138 lines
5.3 KiB
Python
Executable File
138 lines
5.3 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(
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cfg,
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weights,
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images_path,
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output='output',
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img_size=416,
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conf_thres=0.3,
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nms_thres=0.45,
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save_txt=False,
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save_images=True,
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):
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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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checkpoint = torch.load(weights, map_location='cpu')
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model.load_state_dict(checkpoint['model'])
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del checkpoint
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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_path, 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, img0) 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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img = img0
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# The amount of padding that was added
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pad_x = max(img.shape[0] - img.shape[1], 0) * (img_size / max(img.shape))
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pad_y = max(img.shape[1] - img.shape[0], 0) * (img_size / max(img.shape))
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# Image height and width after padding is removed
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unpad_h = img_size - pad_y
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unpad_w = img_size - pad_x
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unique_classes = detections[:, -1].cpu().unique()
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# write results to .txt file
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results_img_path = os.path.join(output, path.split('/')[-1])
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results_txt_path = results_img_path + '.txt'
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if os.path.isfile(results_txt_path):
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os.remove(results_txt_path)
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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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# Rescale coordinates to original dimensions
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box_h = ((y2 - y1) / unpad_h) * img.shape[0]
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box_w = ((x2 - x1) / unpad_w) * img.shape[1]
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y1 = (((y1 - pad_y // 2) / unpad_h) * img.shape[0]).round()
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x1 = (((x1 - pad_x // 2) / unpad_w) * img.shape[1]).round()
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x2 = (x1 + box_w).round()
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y2 = (y1 + box_h).round()
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x1, y1, x2, y2 = max(x1, 0), max(y1, 0), max(x2, 0), max(y2, 0)
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# write to file
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if save_txt:
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with open(results_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:
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# 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], img, label=label, color=colors[int(cls_pred)])
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if save_images:
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# Save generated image with detections
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cv2.imwrite(results_img_path, img)
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print(' Done. (%.3fs)' % (time.time() - t))
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if platform == 'darwin': # MacOS (local)
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os.system('open ' + output)
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if __name__ == '__main__':
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parser = argparse.ArgumentParser()
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parser.add_argument('--image-folder', type=str, default='data/samples', help='path to images')
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parser.add_argument('--output-folder', type=str, default='output', help='path to outputs')
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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('--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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parser.add_argument('--img-size', type=int, default=32 * 13, help='size of each image dimension')
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opt = parser.parse_args()
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print(opt)
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detect(
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opt.cfg,
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opt.weights,
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opt.image_folder,
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output=opt.output_folder,
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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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