128 lines
4.6 KiB
Python
128 lines
4.6 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 *
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from utils.datasets import *
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from utils.utils import *
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def detect(
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cfg,
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data_cfg,
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weights,
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images,
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output='output', # output folder
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img_size=416,
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conf_thres=0.3,
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nms_thres=0.5,
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save_txt=False,
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save_images=True,
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webcam=False
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):
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device = torch_utils.select_device()
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if os.path.exists(output):
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shutil.rmtree(output) # delete output folder
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os.makedirs(output) # make new output folder
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# Initialize model
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model = Darknet(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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model.to(device).eval()
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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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save_images = False
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dataloader = LoadWebcam(img_size=img_size)
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else:
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dataloader = LoadImages(images, img_size=img_size)
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# Get classes and colors
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classes = load_classes(parse_data_cfg(data_cfg)['names'])
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colors = [[random.randint(0, 255) for _ in range(3)] for _ in range(len(classes))]
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for i, (path, img, im0, vid_cap) in enumerate(dataloader):
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t = time.time()
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save_path = str(Path(output) / 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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if ONNX_EXPORT:
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torch.onnx.export(model, img, 'weights/model.onnx', verbose=True)
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return
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pred = model(img)
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detections = non_max_suppression(pred, conf_thres, nms_thres)[0]
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if detections is not None and len(detections) > 0:
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# Rescale boxes from 416 to true image size
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scale_coords(img_size, detections[:, :4], im0.shape).round()
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# Print results to screen
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for c in detections[:, -1].unique():
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n = (detections[:, -1] == c).sum()
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print('%g %ss' % (n, classes[int(c)]), end=', ')
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# Draw bounding boxes and labels of detections
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for *xyxy, conf, cls_conf, cls in detections:
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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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# Add bbox to the 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('Done. (%.3fs)' % (time.time() - t))
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if webcam: # Show live webcam
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cv2.imshow(weights, im0)
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if save_images: # Save generated image with detections
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if dataloader.mode == 'video':
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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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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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fps = vid_cap.get(cv2.CAP_PROP_FPS)
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vid_writer = cv2.VideoWriter(save_path, cv2.VideoWriter_fourcc(*'avc1'), fps, (width, height))
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vid_writer.write(im0)
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else:
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cv2.imwrite(save_path, im0)
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if save_images and platform == 'darwin': # macos
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os.system('open ' + output + ' ' + save_path)
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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.cfg', help='cfg file path')
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parser.add_argument('--data-cfg', type=str, default='data/coco.data', help='coco.data file path')
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parser.add_argument('--weights', type=str, default='weights/yolov3.weights', help='path to weights file')
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parser.add_argument('--images', type=str, default='data/samples', help='path to images')
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parser.add_argument('--img-size', type=int, default=32 * 13, help='size of each image dimension')
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parser.add_argument('--conf-thres', type=float, default=0.5, 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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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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opt.cfg,
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opt.data_cfg,
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opt.weights,
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opt.images,
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img_size=opt.img_size,
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conf_thres=opt.conf_thres,
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nms_thres=opt.nms_thres
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)
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