139 lines
5.5 KiB
Python
139 lines
5.5 KiB
Python
import argparse
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import time
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from sys import platform
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from models import * # set ONNX_EXPORT in models.py
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from utils.datasets import *
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from utils.utils import *
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def detect(save_txt=False, save_img=False, stream_img=False):
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img_size = (320, 192) if ONNX_EXPORT else opt.img_size # (320, 192) or (416, 256) or (608, 352) for (height, width)
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out, source, weights, half = opt.output, opt.source, opt.weights, opt.half
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webcam = source == '0' or source.startswith('rtsp') or source.startswith('http')
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# Initialize
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device = torch_utils.select_device(force_cpu=ONNX_EXPORT)
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torch.backends.cudnn.benchmark = False # set True to speed up constant image size inference
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if os.path.exists(out):
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shutil.rmtree(out) # delete output folder
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os.makedirs(out) # make new output folder
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# Initialize model
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model = Darknet(opt.cfg, img_size)
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# Load weights
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if weights.endswith('.pt'): # pytorch format
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model.load_state_dict(torch.load(weights, map_location=device)['model'])
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else: # darknet format
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_ = load_darknet_weights(model, weights)
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# Fuse Conv2d + BatchNorm2d layers
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# model.fuse()
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# Eval mode
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model.to(device).eval()
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# Export mode
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if ONNX_EXPORT:
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img = torch.zeros((1, 3) + img_size) # (1, 3, 320, 192)
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torch.onnx.export(model, img, 'weights/export.onnx', verbose=True)
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return
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# Half precision
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half = half and device.type != 'cpu' # half precision only supported on CUDA
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if half:
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model.half()
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# Set Dataloader
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vid_path, vid_writer = None, None
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if webcam:
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stream_img = True
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dataset = LoadWebcam(source, img_size=img_size, half=half)
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else:
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save_img = True
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dataset = LoadImages(source, img_size=img_size, half=half)
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# Get classes and colors
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classes = load_classes(parse_data_cfg(opt.data)['names'])
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colors = [[random.randint(0, 255) for _ in range(3)] for _ in range(len(classes))]
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# Run inference
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t0 = time.time()
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for path, img, im0, vid_cap in dataset:
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t = time.time()
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save_path = str(Path(out) / Path(path).name)
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# Get detections
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img = torch.from_numpy(img).unsqueeze(0).to(device)
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pred, _ = model(img)
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det = non_max_suppression(pred.float(), opt.conf_thres, opt.nms_thres)[0]
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s = '%gx%g ' % img.shape[2:] # print string
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if det is not None and len(det):
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# Rescale boxes from img_size to im0 size
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det[:, :4] = scale_coords(img.shape[2:], det[:, :4], im0.shape).round()
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# Print results
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for c in det[:, -1].unique():
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n = (det[:, -1] == c).sum() # detections per class
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s += '%g %ss, ' % (n, classes[int(c)]) # add to string
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# Write results
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for *xyxy, conf, _, cls in det:
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if save_txt: # Write to file
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with open(save_path + '.txt', 'a') as file:
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file.write(('%g ' * 6 + '\n') % (*xyxy, cls, conf))
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if save_img or stream_img: # Add bbox to image
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label = '%s %.2f' % (classes[int(cls)], conf)
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plot_one_box(xyxy, im0, label=label, color=colors[int(cls)])
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print('%sDone. (%.3fs)' % (s, time.time() - t))
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# Stream results
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if stream_img:
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cv2.imshow(weights, im0)
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# Save results (image with detections)
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if save_img:
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if dataset.mode == 'images':
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cv2.imwrite(save_path, im0)
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else:
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if vid_path != save_path: # new video
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vid_path = save_path
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if isinstance(vid_writer, cv2.VideoWriter):
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vid_writer.release() # release previous video writer
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fps = vid_cap.get(cv2.CAP_PROP_FPS)
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width = int(vid_cap.get(cv2.CAP_PROP_FRAME_WIDTH))
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height = int(vid_cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
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vid_writer = cv2.VideoWriter(save_path, cv2.VideoWriter_fourcc(*opt.fourcc), fps, (width, height))
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vid_writer.write(im0)
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if save_txt or save_img:
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print('Results saved to %s' % os.getcwd() + os.sep + out)
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if platform == 'darwin': # MacOS
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os.system('open ' + out + ' ' + save_path)
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print('Done. (%.3fs)' % (time.time() - t0))
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if __name__ == '__main__':
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parser = argparse.ArgumentParser()
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parser.add_argument('--cfg', type=str, default='cfg/yolov3-spp.cfg', help='cfg file path')
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parser.add_argument('--data', type=str, default='data/coco.data', help='coco.data file path')
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parser.add_argument('--weights', type=str, default='weights/yolov3-spp.weights', help='path to weights file')
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parser.add_argument('--source', type=str, default='data/samples', help='source') # input file/folder, 0 for webcam
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parser.add_argument('--output', type=str, default='output', help='output folder') # output folder
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parser.add_argument('--img-size', type=int, default=416, help='inference size (pixels)')
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parser.add_argument('--conf-thres', type=float, default=0.3, help='object confidence threshold')
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parser.add_argument('--nms-thres', type=float, default=0.5, help='iou threshold for non-maximum suppression')
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parser.add_argument('--fourcc', type=str, default='mp4v', help='output video codec (verify ffmpeg support)')
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parser.add_argument('--half', action='store_true', help='half precision FP16 inference')
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opt = parser.parse_args()
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print(opt)
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with torch.no_grad():
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detect()
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