130 lines
4.6 KiB
Python
130 lines
4.6 KiB
Python
import os
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import time
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import cv2
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import torch
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from DMPRUtils.DMPR_process import DMPR_process, plot_points
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from DMPRUtils.model.detector import DirectionalPointDetector
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from DMPRUtils.yolo_net import Model
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from DMPR_YOLO.jointUtil import dmpr_yolo
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from STDCUtils.STDC_process import STDC_process
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from STDCUtils.models.model_stages import BiSeNet
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from STDC_YOLO.yolo_stdc_joint import stdc_yolo
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from conf import config
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from models.experimental import attempt_load
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from models.yolo_process import yolo_process
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from utils.plots import plot_one_box
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from utils.torch_utils import select_device
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def main():
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##预先设置的参数
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device_ = '1' ##选定模型，可选 cpu,'0','1'
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##以下参数目前不可改
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Detweights = 'weights/urbanManagement/yolo/best1023.pt'
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seg_nclass = 2
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DMPRweights = "weights/urbanManagement/DMPR/dp_detector_299_1023.pth"
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conf_thres, iou_thres, classes = 0.25, 0.45, 3
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labelnames = "weights/yolov5/class5/labelnames.json"
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rainbows = [[0, 0, 255], [0, 255, 0], [255, 0, 0], [255, 0, 255], [255, 255, 0], [255, 129, 0], [255, 0, 127],
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[127, 255, 0], [0, 255, 127], [0, 127, 255], [127, 0, 255], [255, 127, 255], [255, 255, 127],
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[127, 255, 255], [0, 255, 255], [255, 127, 255], [127, 255, 255], [0, 127, 0], [0, 0, 127],
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[0, 255, 255]]
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allowedList = [0, 1, 2, 3]
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##加载模型，准备好显示字符
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device = select_device(device_)
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half = device.type != 'cpu' # half precision only supported on CUDA
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# yolov5 model
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model = attempt_load(Detweights, map_location=device)
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if half:
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model.half()
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# load args
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args = config.get_parser_for_inference().parse_args()
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# STDC model
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STDC_model = BiSeNet(backbone=args.backbone, n_classes=args.n_classes,
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use_boundary_2=args.use_boundary_2, use_boundary_4=args.use_boundary_4,
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use_boundary_8=args.use_boundary_8, use_boundary_16=args.use_boundary_16,
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use_conv_last=args.use_conv_last).to(device)
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STDC_model.load_state_dict(torch.load(args.respth))
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STDC_model.eval()
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# DMPR model
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# DMPRmodel = DirectionalPointDetector(3, args.depth_factor, config.NUM_FEATURE_MAP_CHANNEL).to(device)
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# DMPRmodel.load_state_dict(torch.load(DMPRweights))
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DMPRmodel = Model(args.cfg, ch=3).to(device)
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DMPRmodel.load_state_dict(torch.load(DMPRweights))
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# 图像测试
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# impth = 'images/input'
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impth = 'images/debug'
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# impth = '/home/thsw/WJ/zjc/AI_old/images/input_0'
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# outpth = 'images/output'
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outpth = 'images/debug_out'
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folders = os.listdir(impth)
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for file in folders:
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imgpath = os.path.join(impth, file)
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img0 = cv2.imread(imgpath)
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assert img0 is not None, 'Image Not Found ' + imgpath
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t_start = time.time()
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# yolo process
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det0 = yolo_process(img0, model, device, args, half)
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det0 = det0.cpu().detach().numpy()
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t_yolo = time.time()
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print(f't_yolo. ({t_yolo - t_start:.3f}s)')
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# STDC process
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det2 = STDC_process(img0, STDC_model, device, args.n_classes, args.stdc_scale)
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# STDC joint yolo
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det0 = stdc_yolo(det2, det0)
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# plot所有box
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# for *xyxy, conf, cls in reversed(det0):
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# label = f'{int(cls)} {conf:.2f}'
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# plot_one_box(xyxy, img0, label=label, color=rainbows[int(cls)], line_thickness=2)
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# DMPR process
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det1 = DMPR_process(img0, DMPRmodel, device, args)
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det1 = det1.cpu().detach().numpy()
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t_dmpr = time.time()
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print(f't_dmpr. ({t_dmpr - t_yolo:.3f}s)')
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# 绘制角点
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plot_points(img0, det1)
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# yolo joint DMPR
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cls = 0 #需要过滤的box类别
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joint_det, dilate_box = dmpr_yolo(det1, det0, img0.shape, cls, args.scale_ratio, args.border)
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t_joint = time.time()
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print(f't_joint. ({t_joint - t_dmpr:.3f}s)')
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# t_end = time.time()
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# print(f'Done. ({t_end - t_start:.3f}s)')
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# 绘制膨胀box
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for *xyxy, flag in dilate_box:
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plot_one_box(xyxy, img0, color=rainbows[int(cls)], line_thickness=2)
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#
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# # 绘制删除满足 在膨胀框内 && 角度差小于90度 的box
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for *xyxy, conf, cls, flag in reversed(joint_det):
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if flag == 0:
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# label = f'{int(cls)} {conf:.2f}'
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label = None
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plot_one_box(xyxy, img0, label=label, color=rainbows[int(cls)], line_thickness=2)
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# save
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save_path = os.path.join(outpth, file)
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cv2.imwrite(save_path, img0)
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if __name__ == '__main__':
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main() |