coach_Trainer_sportcoaching_55_413.jpg +# 55--Sports_Coach_Trainer/55_Sports_Coach_Trainer_sportcoaching_55_535.jpg +# 55--Sports_Coach_Trainer/55_Sports_Coach_Trainer_sportcoaching_55_69.jpg +# 55--Sports_Coach_Trainer/55_Sports_Coach_Trainer_sportcoaching_55_456.jpg +# 55--Sports_Coach_Trainer/55_Sports_Coach_Trainer_sportcoaching_55_211.jpg +# 55--Sports_Coach_Trainer/55_Sports_Coach_Trainer_sportcoaching_55_784.jpg +# 55--Sports_Coach_Trainer/55_Sports_Coach_Trainer_sportcoaching_55_140.jpg +# 55--Sports_Coach_Trainer/55_Sports_Coach_Trainer_sportcoaching_55_240.jpg +# 55--Sports_Coach_Trainer/55_Sports_Coach_Trainer_sportcoaching_55_471.jpg +# 55--Sports_Coach_Trainer/55_Sports_Coach_Trainer_sportcoaching_55_330.jpg +# 55--Sports_Coach_Trainer/55_Sports_Coach_Trainer_sportcoaching_55_667.jpg +# 55--Sports_Coach_Trainer/55_Sports_Coach_Trainer_sportcoaching_55_66.jpg +# 55--Sports_Coach_Trainer/55_Sports_Coach_Trainer_sportcoaching_55_1029.jpg +# 55--Sports_Coach_Trainer/55_Sports_Coach_Trainer_sportcoaching_55_244.jpg +# 55--Sports_Coach_Trainer/55_Sports_Coach_Trainer_sportcoaching_55_386.jpg +# 55--Sports_Coach_Trainer/55_Sports_Coach_Trainer_sportcoaching_55_624.jpg +# 55--Sports_Coach_Trainer/55_Sports_Coach_Trainer_sportcoaching_55_999.jpg +# 55--Sports_Coach_Trainer/55_Sports_Coach_Trainer_sportcoaching_55_452.jpg +# 55--Sports_Coach_Trainer/55_Sports_Coach_Trainer_sportcoaching_55_737.jpg +# 55--Sports_Coach_Trainer/55_Sports_Coach_Trainer_sportcoaching_55_506.jpg +# 55--Sports_Coach_Trainer/55_Sports_Coach_Trainer_sportcoaching_55_163.jpg +# 55--Sports_Coach_Trainer/55_Sports_Coach_Trainer_sportcoaching_55_124.jpg +# 55--Sports_Coach_Trainer/55_Sports_Coach_Trainer_sportcoaching_55_293.jpg +# 55--Sports_Coach_Trainer/55_Sports_Coach_Trainer_sportcoaching_55_238.jpg +# 55--Sports_Coach_Trainer/55_Sports_Coach_Trainer_sportcoaching_55_554.jpg +# 55--Sports_Coach_Trainer/55_Sports_Coach_Trainer_sportcoaching_55_981.jpg +# 55--Sports_Coach_Trainer/55_Sports_Coach_Trainer_sportcoaching_55_271.jpg +# 55--Sports_Coach_Trainer/55_Sports_Coach_Trainer_sportcoaching_55_1022.jpg +# 55--Sports_Coach_Trainer/55_Sports_Coach_Trainer_sportcoaching_55_139.jpg +# 55--Sports_Coach_Trainer/55_Sports_Coach_Trainer_sportcoaching_55_858.jpg +# 55--Sports_Coach_Trainer/55_Sports_Coach_Trainer_sportcoaching_55_366.jpg +# 55--Sports_Coach_Trainer/55_Sports_Coach_Trainer_sportcoaching_55_259.jpg +# 55--Sports_Coach_Trainer/55_Sports_Coach_Trainer_sportcoaching_55_603.jpg +# 55--Sports_Coach_Trainer/55_Sports_Coach_Trainer_sportcoaching_55_294.jpg +# 55--Sports_Coach_Trainer/55_Sports_Coach_Trainer_sportcoaching_55_314.jpg 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58--Hockey/58_Hockey_icehockey_puck_58_632.jpg +# 58--Hockey/58_Hockey_icehockey_puck_58_153.jpg +# 58--Hockey/58_Hockey_icehockey_puck_58_90.jpg +# 58--Hockey/58_Hockey_icehockey_puck_58_801.jpg +# 58--Hockey/58_Hockey_icehockey_puck_58_21.jpg +# 58--Hockey/58_Hockey_icehockey_puck_58_173.jpg +# 58--Hockey/58_Hockey_icehockey_puck_58_796.jpg +# 58--Hockey/58_Hockey_icehockey_puck_58_808.jpg +# 58--Hockey/58_Hockey_icehockey_puck_58_91.jpg +# 58--Hockey/58_Hockey_icehockey_puck_58_832.jpg +# 58--Hockey/58_Hockey_icehockey_puck_58_315.jpg +# 58--Hockey/58_Hockey_icehockey_puck_58_570.jpg +# 58--Hockey/58_Hockey_icehockey_puck_58_896.jpg +# 58--Hockey/58_Hockey_icehockey_puck_58_742.jpg +# 58--Hockey/58_Hockey_icehockey_puck_58_121.jpg +# 58--Hockey/58_Hockey_icehockey_puck_58_63.jpg +# 58--Hockey/58_Hockey_icehockey_puck_58_257.jpg +# 58--Hockey/58_Hockey_icehockey_puck_58_226.jpg +# 58--Hockey/58_Hockey_icehockey_puck_58_803.jpg +# 58--Hockey/58_Hockey_icehockey_puck_58_11.jpg +# 58--Hockey/58_Hockey_icehockey_puck_58_719.jpg +# 58--Hockey/58_Hockey_icehockey_puck_58_792.jpg +# 58--Hockey/58_Hockey_icehockey_puck_58_1013.jpg +# 58--Hockey/58_Hockey_icehockey_puck_58_23.jpg +# 58--Hockey/58_Hockey_icehockey_puck_58_405.jpg +# 58--Hockey/58_Hockey_icehockey_puck_58_66.jpg +# 58--Hockey/58_Hockey_icehockey_puck_58_582.jpg +# 58--Hockey/58_Hockey_icehockey_puck_58_76.jpg +# 58--Hockey/58_Hockey_icehockey_puck_58_760.jpg +# 58--Hockey/58_Hockey_icehockey_puck_58_54.jpg +# 58--Hockey/58_Hockey_icehockey_puck_58_385.jpg +# 58--Hockey/58_Hockey_icehockey_puck_58_496.jpg +# 58--Hockey/58_Hockey_icehockey_puck_58_458.jpg +# 58--Hockey/58_Hockey_icehockey_puck_58_187.jpg +# 58--Hockey/58_Hockey_icehockey_puck_58_82.jpg +# 58--Hockey/58_Hockey_icehockey_puck_58_1034.jpg +# 58--Hockey/58_Hockey_icehockey_puck_58_439.jpg +# 58--Hockey/58_Hockey_icehockey_puck_58_400.jpg +# 58--Hockey/58_Hockey_icehockey_puck_58_373.jpg +# 58--Hockey/58_Hockey_icehockey_puck_58_154.jpg +# 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1.0 739.071 153.321 1.0 737.357 156.643 1.0 741.75 156.643 1.0 0.24 +711 132 12 10 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 0.2 +752 116 11 14 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 0.26 +745 122 5 6 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 0.2 +767 140 12 14 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 0.29 +789 153 11 11 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 0.2 +787 131 11 11 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 0.21 +771 103 5 6 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 0.2 +787 99 10 11 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 0.21 +791 115 6 6 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 0.19 +768 123 5 9 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 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a/test_3dian_face.jpg b/test_3dian_face.jpg new file mode 100644 index 0000000..77263bb Binary files /dev/null and b/test_3dian_face.jpg differ diff --git a/test_fddb.py b/test_fddb.py new file mode 100644 index 0000000..98fef43 --- /dev/null +++ b/test_fddb.py @@ -0,0 +1,207 @@ +from __future__ import print_function +import os +import argparse +import torch +import torch.backends.cudnn as cudnn +import numpy as np +from data import cfg_mnet, cfg_re50 +from layers.functions.prior_box import PriorBox +from utils.nms.py_cpu_nms import py_cpu_nms +import cv2 +from models.retinaface import RetinaFace +from utils.box_utils import decode, decode_landm +from utils.timer import Timer + +parser = argparse.ArgumentParser(description='Retinaface') + +parser.add_argument('-m', '--trained_model', default='./weights/mobilenet0.25_Final.pth', + type=str, help='Trained state_dict file path to open') +parser.add_argument('--network', default='mobile0.25', help='Backbone network mobile0.25 or resnet50') +parser.add_argument('--save_folder', default='eval/', type=str, help='Dir to save results') +parser.add_argument('--cpu', action="store_true", default=False, help='Use cpu inference') +parser.add_argument('--dataset', default='FDDB', type=str, choices=['FDDB'], help='dataset') +parser.add_argument('--confidence_threshold', default=0.02, type=float, help='confidence_threshold') +parser.add_argument('--top_k', default=5000, type=int, help='top_k') +parser.add_argument('--nms_threshold', default=0.4, type=float, help='nms_threshold') +parser.add_argument('--keep_top_k', default=750, type=int, help='keep_top_k') +parser.add_argument('-s', '--save_image', action="store_true", default=False, help='show detection results') +parser.add_argument('--vis_thres', default=0.5, type=float, help='visualization_threshold') +args = parser.parse_args() + + +def check_keys(model, pretrained_state_dict): + ckpt_keys = set(pretrained_state_dict.keys()) + model_keys = set(model.state_dict().keys()) + used_pretrained_keys = model_keys & ckpt_keys + unused_pretrained_keys = ckpt_keys - model_keys + missing_keys = model_keys - ckpt_keys + print('Missing keys:{}'.format(len(missing_keys))) + print('Unused checkpoint keys:{}'.format(len(unused_pretrained_keys))) + print('Used keys:{}'.format(len(used_pretrained_keys))) + assert len(used_pretrained_keys) > 0, 'load NONE from pretrained checkpoint' + return True + + +def remove_prefix(state_dict, prefix): + ''' Old style model is stored with all names of parameters sharing common prefix 'module.' ''' + print('remove prefix \'{}\''.format(prefix)) + f = lambda x: x.split(prefix, 1)[-1] if x.startswith(prefix) else x + return {f(key): value for key, value in state_dict.items()} + + +def load_model(model, pretrained_path, load_to_cpu): + print('Loading pretrained model from {}'.format(pretrained_path)) + if load_to_cpu: + pretrained_dict = torch.load(pretrained_path, map_location=lambda storage, loc: storage) + else: + device = torch.cuda.current_device() + pretrained_dict = torch.load(pretrained_path, map_location=lambda storage, loc: storage.cuda(device)) + if "state_dict" in pretrained_dict.keys(): + pretrained_dict = remove_prefix(pretrained_dict['state_dict'], 'module.') + else: + pretrained_dict = remove_prefix(pretrained_dict, 'module.') + check_keys(model, pretrained_dict) + model.load_state_dict(pretrained_dict, strict=False) + return model + + +if __name__ == '__main__': + torch.set_grad_enabled(False) + cfg = None + if args.network == "mobile0.25": + cfg = cfg_mnet + elif args.network == "resnet50": + cfg = cfg_re50 + # net and model + net = RetinaFace(cfg=cfg, phase = 'test') + net = load_model(net, args.trained_model, args.cpu) + net.eval() + print('Finished loading model!') + print(net) + cudnn.benchmark = True + device = torch.device("cpu" if args.cpu else "cuda") + net = net.to(device) + + + # save file + if not os.path.exists(args.save_folder): + os.makedirs(args.save_folder) + fw = open(os.path.join(args.save_folder, args.dataset + '_dets.txt'), 'w') + + # testing dataset + testset_folder = os.path.join('data', args.dataset, 'images/') + testset_list = os.path.join('data', args.dataset, 'img_list.txt') + with open(testset_list, 'r') as fr: + test_dataset = fr.read().split() + num_images = len(test_dataset) + + # testing scale + resize = 1 + + _t = {'forward_pass': Timer(), 'misc': Timer()} + + # testing begin + for i, img_name in enumerate(test_dataset): + image_path = testset_folder + img_name + '.jpg' + img_raw = cv2.imread(image_path, cv2.IMREAD_COLOR) + + img = np.float32(img_raw) + if resize != 1: + img = cv2.resize(img, None, None, fx=resize, fy=resize, interpolation=cv2.INTER_LINEAR) + im_height, im_width, _ = img.shape + scale = torch.Tensor([img.shape[1], img.shape[0], img.shape[1], img.shape[0]]) + img -= (104, 117, 123) + img = img.transpose(2, 0, 1) + img = torch.from_numpy(img).unsqueeze(0) + img = img.to(device) + scale = scale.to(device) + + _t['forward_pass'].tic() + loc, conf, landms = net(img) # forward pass + _t['forward_pass'].toc() + _t['misc'].tic() + priorbox = PriorBox(cfg, image_size=(im_height, im_width)) + priors = priorbox.forward() + priors = priors.to(device) + prior_data = priors.data + boxes = decode(loc.data.squeeze(0), prior_data, cfg['variance']) + boxes = boxes * scale / resize + boxes = boxes.cpu().numpy() + scores = conf.squeeze(0).data.cpu().numpy()[:, 1] + landms = decode_landm(landms.data.squeeze(0), prior_data, cfg['variance']) + scale1 = torch.Tensor([img.shape[3], img.shape[2], img.shape[3], img.shape[2], + img.shape[3], img.shape[2], img.shape[3], img.shape[2], + img.shape[3], img.shape[2]]) + scale1 = scale1.to(device) + landms = landms * scale1 / resize + landms = landms.cpu().numpy() + + # ignore low scores + inds = np.where(scores > args.confidence_threshold)[0] + boxes = boxes[inds] + landms = landms[inds] + scores = scores[inds] + + # keep top-K before NMS + # order = scores.argsort()[::-1][:args.top_k] + order = scores.argsort()[::-1] + boxes = boxes[order] + landms = landms[order] + scores = scores[order] + + # do NMS + dets = np.hstack((boxes, scores[:, np.newaxis])).astype(np.float32, copy=False) + keep = py_cpu_nms(dets, args.nms_threshold) + + dets = dets[keep, :] + landms = landms[keep] + + # keep top-K faster NMS + # dets = dets[:args.keep_top_k, :] + # landms = landms[:args.keep_top_k, :] + + dets = np.concatenate((dets, landms), axis=1) + _t['misc'].toc() + + # save dets + if args.dataset == "FDDB": + fw.write('{:s}\n'.format(img_name)) + fw.write('{:.1f}\n'.format(dets.shape[0])) + for k in range(dets.shape[0]): + xmin = dets[k, 0] + ymin = dets[k, 1] + xmax = dets[k, 2] + ymax = dets[k, 3] + score = dets[k, 4] + w = xmax - xmin + 1 + h = ymax - ymin + 1 + # fw.write('{:.3f} {:.3f} {:.3f} {:.3f} {:.10f}\n'.format(xmin, ymin, w, h, score)) + fw.write('{:d} {:d} {:d} {:d} {:.10f}\n'.format(int(xmin), int(ymin), int(w), int(h), score)) + print('im_detect: {:d}/{:d} forward_pass_time: {:.4f}s misc: {:.4f}s'.format(i + 1, num_images, _t['forward_pass'].average_time, _t['misc'].average_time)) + + # show image + if args.save_image: + for b in dets: + if b[4] < args.vis_thres: + continue + text = "{:.4f}".format(b[4]) + b = list(map(int, b)) + cv2.rectangle(img_raw, (b[0], b[1]), (b[2], b[3]), (0, 0, 255), 2) + cx = b[0] + cy = b[1] + 12 + cv2.putText(img_raw, text, (cx, cy), + cv2.FONT_HERSHEY_DUPLEX, 0.5, (255, 255, 255)) + + # landms + cv2.circle(img_raw, (b[5], b[6]), 1, (0, 0, 255), 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torch +import torch.backends.cudnn as cudnn +import numpy as np +from data import cfg_mnet, cfg_re50 +from layers.functions.prior_box import PriorBox +from utils.nms.py_cpu_nms import py_cpu_nms +import cv2 +from models.retinaface import RetinaFace +from utils.box_utils import decode, decode_landm +from utils.timer import Timer + + +parser = argparse.ArgumentParser(description='Retinaface') +parser.add_argument('-m', '--trained_model', default='./weights/Resnet50_Final.pth', + type=str, help='Trained state_dict file path to open') +parser.add_argument('--network', default='resnet50', help='Backbone network mobile0.25 or resnet50') +parser.add_argument('--origin_size', default=True, type=str, help='Whether use origin image size to evaluate') +# parser.add_argument('--save_folder', default='./widerface_evaluate/widerface_txt/', type=str, help='Dir to save txt results') +parser.add_argument('--save_folder', default=r'D:\TH\3_keypoint\0_Pytorch_Retinaface-master\test_result', type=str, help='Dir to save txt results') +parser.add_argument('--cpu', action="store_true", default=False, help='Use cpu inference') +# parser.add_argument('--dataset_folder', default='./data/widerface/val/images/', type=str, help='dataset path') +parser.add_argument('--dataset_folder', default=r'D:\TH\3_keypoint\0_Pytorch_Retinaface-master\test', type=str, help='dataset path') +parser.add_argument('--confidence_threshold', default=0.02, type=float, help='confidence_threshold') +parser.add_argument('--top_k', default=5000, type=int, help='top_k') +parser.add_argument('--nms_threshold', default=0.4, type=float, help='nms_threshold') +parser.add_argument('--keep_top_k', default=750, type=int, help='keep_top_k') +parser.add_argument('-s', '--save_image', action="store_true", default=False, help='show detection results') +parser.add_argument('--vis_thres', default=0.5, type=float, help='visualization_threshold') +args = parser.parse_args() + + +def check_keys(model, pretrained_state_dict): + ckpt_keys = set(pretrained_state_dict.keys()) + model_keys = set(model.state_dict().keys()) + used_pretrained_keys = model_keys & ckpt_keys + unused_pretrained_keys = ckpt_keys - model_keys + missing_keys = model_keys - ckpt_keys + print('Missing keys:{}'.format(len(missing_keys))) + print('Unused checkpoint keys:{}'.format(len(unused_pretrained_keys))) + print('Used keys:{}'.format(len(used_pretrained_keys))) + assert len(used_pretrained_keys) > 0, 'load NONE from pretrained checkpoint' + return True + + +def remove_prefix(state_dict, prefix): + ''' Old style model is stored with all names of parameters sharing common prefix 'module.' ''' + print('remove prefix \'{}\''.format(prefix)) + f = lambda x: x.split(prefix, 1)[-1] if x.startswith(prefix) else x + return {f(key): value for key, value in state_dict.items()} + + +def load_model(model, pretrained_path, load_to_cpu): + print('Loading pretrained model from {}'.format(pretrained_path)) + if load_to_cpu: + pretrained_dict = torch.load(pretrained_path, map_location=lambda storage, loc: storage) + else: + device = torch.cuda.current_device() + pretrained_dict = torch.load(pretrained_path, map_location=lambda storage, loc: storage.cuda(device)) + if "state_dict" in pretrained_dict.keys(): + pretrained_dict = remove_prefix(pretrained_dict['state_dict'], 'module.') + else: + pretrained_dict = remove_prefix(pretrained_dict, 'module.') + check_keys(model, pretrained_dict) + model.load_state_dict(pretrained_dict, strict=False) + return model + + +if __name__ == '__main__': + torch.set_grad_enabled(False) + + cfg = None + if args.network == "mobile0.25": + cfg = cfg_mnet + elif args.network == "resnet50": + cfg = cfg_re50 + # net and model + net = RetinaFace(cfg=cfg, phase = 'test') + net = load_model(net, args.trained_model, args.cpu) + net.eval() + print('Finished loading model!') + print(net) + cudnn.benchmark = True + device = torch.device("cpu" if args.cpu else "cuda") + net = net.to(device) + + # testing dataset + testset_folder = args.dataset_folder + testset_list = args.dataset_folder[:-7] + "wider_val.txt" + + with open(testset_list, 'r') as fr: + test_dataset = fr.read().split() + num_images = len(test_dataset) + + _t = {'forward_pass': Timer(), 'misc': Timer()} + + # testing begin + for i, img_name in enumerate(test_dataset): + image_path = testset_folder + img_name + img_raw = cv2.imread(image_path, cv2.IMREAD_COLOR) + img = np.float32(img_raw) + + # testing scale + target_size = 1600 + max_size = 2150 + im_shape = img.shape + im_size_min = np.min(im_shape[0:2]) + im_size_max = np.max(im_shape[0:2]) + resize = float(target_size) / float(im_size_min) + # prevent bigger axis from being more than max_size: + if np.round(resize * im_size_max) > max_size: + resize = float(max_size) / float(im_size_max) + if args.origin_size: + resize = 1 + + if resize != 1: + img = cv2.resize(img, None, None, fx=resize, fy=resize, interpolation=cv2.INTER_LINEAR) + im_height, im_width, _ = img.shape + scale = torch.Tensor([img.shape[1], img.shape[0], img.shape[1], img.shape[0]]) + img -= (104, 117, 123) + img = img.transpose(2, 0, 1) + img = torch.from_numpy(img).unsqueeze(0) + img = img.to(device) + scale = scale.to(device) + + _t['forward_pass'].tic() + loc, conf, landms = net(img) # forward pass + _t['forward_pass'].toc() + _t['misc'].tic() + priorbox = PriorBox(cfg, image_size=(im_height, im_width)) + priors = priorbox.forward() + priors = priors.to(device) + prior_data = priors.data + boxes = decode(loc.data.squeeze(0), prior_data, cfg['variance']) + boxes = boxes * scale / resize + boxes = boxes.cpu().numpy() + scores = conf.squeeze(0).data.cpu().numpy()[:, 1] + landms = decode_landm(landms.data.squeeze(0), prior_data, cfg['variance']) + scale1 = torch.Tensor([img.shape[3], img.shape[2], img.shape[3], img.shape[2], + img.shape[3], img.shape[2], img.shape[3], img.shape[2], + img.shape[3], img.shape[2]]) + scale1 = scale1.to(device) + landms = landms * scale1 / resize + landms = landms.cpu().numpy() + + # ignore low scores + inds = np.where(scores > args.confidence_threshold)[0] + boxes = boxes[inds] + landms = landms[inds] + scores = scores[inds] + + # keep top-K before NMS + order = scores.argsort()[::-1] + # order = scores.argsort()[::-1][:args.top_k] + boxes = boxes[order] + landms = landms[order] + scores = scores[order] + + # do NMS + dets = np.hstack((boxes, scores[:, np.newaxis])).astype(np.float32, copy=False) + keep = py_cpu_nms(dets, args.nms_threshold) + # keep = nms(dets, args.nms_threshold,force_cpu=args.cpu) + dets = dets[keep, :] + landms = landms[keep] + + # keep top-K faster NMS + # dets = dets[:args.keep_top_k, :] + # landms = landms[:args.keep_top_k, :] + + dets = np.concatenate((dets, landms), axis=1) + _t['misc'].toc() + + # -------------------------------------------------------------------- + save_name = args.save_folder + img_name[:-4] + ".txt" + dirname = os.path.dirname(save_name) + if not os.path.isdir(dirname): + os.makedirs(dirname) + with open(save_name, "w") as fd: + bboxs = dets + file_name = os.path.basename(save_name)[:-4] + "\n" + bboxs_num = str(len(bboxs)) + "\n" + fd.write(file_name) + fd.write(bboxs_num) + for box in bboxs: + x = int(box[0]) + y = int(box[1]) + w = int(box[2]) - int(box[0]) + h = int(box[3]) - int(box[1]) + confidence = str(box[4]) + line = str(x) + " " + str(y) + " " + str(w) + " " + str(h) + " " + confidence + " \n" + fd.write(line) + + print('im_detect: {:d}/{:d} forward_pass_time: {:.4f}s misc: {:.4f}s'.format(i + 1, num_images, _t['forward_pass'].average_time, _t['misc'].average_time)) + + # save image + if args.save_image: + for b in dets: + if b[4] < args.vis_thres: + continue + text = "{:.4f}".format(b[4]) + b = list(map(int, b)) + cv2.rectangle(img_raw, (b[0], b[1]), (b[2], b[3]), (0, 0, 255), 2) + cx = b[0] + cy = b[1] + 12 + cv2.putText(img_raw, text, (cx, cy), + cv2.FONT_HERSHEY_DUPLEX, 0.5, (255, 255, 255)) + + # landms + cv2.circle(img_raw, (b[5], b[6]), 1, (0, 0, 255), 4) + cv2.circle(img_raw, (b[7], b[8]), 1, (0, 255, 255), 4) + cv2.circle(img_raw, (b[9], b[10]), 1, (255, 0, 255), 4) + cv2.circle(img_raw, (b[11], b[12]), 1, (0, 255, 0), 4) + cv2.circle(img_raw, (b[13], b[14]), 1, (255, 0, 0), 4) + # save image + if not os.path.exists("./results/"): + os.makedirs("./results/") + name = "./results/" + str(i) + ".jpg" + cv2.imwrite(name, img_raw) + diff --git a/train.py b/train.py new file mode 100644 index 0000000..32d83ed --- /dev/null +++ b/train.py @@ -0,0 +1,160 @@ +from __future__ import print_function +import os +import torch +import torch.optim as optim +import torch.backends.cudnn as cudnn +import argparse +import torch.utils.data as data +from data import WiderFaceDetection, detection_collate, preproc, cfg_mnet, cfg_re50 +from layers.modules import MultiBoxLoss +from layers.functions.prior_box import PriorBox +import time +import datetime +import math +from models.retinaface import RetinaFace + +parser = argparse.ArgumentParser(description='Retinaface Training') +parser.add_argument('--training_dataset', default='./data/widerface/train/label.txt', help='Training dataset directory') +parser.add_argument('--network', default='mobile0.25', help='Backbone network mobile0.25 or resnet50') +parser.add_argument('--num_workers', default=0, type=int, help='Number of workers used in dataloading') +parser.add_argument('--lr', '--learning-rate', default=1e-3, type=float, help='initial learning rate') +parser.add_argument('--momentum', default=0.9, type=float, help='momentum') +parser.add_argument('--resume_net', default=None, help='resume net for retraining') +parser.add_argument('--resume_epoch', default=0, type=int, help='resume iter for retraining') +parser.add_argument('--weight_decay', default=5e-4, type=float, help='Weight decay for SGD') +parser.add_argument('--gamma', default=0.1, type=float, help='Gamma update for SGD') +parser.add_argument('--save_folder', default='./weights/', help='Location to save checkpoint models') + +args = parser.parse_args() + +if not os.path.exists(args.save_folder): + os.mkdir(args.save_folder) +cfg = None +if args.network == "mobile0.25": + cfg = cfg_mnet +elif args.network == "resnet50": + cfg = cfg_re50 + +rgb_mean = (104, 117, 123) # bgr order +num_classes = 2 +img_dim = cfg['image_size'] +num_gpu = cfg['ngpu'] +batch_size = cfg['batch_size'] +max_epoch = cfg['epoch'] +gpu_train = cfg['gpu_train'] + +num_workers = args.num_workers +momentum = args.momentum +weight_decay = args.weight_decay +initial_lr = args.lr +gamma = args.gamma +training_dataset = args.training_dataset +save_folder = args.save_folder + +net = RetinaFace(cfg=cfg) +print("Printing net...") +print(net) + +if args.resume_net is not None: + print('Loading resume network...') + state_dict = torch.load(args.resume_net) + # create new OrderedDict that does not contain `module.` + from collections import OrderedDict + new_state_dict = OrderedDict() + for k, v in state_dict.items(): + head = k[:7] + if head == 'module.': + name = k[7:] # remove `module.` + else: + name = k + new_state_dict[name] = v + net.load_state_dict(new_state_dict) + +if num_gpu > 1 and gpu_train: + net = torch.nn.DataParallel(net).cuda() +else: + net = net.cuda() + +cudnn.benchmark = True + + +optimizer = optim.SGD(net.parameters(), lr=initial_lr, momentum=momentum, weight_decay=weight_decay) +criterion = MultiBoxLoss(num_classes, 0.35, True, 0, True, 7, 0.35, False) + +priorbox = PriorBox(cfg, image_size=(img_dim, img_dim)) +with torch.no_grad(): + priors = priorbox.forward() + priors = priors.cuda() + +def train(): + net.train() + epoch = 0 + args.resume_epoch + print('Loading Dataset...') + + dataset = WiderFaceDetection( training_dataset,preproc(img_dim, rgb_mean)) + + epoch_size = math.ceil(len(dataset) / batch_size) + max_iter = max_epoch * epoch_size + + stepvalues = (cfg['decay1'] * epoch_size, cfg['decay2'] * epoch_size) + step_index = 0 + + if args.resume_epoch > 0: + start_iter = args.resume_epoch * epoch_size + else: + start_iter = 0 + + for iteration in range(start_iter, max_iter): + if iteration % epoch_size == 0: + # create batch iterator + batch_iterator = iter(data.DataLoader(dataset, batch_size, shuffle=True, num_workers=num_workers, collate_fn=detection_collate)) + if (epoch % 10 == 0 and epoch > 0) or (epoch % 5 == 0 and epoch > cfg['decay1']): + torch.save(net.state_dict(), save_folder + cfg['name']+ '_epoch_' + str(epoch) + '.pth') + epoch += 1 + + load_t0 = time.time() + if iteration in stepvalues: + step_index += 1 + lr = adjust_learning_rate(optimizer, gamma, epoch, step_index, iteration, epoch_size) + + # load train data + images, targets = next(batch_iterator) + images = images.cuda() + targets = [anno.cuda() for anno in targets] + + # forward + out = net(images) + + # backprop + optimizer.zero_grad() + loss_l, loss_c, loss_landm = criterion(out, priors, targets) + loss = cfg['loc_weight'] * loss_l + loss_c + loss_landm + loss.backward() + optimizer.step() + load_t1 = time.time() + batch_time = load_t1 - load_t0 + eta = int(batch_time * (max_iter - iteration)) + print('Epoch:{}/{} || Epochiter: {}/{} || Iter: {}/{} || Loc: {:.4f} Cla: {:.4f} Landm: {:.4f} || LR: {:.8f} || Batchtime: {:.4f} s || ETA: {}' + .format(epoch, max_epoch, (iteration % epoch_size) + 1, + epoch_size, iteration + 1, max_iter, loss_l.item(), loss_c.item(), loss_landm.item(), lr, batch_time, str(datetime.timedelta(seconds=eta)))) + + torch.save(net.state_dict(), save_folder + cfg['name'] + '_Final.pth') + # torch.save(net.state_dict(), save_folder + 'Final_Retinaface.pth') + + +def adjust_learning_rate(optimizer, gamma, epoch, step_index, iteration, epoch_size): + """Sets the learning rate + # Adapted from PyTorch Imagenet example: + # https://github.com/pytorch/examples/blob/master/imagenet/main.py + """ + warmup_epoch = -1 + if epoch <= warmup_epoch: + lr = 1e-6 + (initial_lr-1e-6) * iteration / (epoch_size * warmup_epoch) + else: + lr = initial_lr * (gamma ** (step_index)) + for param_group in optimizer.param_groups: + param_group['lr'] = lr + return lr + +if __name__ == '__main__': + train() diff --git a/utils/__init__.py b/utils/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/utils/__pycache__/__init__.cpython-37.pyc b/utils/__pycache__/__init__.cpython-37.pyc new file mode 100644 index 0000000..70ac4f2 Binary files /dev/null and b/utils/__pycache__/__init__.cpython-37.pyc differ diff --git a/utils/__pycache__/box_utils.cpython-37.pyc b/utils/__pycache__/box_utils.cpython-37.pyc new file mode 100644 index 0000000..1e0df5c Binary files /dev/null and b/utils/__pycache__/box_utils.cpython-37.pyc differ diff --git a/utils/__pycache__/timer.cpython-37.pyc b/utils/__pycache__/timer.cpython-37.pyc new file mode 100644 index 0000000..ba72f55 Binary files /dev/null and b/utils/__pycache__/timer.cpython-37.pyc differ diff --git a/utils/box_utils.py b/utils/box_utils.py new file mode 100644 index 0000000..9254912 --- /dev/null +++ b/utils/box_utils.py @@ -0,0 +1,335 @@ +import torch +import numpy as np + + +def point_form(boxes): + """ Convert prior_boxes to (xmin, ymin, xmax, ymax) + representation for comparison to point form ground truth data. + Args: + boxes: (tensor) center-size default boxes from priorbox layers. + Return: + boxes: (tensor) Converted xmin, ymin, xmax, ymax form of boxes. + """ + return torch.cat((boxes[:, :2] - boxes[:, 2:]/2, # xmin, ymin + boxes[:, :2] + boxes[:, 2:]/2), 1) # xmax, ymax + + +def center_size(boxes): + """ Convert prior_boxes to (cx, cy, w, h) + representation for comparison to center-size form ground truth data. + Args: + boxes: (tensor) point_form boxes + Return: + boxes: (tensor) Converted xmin, ymin, xmax, ymax form of boxes. + """ + return torch.cat((boxes[:, 2:] + boxes[:, :2])/2, # cx, cy + boxes[:, 2:] - boxes[:, :2], 1) # w, h + + +def intersect(box_a, box_b): + """ We resize both tensors to [A,B,2] without new malloc: + [A,2] -> [A,1,2] -> [A,B,2] + [B,2] -> [1,B,2] -> [A,B,2] + Then we compute the area of intersect between box_a and box_b. + Args: + box_a: (tensor) bounding boxes, Shape: [A,4]. + box_b: (tensor) bounding boxes, Shape: [B,4]. + Return: + (tensor) intersection area, Shape: [A,B]. + """ + A = box_a.size(0) + B = box_b.size(0) + max_xy = torch.min(box_a[:, 2:].unsqueeze(1).expand(A, B, 2), + box_b[:, 2:].unsqueeze(0).expand(A, B, 2)) + min_xy = torch.max(box_a[:, :2].unsqueeze(1).expand(A, B, 2), + box_b[:, :2].unsqueeze(0).expand(A, B, 2)) + inter = torch.clamp((max_xy - min_xy), min=0) + return inter[:, :, 0] * inter[:, :, 1] + + +def jaccard(box_a, box_b): + """Compute the jaccard overlap of two sets of boxes. The jaccard overlap + is simply the intersection over union of two boxes. Here we operate on + ground truth boxes and default boxes. + E.g.: + A ∩ B / A ∪ B = A ∩ B / (area(A) + area(B) - A ∩ B) + Args: + box_a: (tensor) Ground truth bounding boxes, Shape: [num_objects,4] + box_b: (tensor) Prior boxes from priorbox layers, Shape: [num_priors,4] + Return: + jaccard overlap: (tensor) Shape: [box_a.size(0), box_b.size(0)] + """ + inter = intersect(box_a, box_b) + area_a = ((box_a[:, 2]-box_a[:, 0]) * + (box_a[:, 3]-box_a[:, 1])).unsqueeze(1).expand_as(inter) # [A,B] + area_b = ((box_b[:, 2]-box_b[:, 0]) * + (box_b[:, 3]-box_b[:, 1])).unsqueeze(0).expand_as(inter) # [A,B] + union = area_a + area_b - inter + return inter / union # [A,B] + + +def matrix_iou(a, b): + """ + return iou of a and b, numpy version for data augenmentation + """ + lt = np.maximum(a[:, np.newaxis, :2], b[:, :2]) + rb = np.minimum(a[:, np.newaxis, 2:], b[:, 2:]) + + area_i = np.prod(rb - lt, axis=2) * (lt < rb).all(axis=2) + area_a = np.prod(a[:, 2:] - a[:, :2], axis=1) + area_b = np.prod(b[:, 2:] - b[:, :2], axis=1) + return area_i / (area_a[:, np.newaxis] + area_b - area_i) + + +def matrix_iof(a, b): + """ + return iof of a and b, numpy version for data augenmentation + """ + lt = np.maximum(a[:, np.newaxis, :2], b[:, :2]) + rb = np.minimum(a[:, np.newaxis, 2:], b[:, 2:]) + + area_i = np.prod(rb - lt, axis=2) * (lt < rb).all(axis=2) + area_a = np.prod(a[:, 2:] - a[:, :2], axis=1) + return area_i / np.maximum(area_a[:, np.newaxis], 1) + + +def match(threshold, truths, priors, variances, labels, landms, loc_t, conf_t, landm_t, idx): + """Match each prior box with the ground truth box of the highest jaccard + overlap, encode the bounding boxes, then return the matched indices + corresponding to both confidence and location preds. + Args: + threshold: (float) The overlap threshold used when mathing boxes. + truths: (tensor) Ground truth boxes, Shape: [num_obj, 4]. + priors: (tensor) Prior boxes from priorbox layers, Shape: [n_priors,4]. + variances: (tensor) Variances corresponding to each prior coord, + Shape: [num_priors, 4]. + labels: (tensor) All the class labels for the image, Shape: [num_obj]. + landms: (tensor) Ground truth landms, Shape [num_obj, 10]. + loc_t: (tensor) Tensor to be filled w/ endcoded location targets. + conf_t: (tensor) Tensor to be filled w/ matched indices for conf preds. + landm_t: (tensor) Tensor to be filled w/ endcoded landm targets. + idx: (int) current batch index + Return: + The matched indices corresponding to 1)location 2)confidence 3)landm preds. + """ + # jaccard index + overlaps = jaccard( + truths, + point_form(priors) + ) + # (Bipartite Matching) + # [1,num_objects] best prior for each ground truth + best_prior_overlap, best_prior_idx = overlaps.max(1, keepdim=True) + + # ignore hard gt + valid_gt_idx = best_prior_overlap[:, 0] >= 0.2 + best_prior_idx_filter = best_prior_idx[valid_gt_idx, :] + if best_prior_idx_filter.shape[0] <= 0: + loc_t[idx] = 0 + conf_t[idx] = 0 + return + + # [1,num_priors] best ground truth for each prior + best_truth_overlap, best_truth_idx = overlaps.max(0, keepdim=True) + best_truth_idx.squeeze_(0) + best_truth_overlap.squeeze_(0) + best_prior_idx.squeeze_(1) + best_prior_idx_filter.squeeze_(1) + best_prior_overlap.squeeze_(1) + best_truth_overlap.index_fill_(0, best_prior_idx_filter, 2) # ensure best prior + # TODO refactor: index best_prior_idx with long tensor + # ensure every gt matches with its prior of max overlap + for j in range(best_prior_idx.size(0)): # 判别此anchor是预测哪一个boxes + best_truth_idx[best_prior_idx[j]] = j + matches = truths[best_truth_idx] # Shape: [num_priors,4] 此处为每一个anchor对应的bbox取出来 + conf = labels[best_truth_idx] # Shape: [num_priors] 此处为每一个anchor对应的label取出来 + conf[best_truth_overlap < threshold] = 0 # label as background overlap<0.35的全部作为负样本 + loc = encode(matches, priors, variances) + + matches_landm = landms[best_truth_idx] + landm = encode_landm(matches_landm, priors, variances) + loc_t[idx] = loc # [num_priors,4] encoded offsets to learn + conf_t[idx] = conf # [num_priors] top class label for each prior + landm_t[idx] = landm + + +def encode(matched, priors, variances): + """Encode the variances from the priorbox layers into the ground truth boxes + we have matched (based on jaccard overlap) with the prior boxes. + Args: + matched: (tensor) Coords of ground truth for each prior in point-form + Shape: [num_priors, 4]. + priors: (tensor) Prior boxes in center-offset form + Shape: [num_priors,4]. + variances: (list[float]) Variances of priorboxes + Return: + encoded boxes (tensor), Shape: [num_priors, 4] + """ + + # dist b/t match center and prior's center + g_cxcy = (matched[:, :2] + matched[:, 2:])/2 - priors[:, :2] + # encode variance + g_cxcy /= (variances[0] * priors[:, 2:]) + # match wh / prior wh + g_wh = (matched[:, 2:] - matched[:, :2]) / priors[:, 2:] + g_wh = torch.log(g_wh) / variances[1] + # return target for smooth_l1_loss + return torch.cat([g_cxcy, g_wh], 1) # [num_priors,4] + +def encode_landm(matched, priors, variances): + """Encode the variances from the priorbox layers into the ground truth boxes + we have matched (based on jaccard overlap) with the prior boxes. + Args: + matched: (tensor) Coords of ground truth for each prior in point-form + Shape: [num_priors, 10]. + priors: (tensor) Prior boxes in center-offset form + Shape: [num_priors,4]. + variances: (list[float]) Variances of priorboxes + Return: + encoded landm (tensor), Shape: [num_priors, 10] + """ + + # dist b/t match center and prior's center + # matched = torch.reshape(matched, (matched.size(0), 5, 2)) + matched = torch.reshape(matched, (matched.size(0), 3, 2)) + # priors_cx = priors[:, 0].unsqueeze(1).expand(matched.size(0), 5).unsqueeze(2) + # priors_cy = priors[:, 1].unsqueeze(1).expand(matched.size(0), 5).unsqueeze(2) + # priors_w = priors[:, 2].unsqueeze(1).expand(matched.size(0), 5).unsqueeze(2) + # priors_h = priors[:, 3].unsqueeze(1).expand(matched.size(0), 5).unsqueeze(2) + priors_cx = priors[:, 0].unsqueeze(1).expand(matched.size(0), 3).unsqueeze(2) + priors_cy = priors[:, 1].unsqueeze(1).expand(matched.size(0), 3).unsqueeze(2) + priors_w = priors[:, 2].unsqueeze(1).expand(matched.size(0), 3).unsqueeze(2) + priors_h = priors[:, 3].unsqueeze(1).expand(matched.size(0), 3).unsqueeze(2) + priors = torch.cat([priors_cx, priors_cy, priors_w, priors_h], dim=2) + g_cxcy = matched[:, :, :2] - priors[:, :, :2] + # encode variance + g_cxcy /= (variances[0] * priors[:, :, 2:]) + # g_cxcy /= priors[:, :, 2:] + g_cxcy = g_cxcy.reshape(g_cxcy.size(0), -1) + # return target for smooth_l1_loss + return g_cxcy + + +# Adapted from https://github.com/Hakuyume/chainer-ssd +def decode(loc, priors, variances): + """Decode locations from predictions using priors to undo + the encoding we did for offset regression at train time. + Args: + loc (tensor): location predictions for loc layers, + Shape: [num_priors,4] + priors (tensor): Prior boxes in center-offset form. + Shape: [num_priors,4]. + variances: (list[float]) Variances of priorboxes + Return: + decoded bounding box predictions + """ + + boxes = torch.cat(( + priors[:, :2] + loc[:, :2] * variances[0] * priors[:, 2:], + priors[:, 2:] * torch.exp(loc[:, 2:] * variances[1])), 1) + boxes[:, :2] -= boxes[:, 2:] / 2 + boxes[:, 2:] += boxes[:, :2] + return boxes + +def decode_landm(pre, priors, variances): + """Decode landm from predictions using priors to undo + the encoding we did for offset regression at train time. + Args: + pre (tensor): landm predictions for loc layers, + Shape: [num_priors,10] + priors (tensor): Prior boxes in center-offset form. + Shape: [num_priors,4]. + variances: (list[float]) Variances of priorboxes + Return: + decoded landm predictions + """ + landms = torch.cat((priors[:, :2] + pre[:, :2] * variances[0] * priors[:, 2:], + priors[:, :2] + pre[:, 2:4] * variances[0] * priors[:, 2:], + priors[:, :2] + pre[:, 4:6] * variances[0] * priors[:, 2:], + # priors[:, :2] + pre[:, 6:8] * variances[0] * priors[:, 2:], + # priors[:, :2] + pre[:, 8:10] * variances[0] * priors[:, 2:], + ), dim=1) + return landms + + +def log_sum_exp(x): + """Utility function for computing log_sum_exp while determining + This will be used to determine unaveraged confidence loss across + all examples in a batch. + Args: + x (Variable(tensor)): conf_preds from conf layers + """ + x_max = x.data.max() + return torch.log(torch.sum(torch.exp(x-x_max), 1, keepdim=True)) + x_max + + +# Original author: Francisco Massa: +# https://github.com/fmassa/object-detection.torch +# Ported to PyTorch by Max deGroot (02/01/2017) +def nms(boxes, scores, overlap=0.5, top_k=200): + """Apply non-maximum suppression at test time to avoid detecting too many + overlapping bounding boxes for a given object. + Args: + boxes: (tensor) The location preds for the img, Shape: [num_priors,4]. + scores: (tensor) The class predscores for the img, Shape:[num_priors]. + overlap: (float) The overlap thresh for suppressing unnecessary boxes. + top_k: (int) The Maximum number of box preds to consider. + Return: + The indices of the kept boxes with respect to num_priors. + """ + + keep = torch.Tensor(scores.size(0)).fill_(0).long() + if boxes.numel() == 0: + return keep + x1 = boxes[:, 0] + y1 = boxes[:, 1] + x2 = boxes[:, 2] + y2 = boxes[:, 3] + area = torch.mul(x2 - x1, y2 - y1) + v, idx = scores.sort(0) # sort in ascending order + # I = I[v >= 0.01] + idx = idx[-top_k:] # indices of the top-k largest vals + xx1 = boxes.new() + yy1 = boxes.new() + xx2 = boxes.new() + yy2 = boxes.new() + w = boxes.new() + h = boxes.new() + + # keep = torch.Tensor() + count = 0 + while idx.numel() > 0: + i = idx[-1] # index of current largest val + # keep.append(i) + keep[count] = i + count += 1 + if idx.size(0) == 1: + break + idx = idx[:-1] # remove kept element from view + # load bboxes of next highest vals + torch.index_select(x1, 0, idx, out=xx1) + torch.index_select(y1, 0, idx, out=yy1) + torch.index_select(x2, 0, idx, out=xx2) + torch.index_select(y2, 0, idx, out=yy2) + # store element-wise max with next highest score + xx1 = torch.clamp(xx1, min=x1[i]) + yy1 = torch.clamp(yy1, min=y1[i]) + xx2 = torch.clamp(xx2, max=x2[i]) + yy2 = torch.clamp(yy2, max=y2[i]) + w.resize_as_(xx2) + h.resize_as_(yy2) + w = xx2 - xx1 + h = yy2 - yy1 + # check sizes of xx1 and xx2.. after each iteration + w = torch.clamp(w, min=0.0) + h = torch.clamp(h, min=0.0) + inter = w*h + # IoU = i / (area(a) + area(b) - i) + rem_areas = torch.index_select(area, 0, idx) # load remaining areas) + union = (rem_areas - inter) + area[i] + IoU = inter/union # store result in iou + # keep only elements with an IoU <= overlap + idx = idx[IoU.le(overlap)] + return keep, count + + diff --git a/utils/nms/__init__.py b/utils/nms/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/utils/nms/__pycache__/__init__.cpython-37.pyc b/utils/nms/__pycache__/__init__.cpython-37.pyc new file mode 100644 index 0000000..c414233 Binary files /dev/null and b/utils/nms/__pycache__/__init__.cpython-37.pyc differ diff --git a/utils/nms/__pycache__/py_cpu_nms.cpython-37.pyc b/utils/nms/__pycache__/py_cpu_nms.cpython-37.pyc new file mode 100644 index 0000000..404b1a9 Binary files /dev/null and b/utils/nms/__pycache__/py_cpu_nms.cpython-37.pyc differ diff --git a/utils/nms/py_cpu_nms.py b/utils/nms/py_cpu_nms.py new file mode 100644 index 0000000..54e7b25 --- /dev/null +++ b/utils/nms/py_cpu_nms.py @@ -0,0 +1,38 @@ +# -------------------------------------------------------- +# Fast R-CNN +# Copyright (c) 2015 Microsoft +# Licensed under The MIT License [see LICENSE for details] +# Written by Ross Girshick +# -------------------------------------------------------- + +import numpy as np + +def py_cpu_nms(dets, thresh): + """Pure Python NMS baseline.""" + x1 = dets[:, 0] + y1 = dets[:, 1] + x2 = dets[:, 2] + y2 = dets[:, 3] + scores = dets[:, 4] + + areas = (x2 - x1 + 1) * (y2 - y1 + 1) + order = scores.argsort()[::-1] + + keep = [] + while order.size > 0: + i = order[0] + keep.append(i) + xx1 = np.maximum(x1[i], x1[order[1:]]) + yy1 = np.maximum(y1[i], y1[order[1:]]) + xx2 = np.minimum(x2[i], x2[order[1:]]) + yy2 = np.minimum(y2[i], y2[order[1:]]) + + w = np.maximum(0.0, xx2 - xx1 + 1) + h = np.maximum(0.0, yy2 - yy1 + 1) + inter = w * h + ovr = inter / (areas[i] + areas[order[1:]] - inter) + + inds = np.where(ovr <= thresh)[0] + order = order[inds + 1] + + return keep diff --git a/utils/timer.py b/utils/timer.py new file mode 100644 index 0000000..e4b3b80 --- /dev/null +++ b/utils/timer.py @@ -0,0 +1,40 @@ +# -------------------------------------------------------- +# Fast R-CNN +# Copyright (c) 2015 Microsoft +# Licensed under The MIT License [see LICENSE for details] +# Written by Ross Girshick +# -------------------------------------------------------- + +import time + + +class Timer(object): + """A simple timer.""" + def __init__(self): + self.total_time = 0. + self.calls = 0 + self.start_time = 0. + self.diff = 0. + self.average_time = 0. + + def tic(self): + # using time.time instead of time.clock because time time.clock + # does not normalize for multithreading + self.start_time = time.time() + + def toc(self, average=True): + self.diff = time.time() - self.start_time + self.total_time += self.diff + self.calls += 1 + self.average_time = self.total_time / self.calls + if average: + return self.average_time + else: + return self.diff + + def clear(self): + self.total_time = 0. + self.calls = 0 + self.start_time = 0. + self.diff = 0. + self.average_time = 0. diff --git a/weights/mobilenet0.25_Final.pth b/weights/mobilenet0.25_Final.pth new file mode 100644 index 0000000..2d887c6 Binary files /dev/null and b/weights/mobilenet0.25_Final.pth differ diff --git a/weights/mobilenet0.25_code.pth b/weights/mobilenet0.25_code.pth new file mode 100644 index 0000000..0b62eb9 Binary files /dev/null and b/weights/mobilenet0.25_code.pth differ diff --git a/weights/mobilenet0.25_epoch_10.pth b/weights/mobilenet0.25_epoch_10.pth new file mode 100644 index 0000000..932306d Binary files /dev/null and b/weights/mobilenet0.25_epoch_10.pth differ diff --git a/weights/mobilenet0.25_epoch_245.pth b/weights/mobilenet0.25_epoch_245.pth new file mode 100644 index 0000000..0b62eb9 Binary files /dev/null and b/weights/mobilenet0.25_epoch_245.pth differ diff --git a/weights/mobilenet0.25_epoch_245_3dian.pth b/weights/mobilenet0.25_epoch_245_3dian.pth new file mode 100644 index 0000000..00c6d99 Binary files /dev/null and b/weights/mobilenet0.25_epoch_245_3dian.pth differ diff --git a/weights/mobilenetV1X0.25_pretrain.tar b/weights/mobilenetV1X0.25_pretrain.tar new file mode 100644 index 0000000..cb53f82 Binary files /dev/null and b/weights/mobilenetV1X0.25_pretrain.tar differ diff --git a/widerface_evaluate/README.md b/widerface_evaluate/README.md new file mode 100644 index 0000000..95952b7 --- /dev/null +++ b/widerface_evaluate/README.md @@ -0,0 +1,27 @@ +# WiderFace-Evaluation +Python Evaluation Code for [Wider Face Dataset](http://mmlab.ie.cuhk.edu.hk/projects/WIDERFace/) + + +## Usage + + +##### before evaluating .... + +```` +python3 setup.py build_ext --inplace +```` + +##### evaluating + +**GroungTruth:** `wider_face_val.mat`, `wider_easy_val.mat`, `wider_medium_val.mat`,`wider_hard_val.mat` + +```` +python3 evaluation.py -p -g +```` + +## Bugs & Problems +please issue + +## Acknowledgements + +some code borrowed from Sergey Karayev diff --git a/widerface_evaluate/box_overlaps.pyx b/widerface_evaluate/box_overlaps.pyx new file mode 100644 index 0000000..ad326ba --- /dev/null +++ b/widerface_evaluate/box_overlaps.pyx @@ -0,0 +1,55 @@ +# -------------------------------------------------------- +# Fast R-CNN +# Copyright (c) 2015 Microsoft +# Licensed under The MIT License [see LICENSE for details] +# Written by Sergey Karayev +# -------------------------------------------------------- + +cimport cython +import numpy as np +cimport numpy as np + +DTYPE = np.float +ctypedef np.float_t DTYPE_t + +def bbox_overlaps( + np.ndarray[DTYPE_t, ndim=2] boxes, + np.ndarray[DTYPE_t, ndim=2] query_boxes): + """ + Parameters + ---------- + boxes: (N, 4) ndarray of float + query_boxes: (K, 4) ndarray of float + Returns + ------- + overlaps: (N, K) ndarray of overlap between boxes and query_boxes + """ + cdef unsigned int N = boxes.shape[0] + cdef unsigned int K = query_boxes.shape[0] + cdef np.ndarray[DTYPE_t, ndim=2] overlaps = np.zeros((N, K), dtype=DTYPE) + cdef DTYPE_t iw, ih, box_area + cdef DTYPE_t ua + cdef unsigned int k, n + for k in range(K): + box_area = ( + (query_boxes[k, 2] - query_boxes[k, 0] + 1) * + (query_boxes[k, 3] - query_boxes[k, 1] + 1) + ) + for n in range(N): + iw = ( + min(boxes[n, 2], query_boxes[k, 2]) - + max(boxes[n, 0], query_boxes[k, 0]) + 1 + ) + if iw > 0: + ih = ( + min(boxes[n, 3], query_boxes[k, 3]) - + max(boxes[n, 1], query_boxes[k, 1]) + 1 + ) + if ih > 0: + ua = float( + (boxes[n, 2] - boxes[n, 0] + 1) * + (boxes[n, 3] - boxes[n, 1] + 1) + + box_area - iw * ih + ) + overlaps[n, k] = iw * ih / ua + return overlaps \ No newline at end of file diff --git a/widerface_evaluate/evaluation.py b/widerface_evaluate/evaluation.py new file mode 100644 index 0000000..a9b1744 --- /dev/null +++ b/widerface_evaluate/evaluation.py @@ -0,0 +1,303 @@ +""" +WiderFace evaluation code +author: wondervictor +mail: tianhengcheng@gmail.com +copyright@wondervictor +""" + +import os +import tqdm +import pickle +import argparse +import numpy as np +from scipy.io import loadmat +from bbox import bbox_overlaps +from IPython import embed + + +def get_gt_boxes(gt_dir): + """ gt dir: (wider_face_val.mat, wider_easy_val.mat, wider_medium_val.mat, wider_hard_val.mat)""" + + gt_mat = loadmat(os.path.join(gt_dir, 'wider_face_val.mat')) + hard_mat = loadmat(os.path.join(gt_dir, 'wider_hard_val.mat')) + medium_mat = loadmat(os.path.join(gt_dir, 'wider_medium_val.mat')) + easy_mat = loadmat(os.path.join(gt_dir, 'wider_easy_val.mat')) + + facebox_list = gt_mat['face_bbx_list'] + event_list = gt_mat['event_list'] + file_list = gt_mat['file_list'] + + hard_gt_list = hard_mat['gt_list'] + medium_gt_list = medium_mat['gt_list'] + easy_gt_list = easy_mat['gt_list'] + + return facebox_list, event_list, file_list, hard_gt_list, medium_gt_list, easy_gt_list + + +def get_gt_boxes_from_txt(gt_path, cache_dir): + + cache_file = os.path.join(cache_dir, 'gt_cache.pkl') + if os.path.exists(cache_file): + f = open(cache_file, 'rb') + boxes = pickle.load(f) + f.close() + return boxes + + f = open(gt_path, 'r') + state = 0 + lines = f.readlines() + lines = list(map(lambda x: x.rstrip('\r\n'), lines)) + boxes = {} + print(len(lines)) + f.close() + current_boxes = [] + current_name = None + for line in lines: + if state == 0 and '--' in line: + state = 1 + current_name = line + continue + if state == 1: + state = 2 + continue + + if state == 2 and '--' in line: + state = 1 + boxes[current_name] = np.array(current_boxes).astype('float32') + current_name = line + current_boxes = [] + continue + + if state == 2: + box = [float(x) for x in line.split(' ')[:4]] + current_boxes.append(box) + continue + + f = open(cache_file, 'wb') + pickle.dump(boxes, f) + f.close() + return boxes + + +def read_pred_file(filepath): + + with open(filepath, 'r') as f: + lines = f.readlines() + img_file = lines[0].rstrip('\n\r') + lines = lines[2:] + + # b = lines[0].rstrip('\r\n').split(' ')[:-1] + # c = float(b) + # a = map(lambda x: [[float(a[0]), float(a[1]), float(a[2]), float(a[3]), float(a[4])] for a in x.rstrip('\r\n').split(' ')], lines) + boxes = [] + for line in lines: + line = line.rstrip('\r\n').split(' ') + if line[0] is '': + continue + # a = float(line[4]) + boxes.append([float(line[0]), float(line[1]), float(line[2]), float(line[3]), float(line[4])]) + boxes = np.array(boxes) + # boxes = np.array(list(map(lambda x: [float(a) for a in x.rstrip('\r\n').split(' ')], lines))).astype('float') + return img_file.split('/')[-1], boxes + + +def get_preds(pred_dir): + events = os.listdir(pred_dir) + boxes = dict() + pbar = tqdm.tqdm(events) + + for event in pbar: + pbar.set_description('Reading Predictions ') + event_dir = os.path.join(pred_dir, event) + event_images = os.listdir(event_dir) + current_event = dict() + for imgtxt in event_images: + imgname, _boxes = read_pred_file(os.path.join(event_dir, imgtxt)) + current_event[imgname.rstrip('.jpg')] = _boxes + boxes[event] = current_event + return boxes + + +def norm_score(pred): + """ norm score + pred {key: [[x1,y1,x2,y2,s]]} + """ + + max_score = 0 + min_score = 1 + + for _, k in pred.items(): + for _, v in k.items(): + if len(v) == 0: + continue + _min = np.min(v[:, -1]) + _max = np.max(v[:, -1]) + max_score = max(_max, max_score) + min_score = min(_min, min_score) + + diff = max_score - min_score + for _, k in pred.items(): + for _, v in k.items(): + if len(v) == 0: + continue + v[:, -1] = (v[:, -1] - min_score)/diff + + +def image_eval(pred, gt, ignore, iou_thresh): + """ single image evaluation + pred: Nx5 + gt: Nx4 + ignore: + """ + + _pred = pred.copy() + _gt = gt.copy() + pred_recall = np.zeros(_pred.shape[0]) + recall_list = np.zeros(_gt.shape[0]) + proposal_list = np.ones(_pred.shape[0]) + + _pred[:, 2] = _pred[:, 2] + _pred[:, 0] + _pred[:, 3] = _pred[:, 3] + _pred[:, 1] + _gt[:, 2] = _gt[:, 2] + _gt[:, 0] + _gt[:, 3] = _gt[:, 3] + _gt[:, 1] + + overlaps = bbox_overlaps(_pred[:, :4], _gt) + + for h in range(_pred.shape[0]): + + gt_overlap = overlaps[h] + max_overlap, max_idx = gt_overlap.max(), gt_overlap.argmax() + if max_overlap >= iou_thresh: + if ignore[max_idx] == 0: + recall_list[max_idx] = -1 + proposal_list[h] = -1 + elif recall_list[max_idx] == 0: + recall_list[max_idx] = 1 + + r_keep_index = np.where(recall_list == 1)[0] + pred_recall[h] = len(r_keep_index) + return pred_recall, proposal_list + + +def img_pr_info(thresh_num, pred_info, proposal_list, pred_recall): + pr_info = np.zeros((thresh_num, 2)).astype('float') + for t in range(thresh_num): + + thresh = 1 - (t+1)/thresh_num + r_index = np.where(pred_info[:, 4] >= thresh)[0] + if len(r_index) == 0: + pr_info[t, 0] = 0 + pr_info[t, 1] = 0 + else: + r_index = r_index[-1] + p_index = np.where(proposal_list[:r_index+1] == 1)[0] + pr_info[t, 0] = len(p_index) + pr_info[t, 1] = pred_recall[r_index] + return pr_info + + +def dataset_pr_info(thresh_num, pr_curve, count_face): + _pr_curve = np.zeros((thresh_num, 2)) + for i in range(thresh_num): + _pr_curve[i, 0] = pr_curve[i, 1] / pr_curve[i, 0] + _pr_curve[i, 1] = pr_curve[i, 1] / count_face + return _pr_curve + + +def voc_ap(rec, prec): + + # correct AP calculation + # first append sentinel values at the end + mrec = np.concatenate(([0.], rec, [1.])) + mpre = np.concatenate(([0.], prec, [0.])) + + # compute the precision envelope + for i in range(mpre.size - 1, 0, -1): + mpre[i - 1] = np.maximum(mpre[i - 1], mpre[i]) + + # to calculate area under PR curve, look for points + # where X axis (recall) changes value + i = np.where(mrec[1:] != mrec[:-1])[0] + + # and sum (\Delta recall) * prec + ap = np.sum((mrec[i + 1] - mrec[i]) * mpre[i + 1]) + return ap + + +def evaluation(pred, gt_path, iou_thresh=0.5): + pred = get_preds(pred) + norm_score(pred) + facebox_list, event_list, file_list, hard_gt_list, medium_gt_list, easy_gt_list = get_gt_boxes(gt_path) + event_num = len(event_list) + thresh_num = 1000 + settings = ['easy', 'medium', 'hard'] + setting_gts = [easy_gt_list, medium_gt_list, hard_gt_list] + aps = [] + for setting_id in range(3): + # different setting + gt_list = setting_gts[setting_id] + count_face = 0 + pr_curve = np.zeros((thresh_num, 2)).astype('float') + # [hard, medium, easy] + pbar = tqdm.tqdm(range(event_num)) + for i in pbar: + pbar.set_description('Processing {}'.format(settings[setting_id])) + event_name = str(event_list[i][0][0]) + img_list = file_list[i][0] + pred_list = pred[event_name] + sub_gt_list = gt_list[i][0] + # img_pr_info_list = np.zeros((len(img_list), thresh_num, 2)) + gt_bbx_list = facebox_list[i][0] + + for j in range(len(img_list)): + pred_info = pred_list[str(img_list[j][0][0])] + + gt_boxes = gt_bbx_list[j][0].astype('float') + keep_index = sub_gt_list[j][0] + count_face += len(keep_index) + + if len(gt_boxes) == 0 or len(pred_info) == 0: + continue + ignore = np.zeros(gt_boxes.shape[0]) + if len(keep_index) != 0: + ignore[keep_index-1] = 1 + pred_recall, proposal_list = image_eval(pred_info, gt_boxes, ignore, iou_thresh) + + _img_pr_info = img_pr_info(thresh_num, pred_info, proposal_list, pred_recall) + + pr_curve += _img_pr_info + pr_curve = dataset_pr_info(thresh_num, pr_curve, count_face) + + propose = pr_curve[:, 0] + recall = pr_curve[:, 1] + + ap = voc_ap(recall, propose) + aps.append(ap) + + print("==================== Results ====================") + print("Easy Val AP: {}".format(aps[0])) + print("Medium Val AP: {}".format(aps[1])) + print("Hard Val AP: {}".format(aps[2])) + print("=================================================") + + +if __name__ == '__main__': + + parser = argparse.ArgumentParser() + parser.add_argument('-p', '--pred', default="./widerface_txt/") + parser.add_argument('-g', '--gt', default='./ground_truth/') + + args = parser.parse_args() + evaluation(args.pred, args.gt) + + + + + + + + + + + + diff --git a/widerface_evaluate/ground_truth/wider_easy_val.mat b/widerface_evaluate/ground_truth/wider_easy_val.mat new file mode 100644 index 0000000..5b78df7 Binary files /dev/null and b/widerface_evaluate/ground_truth/wider_easy_val.mat differ diff --git a/widerface_evaluate/ground_truth/wider_face_val.mat b/widerface_evaluate/ground_truth/wider_face_val.mat new file mode 100644 index 0000000..7ac47e5 Binary files /dev/null and b/widerface_evaluate/ground_truth/wider_face_val.mat differ diff --git a/widerface_evaluate/ground_truth/wider_hard_val.mat b/widerface_evaluate/ground_truth/wider_hard_val.mat new file mode 100644 index 0000000..061fcab Binary files /dev/null and b/widerface_evaluate/ground_truth/wider_hard_val.mat differ diff --git a/widerface_evaluate/setup.py b/widerface_evaluate/setup.py new file mode 100644 index 0000000..74dba05 --- /dev/null +++ b/widerface_evaluate/setup.py @@ -0,0 +1,13 @@ +""" +WiderFace evaluation code +author: wondervictor +mail: tianhengcheng@gmail.com +copyright@wondervictor +""" + +from distutils.core import setup, Extension +from Cython.Build import cythonize +import numpy + +package = Extension('bbox', ['box_overlaps.pyx'], include_dirs=[numpy.get_include()]) +setup(ext_modules=cythonize([package]))