96 lines
4.0 KiB
Python
Executable File
96 lines
4.0 KiB
Python
Executable File
import argparse
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import time
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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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from utils import torch_utils
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def detect(cfg, weights, images, output='output', img_size=416, conf_thres=0.3, nms_thres=0.45, save_txt=False,
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save_images=True):
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device = torch_utils.select_device()
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os.system('rm -rf ' + output)
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os.makedirs(output, exist_ok=True)
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# Load model
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model = Darknet(cfg, img_size)
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if weights.endswith('.pt'): # pytorch format
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if weights.endswith('weights/yolov3.pt') and not os.path.isfile(weights):
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os.system('wget https://storage.googleapis.com/ultralytics/yolov3.pt -O ' + weights)
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model.load_state_dict(torch.load(weights, map_location='cpu')['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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dataloader = load_images(images, img_size=img_size)
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# Classes and colors
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classes = load_classes(parse_data_cfg('cfg/coco.data')['names']) # Extracts class labels from file
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colors = [[random.randint(0, 255), random.randint(0, 255), random.randint(0, 255)] for _ in range(len(classes))]
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for i, (path, img, im0) in enumerate(dataloader):
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print("%g/%g '%s': " % (i + 1, len(dataloader), path), end='')
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t = time.time()
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# Get detections
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with torch.no_grad():
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img = torch.from_numpy(img).unsqueeze(0).to(device)
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if ONNX_EXPORT:
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pred = torch.onnx._export(model, img, 'weights/model.onnx', verbose=True)
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return # ONNX export
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pred = model(img)
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pred = pred[pred[:, :, 4] > conf_thres]
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if len(pred) > 0:
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detections = non_max_suppression(pred.unsqueeze(0), conf_thres, nms_thres)[0]
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# Draw bounding boxes and labels of detections
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if detections is not None:
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save_img_path = os.path.join(output, path.split('/')[-1])
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save_txt_path = save_img_path + '.txt'
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# Rescale boxes from 416 to true image size
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detections[:, :4] = scale_coords(img_size, detections[:, :4], im0.shape)
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unique_classes = detections[:, -1].cpu().unique()
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for i in unique_classes:
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n = (detections[:, -1].cpu() == i).sum()
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print('%g %ss' % (n, classes[int(i)]), end=', ')
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for x1, y1, x2, y2, conf, cls_conf, cls_pred in detections:
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if save_txt: # Write to file
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with open(save_txt_path, 'a') as file:
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file.write('%g %g %g %g %g %g\n' % (x1, y1, x2, y2, cls_pred, cls_conf * conf))
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if save_images: # Add bbox to the image
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label = '%s %.2f' % (classes[int(cls_pred)], conf)
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plot_one_box([x1, y1, x2, y2], im0, label=label, color=colors[int(cls_pred)])
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if save_images: # Save generated image with detections
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cv2.imwrite(save_img_path, im0)
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print(' Done. (%.3fs)' % (time.time() - t))
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if platform == 'darwin': # MacOS
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os.system('open ' + output + '&& open ' + save_img_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('--weights', type=str, default='weights/yolov3.pt', 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=0.50, help='object confidence threshold')
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parser.add_argument('--nms-thres', type=float, default=0.45, 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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detect(opt.cfg, opt.weights, opt.images, img_size=opt.img_size, conf_thres=opt.conf_thres, nms_thres=opt.nms_thres)
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