146 lines
5.9 KiB
Python
Executable File
146 lines
5.9 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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cuda = torch.cuda.is_available()
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device = torch.device('cuda:0' if cuda else 'cpu')
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parser = argparse.ArgumentParser()
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# Get data configuration
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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('-plot_flag', type=bool, default=True)
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parser.add_argument('-txt_out', type=bool, default=False)
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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('-class_path', type=str, default='data/coco.names', help='path to class label file')
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parser.add_argument('-conf_thres', type=float, default=0.98, 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('-batch_size', type=int, default=1, help='size of the batches')
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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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def detect(opt):
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os.system('rm -rf ' + opt.output_folder)
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os.makedirs(opt.output_folder, exist_ok=True)
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# Load model
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model = Darknet(opt.cfg, opt.img_size)
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#weights_path = 'checkpoints/yolov3.weights'
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weights_path = 'checkpoints/yolov3.pt'
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if weights_path.endswith('.weights'): # saved in darknet format
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load_weights(model, weights_path)
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else: # endswith('.pt'), saved in pytorch format
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checkpoint = torch.load(weights_path, map_location='cpu')
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model.load_state_dict(checkpoint['model'])
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del checkpoint
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# current = model.state_dict()
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# saved = checkpoint['model']
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# # 1. filter out unnecessary keys
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# saved = {k: v for k, v in saved.items() if ((k in current) and (current[k].shape == v.shape))}
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# # 2. overwrite entries in the existing state dict
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# current.update(saved)
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# # 3. load the new state dict
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# model.load_state_dict(current)
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# model.to(device).eval()
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# del checkpoint, current, saved
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model.to(device).eval()
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# Set Dataloader
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classes = load_classes(opt.class_path) # Extracts class labels from file
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dataloader = load_images(opt.image_folder, batch_size=opt.batch_size, img_size=opt.img_size)
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imgs = [] # Stores image paths
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img_detections = [] # Stores detections for each image index
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prev_time = time.time()
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for batch_i, (img_paths, img) in enumerate(dataloader):
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print(batch_i, img.shape, end=' ')
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# Get detections
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with torch.no_grad():
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chip = torch.from_numpy(img).unsqueeze(0).to(device)
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pred = model(chip)
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pred = pred[pred[:, :, 4] > opt.conf_thres]
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if len(pred) > 0:
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detections = non_max_suppression(pred.unsqueeze(0), opt.conf_thres, opt.nms_thres)
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img_detections.extend(detections)
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imgs.extend(img_paths)
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print('Batch %d... (Done %.3fs)' % (batch_i, time.time() - prev_time))
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prev_time = time.time()
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# Bounding-box colors
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color_list = [[random.randint(0, 255), random.randint(0, 255), random.randint(0, 255)] for _ in range(len(classes))]
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if len(img_detections) == 0:
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return
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# Iterate through images and save plot of detections
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for img_i, (path, detections) in enumerate(zip(imgs, img_detections)):
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print("image %g: '%s'" % (img_i, path))
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if opt.plot_flag:
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img = cv2.imread(path)
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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) * (opt.img_size / max(img.shape))
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pad_y = max(img.shape[1] - img.shape[0], 0) * (opt.img_size / max(img.shape))
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# Image height and width after padding is removed
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unpad_h = opt.img_size - pad_y
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unpad_w = opt.img_size - pad_x
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# Draw bounding boxes and labels of detections
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if detections is not None:
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unique_classes = detections[:, -1].cpu().unique()
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bbox_colors = random.sample(color_list, len(unique_classes))
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# write results to .txt file
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results_img_path = os.path.join(opt.output_folder, 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)]))
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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().item()
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x1 = (((x1 - pad_x // 2) / unpad_w) * img.shape[1]).round().item()
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x2 = (x1 + box_w).round().item()
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y2 = (y1 + box_h).round().item()
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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 opt.txt_out:
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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 opt.plot_flag:
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# Add the bbox to the plot
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label = '%s %.2f' % (classes[int(cls_pred)], conf)
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color = bbox_colors[int(np.where(unique_classes == int(cls_pred))[0])]
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plot_one_box([x1, y1, x2, y2], img, label=label, color=color)
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if opt.plot_flag:
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# Save generated image with detections
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cv2.imwrite(results_img_path.replace('.bmp', '.jpg').replace('.tif', '.jpg'), img)
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if __name__ == '__main__':
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torch.cuda.empty_cache()
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detect(opt)
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