106 lines
3.8 KiB
Python
106 lines
3.8 KiB
Python
"""Inference demo of directional point detector."""
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import math
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import cv2 as cv
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import numpy as np
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import torch
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from torchvision.transforms import ToTensor
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import config
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from data import get_predicted_points
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from detector import DirectionalPointDetector
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from utils import Timer
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def plot_marking_points(image, marking_points):
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"""Plot marking points on the image and show."""
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height = image.shape[0]
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width = image.shape[1]
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for marking_point in marking_points:
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p0_x = width * marking_point.x - 0.5
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p0_y = height * marking_point.y - 0.5
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cos_val = math.cos(marking_point.direction)
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sin_val = math.sin(marking_point.direction)
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p1_x = p0_x + 50*cos_val
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p1_y = p0_y + 50*sin_val
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p2_x = p0_x - 50*sin_val
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p2_y = p0_y + 50*cos_val
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p3_x = p0_x + 50*sin_val
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p3_y = p0_y - 50*cos_val
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p0_x = int(round(p0_x))
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p0_y = int(round(p0_y))
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p1_x = int(round(p1_x))
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p1_y = int(round(p1_y))
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p2_x = int(round(p2_x))
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p2_y = int(round(p2_y))
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cv.line(image, (p0_x, p0_y), (p1_x, p1_y), (0, 0, 255))
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if marking_point.shape > 0.5:
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cv.line(image, (p0_x, p0_y), (p2_x, p2_y), (0, 0, 255))
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else:
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p3_x = int(round(p3_x))
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p3_y = int(round(p3_y))
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cv.line(image, (p2_x, p2_y), (p3_x, p3_y), (0, 0, 255))
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def preprocess_image(image):
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"""Preprocess numpy image to torch tensor."""
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if image.shape[0] != 512 or image.shape[1] != 512:
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image = cv.resize(image, (512, 512))
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return torch.unsqueeze(ToTensor()(image), 0)
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def detect_video(detector, device, args):
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"""Demo for detecting video."""
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timer = Timer()
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input_video = cv.VideoCapture(args.video)
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frame_width = int(input_video.get(cv.CAP_PROP_FRAME_WIDTH))
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frame_height = int(input_video.get(cv.CAP_PROP_FRAME_HEIGHT))
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output_video = cv.VideoWriter()
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if args.save:
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output_video.open('record.avi', cv.VideoWriter_fourcc(* 'MJPG'),
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input_video.get(cv.CAP_PROP_FPS),
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(frame_width, frame_height))
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frame = np.empty([frame_height, frame_width, 3], dtype=np.uint8)
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while input_video.read(frame)[0]:
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if args.timing:
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timer.tic()
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prediction = detector(preprocess_image(frame).to(device))
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if args.timing:
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timer.toc()
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pred_points = get_predicted_points(prediction[0], args.thresh)
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if pred_points:
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plot_marking_points(frame, list(list(zip(*pred_points))[1]))
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cv.imshow('demo', frame)
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cv.waitKey(1)
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if args.save:
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output_video.write(frame)
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input_video.release()
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output_video.release()
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def detect_image(detector, device, args):
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"""Demo for detecting images."""
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image_file = input('Enter image file path: ')
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image = cv.imread(image_file)
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prediction = detector(preprocess_image(image).to(device))
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pred_points = get_predicted_points(prediction[0], args.thresh)
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if pred_points:
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plot_marking_points(image, list(list(zip(*pred_points))[1]))
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cv.imshow('demo', image)
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cv.waitKey(1)
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def inference_detector(args):
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"""Inference demo of directional point detector."""
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args.cuda = not args.disable_cuda and torch.cuda.is_available()
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device = torch.device('cuda:' + str(args.gpu_id) if args.cuda else 'cpu')
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dp_detector = DirectionalPointDetector(
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3, args.depth_factor, config.NUM_FEATURE_MAP_CHANNEL).to(device)
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dp_detector.load_state_dict(torch.load(args.detector_weights))
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if args.mode == "image":
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detect_image(dp_detector, device, args)
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elif args.mode == "video":
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detect_video(dp_detector, device, args)
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if __name__ == '__main__':
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inference_detector(config.get_parser_for_inference().parse_args())
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