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--- a +++ b/detect.py @@ -0,0 +1,433 @@ +# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license +""" +Run YOLOv5 detection inference on images, videos, directories, globs, YouTube, webcam, streams, etc. + +Usage - sources: + $ python detect.py --weights yolov5s.pt --source 0 # webcam + img.jpg # image + vid.mp4 # video + screen # screenshot + path/ # directory + list.txt # list of images + list.streams # list of streams + 'path/*.jpg' # glob + 'https://youtu.be/LNwODJXcvt4' # YouTube + 'rtsp://example.com/media.mp4' # RTSP, RTMP, HTTP stream + +Usage - formats: + $ python detect.py --weights yolov5s.pt # PyTorch + yolov5s.torchscript # TorchScript + yolov5s.onnx # ONNX Runtime or OpenCV DNN with --dnn + yolov5s_openvino_model # OpenVINO + yolov5s.engine # TensorRT + yolov5s.mlmodel # CoreML (macOS-only) + yolov5s_saved_model # TensorFlow SavedModel + yolov5s.pb # TensorFlow GraphDef + yolov5s.tflite # TensorFlow Lite + yolov5s_edgetpu.tflite # TensorFlow Edge TPU + yolov5s_paddle_model # PaddlePaddle +""" + +import argparse +import csv +import os +import platform +import sys +from pathlib import Path + +import torch +import copy +import torch.nn.functional as F + + +FILE = Path(__file__).resolve() +ROOT = FILE.parents[0] # YOLOv5 root directory +if str(ROOT) not in sys.path: + sys.path.append(str(ROOT)) # add ROOT to PATH +ROOT = Path(os.path.relpath(ROOT, Path.cwd())) # relative + +from ultralytics.utils.plotting import Annotator, colors, save_one_box + +from models.common import DetectMultiBackend +from utils.dataloaders import IMG_FORMATS, VID_FORMATS, LoadImages, LoadScreenshots, LoadStreams +from utils.general import (LOGGER, Profile, check_file, check_img_size, check_imshow, check_requirements, colorstr, cv2, + increment_path, non_max_suppression, print_args, scale_boxes, strip_optimizer, xyxy2xywh,get_fixed_xyxy) +from utils.torch_utils import select_device, smart_inference_mode +from utils.my_model import MyCNN +from torchvision.ops import roi_align + +@smart_inference_mode() +def run( + weights=ROOT / "yolov5s.pt", # model path or triton URL + source=ROOT / "data/images", # file/dir/URL/glob/screen/0(webcam) + data=ROOT / "data/coco128.yaml", # dataset.yaml path + imgsz=(640, 640), # inference size (height, width) + conf_thres=0.25, # confidence threshold + iou_thres=0.45, # NMS IOU threshold + max_det=1000, # maximum detections per image + device="", # cuda device, i.e. 0 or 0,1,2,3 or cpu + view_img=False, # show results + save_txt=False, # save results to *.txt + save_csv=False, # save results in CSV format + save_conf=False, # save confidences in --save-txt labels + save_crop=False, # save cropped prediction boxes + nosave=False, # do not save images/videos + classes=None, # filter by class: --class 0, or --class 0 2 3 + agnostic_nms=False, # class-agnostic NMS + augment=False, # augmented inference + visualize=False, # visualize features + update=False, # update all models + project=ROOT / "runs/detect", # save results to project/name + name="exp", # save results to project/name + exist_ok=False, # existing project/name ok, do not increment + line_thickness=3, # bounding box thickness (pixels) + hide_labels=False, # hide labels + hide_conf=False, # hide confidences + half=False, # use FP16 half-precision inference + dnn=False, # use OpenCV DNN for ONNX inference + vid_stride=1, # video frame-rate stride +): + source = str(source) + save_img = not nosave and not source.endswith('.txt') # save inference images + is_file = Path(source).suffix[1:] in (IMG_FORMATS + VID_FORMATS) + is_url = source.lower().startswith(('rtsp://', 'rtmp://', 'http://', 'https://')) + webcam = source.isnumeric() or source.endswith('.streams') or (is_url and not is_file) + screenshot = source.lower().startswith('screen') + if is_url and is_file: + source = check_file(source) # download + + # Directories + save_dir = increment_path(Path(project) / name, exist_ok=exist_ok) # increment run + (save_dir / 'labels' if save_txt else save_dir).mkdir(parents=True, exist_ok=True) # make dir + + # Load model + device = select_device(device) + model = DetectMultiBackend(weights, device=device, dnn=dnn, data=data, fp16=half) + stride, names, pt = model.stride, model.names, model.pt + # stride = 16 + imgsz = check_img_size(imgsz, s=stride) # check image size + + # Dataloader + bs = 1 # batch_size + if webcam: + view_img = check_imshow(warn=True) + dataset = LoadStreams(source, img_size=imgsz, stride=stride, auto=pt, vid_stride=vid_stride) + bs = len(dataset) + elif screenshot: + dataset = LoadScreenshots(source, img_size=imgsz, stride=stride, auto=pt) + else: + dataset = LoadImages(source, img_size=imgsz, stride=stride, auto=False, vid_stride=vid_stride) + vid_path, vid_writer = [None] * bs, [None] * bs + + # Run inference + model.warmup(imgsz=(1 if pt or model.triton else bs, 3, *imgsz)) # warmup + seen, windows, dt = 0, [], (Profile(device=device), Profile(device=device), Profile(device=device)) + for path, im, im0s, vid_cap, s, orig_img in dataset: + with dt[0]: + im = torch.from_numpy(im).to(model.device) + im = im.half() if model.fp16 else im.float() # uint8 to fp16/32 + im /= 255 # 0 - 255 to 0.0 - 1.0 + if len(im.shape) == 3: + im = im[None] # expand for batch dim + if model.xml and im.shape[0] > 1: + ims = torch.chunk(im, im.shape[0], 0) + + # Inference + with dt[1]: + visualize = increment_path(save_dir / Path(path).stem, mkdir=True) if visualize else False + if model.xml and im.shape[0] > 1: + pred = None + for image in ims: + if pred is None: + pred,int_feats = model(image, augment=augment, visualize=visualize).unsqueeze(0) + else: + pred, int_feats = torch.cat((pred, model(image, augment=augment, visualize=visualize).unsqueeze(0)), dim=0) + pred = [pred, None] + else: + pred,int_feats = model(im, augment=augment, visualize=visualize) + # NMS + with dt[2]: + pred = non_max_suppression(pred, conf_thres, iou_thres, classes, agnostic_nms, max_det=max_det) + + # int_feats_p3= int_feats[0][0,:,:,:].to(torch.float32) + # int_feats_p3 = int_feats_p3.unsqueeze(0)#.unsqueeze(0) + + int_feats_p2 = int_feats[0][0].to(torch.float32).unsqueeze(0) + int_feats_p3 = int_feats[1][0].to(torch.float32).unsqueeze(0) + + # concat_feat = torch.cat([int_feats_p2,int_feats_p3],dim=1) + in_channels = int_feats_p2.shape[1]+int_feats_p3.shape[1] + cell_attribute_model= MyCNN(num_classes=12, dropout_prob=0.5, in_channels=in_channels).to(device) + folder_name = 'data/WBC_dataset_sample/Attribute_model' + custom_weights_path = f"{folder_name}/last_weights.pth" + custom_weights = torch.load(custom_weights_path) + cell_attribute_model.load_state_dict(custom_weights) + cell_attribute_model.eval().to(device) + + # int_feats_p5= int_feats[1][0,:,:,:].to(torch.float32) + # int_feats_p5 = int_feats_p5.unsqueeze(0)#.unsqueeze(0) + torch.cuda.empty_cache() + + # del int_feats + # resized_int_feats_p5 = F.interpolate(int_feats_p5, size=(int_feats[0].size(2), int_feats[0].size(3)), mode='bilinear', align_corners=False) + # concatenated_features = torch.cat([resized_int_feats_p5,int_feats_p3],dim=1) + + if (len(pred)>0): + all_top_indices_cell_pred = [] + top_indices_cell_pred = [] + pred_Nuclear_Chromatin_array = [] + pred_Nuclear_Shape_array = [] + pred_Nucleus_array = [] + pred_Cytoplasm_array = [] + pred_Cytoplasmic_Basophilia_array = [] + pred_Cytoplasmic_Vacuoles_array = [] + + for i in range(len(pred[0])): + if pred[0][i].numel() > 0: # Check if the tensor is not empty + + pred_tensor = pred[0][i][0:4] + + if pred[0][i][5] != 0: + + img_shape_tensor = torch.tensor([im.shape[2], im.shape[3],im.shape[2],im.shape[3]]).to(device) + + normalized_xyxy=pred_tensor / img_shape_tensor + p2_feature_shape_tensor = torch.tensor([int_feats[0].shape[1], int_feats[0].shape[2],int_feats[0].shape[1],int_feats[0].shape[2]]).to(device) # reduce_channels_layer = torch.nn.Conv2d(1280, 250, kernel_size=1).to(device) + p3_feature_shape_tensor = torch.tensor([int_feats[1].shape[1], int_feats[1].shape[2],int_feats[1].shape[1],int_feats[1].shape[2]]).to(device) # reduce_channels_layer = torch.nn.Conv2d(1280, 250, kernel_size=1).to(device) + + + p2_normalized_xyxy = normalized_xyxy*p2_feature_shape_tensor + p3_normalized_xyxy = normalized_xyxy*p3_feature_shape_tensor + p2_x_min, p2_y_min, p2_x_max, p2_y_max = get_fixed_xyxy(p2_normalized_xyxy,int_feats_p2) + p3_x_min, p3_y_min, p3_x_max, p3_y_max = get_fixed_xyxy(p3_normalized_xyxy,int_feats_p3) + + p2_roi = torch.tensor([p2_x_min, p2_y_min, p2_x_max, p2_y_max], device=device).float() + p3_roi = torch.tensor([p3_x_min, p3_y_min, p3_x_max, p3_y_max], device=device).float() + + batch_index = torch.tensor([0], dtype=torch.float32, device = device) + + # Concatenate the batch index to the bounding box coordinates + p2_roi_with_batch_index = torch.cat([batch_index, p2_roi]) + p3_roi_with_batch_index = torch.cat([batch_index, p3_roi]) + p2_resized_object = roi_align(int_feats_p2, p2_roi_with_batch_index.unsqueeze(0).to(device), output_size=(24, 30)) + p3_resized_object = roi_align(int_feats_p3, p3_roi_with_batch_index.unsqueeze(0).to(device), output_size=(24, 30)) + concat_box = torch.cat([p2_resized_object,p3_resized_object],dim=1) + + output_cell_prediction= cell_attribute_model(concat_box) + output_cell_prediction_prob = F.softmax(output_cell_prediction.view(6,2), dim=1) + top_indices_cell_pred = torch.argmax(output_cell_prediction_prob, dim=1) + pred_Nuclear_Chromatin_array.append(top_indices_cell_pred[0].item()) + pred_Nuclear_Shape_array.append(top_indices_cell_pred[1].item()) + pred_Nucleus_array.append(top_indices_cell_pred[2].item()) + pred_Cytoplasm_array.append(top_indices_cell_pred[3].item()) + pred_Cytoplasmic_Basophilia_array.append(top_indices_cell_pred[4].item()) + pred_Cytoplasmic_Vacuoles_array.append(top_indices_cell_pred[5].item()) + # all_top_indices_cell_pred.append(top_indices_cell_pred.item()) + else: + # top_indices_cell_pred = torch.tensor([0,0,0,0,0,0]).to(device) + pred_Nuclear_Chromatin_array.append(0) + pred_Nuclear_Shape_array.append(0) + pred_Nucleus_array.append(0) + pred_Cytoplasm_array.append(0) + pred_Cytoplasmic_Basophilia_array.append(0) + pred_Cytoplasmic_Vacuoles_array.append(0) + + + + + # Second-stage classifier (optional) + # pred = utils.general.apply_classifier(pred, classifier_model, im, im0s) + + # Define the path for the CSV file + csv_path = save_dir / 'predictions.csv' + + # # Create or append to the CSV file + # def write_to_csv(name, predicts, confid,pred_NC,pred_NS, + # pred_N,pred_C,pred_CB, + # pred_CV,x_min,y_min,x_max,y_max): + # data = {'Image Name': name, 'Prediction': predicts, 'Confidence': confid, 'Nuclear Chromatin':pred_NC, + # 'Nuclear Shape':pred_NS,'Nucleus':pred_N,'Cytoplasm':pred_C, + # 'Cytoplasmic Basophilia': pred_CB, 'Cytoplasmic Vacuoles': pred_CV, + # 'x_min':x_min,'y_min':y_min,'x_max':x_max,'y_max':y_max} + # with open(csv_path, mode='a', newline='') as f: + # writer = csv.DictWriter(f, fieldnames=data.keys()) + # if not csv_path.is_file(): + # writer.writeheader() + # writer.writerow(data) + # Create or append to the CSV file + def write_to_csv(name, predicts, confid, pred_NC, pred_NS, + pred_N, pred_C, pred_CB, pred_CV, + x_min, y_min, x_max, y_max): + data = {'Image Name': name, 'Prediction': predicts, 'Confidence': confid, 'Nuclear Chromatin': pred_NC, + 'Nuclear Shape': pred_NS, 'Nucleus': pred_N, 'Cytoplasm': pred_C, + 'Cytoplasmic Basophilia': pred_CB, 'Cytoplasmic Vacuoles': pred_CV, + 'x_min': x_min, 'y_min': y_min, 'x_max': x_max, 'y_max': y_max} + + # Check if the CSV file exists + if not os.path.isfile(csv_path): + with open(csv_path, mode='w', newline='') as f: + writer = csv.DictWriter(f, fieldnames=data.keys()) + writer.writeheader() + + # Append data to CSV file + with open(csv_path, mode='a', newline='') as f: + writer = csv.DictWriter(f, fieldnames=data.keys()) + writer.writerow(data) + + # Process predictions + for i, det in enumerate(pred): # per image + seen += 1 + if webcam: # batch_size >= 1 + p, im0, frame = path[i], im0s[i].copy(), dataset.count + s += f'{i}: ' + else: + p, im0, frame = path, im0s.copy(), getattr(dataset, 'frame', 0) + + p = Path(p) # to Path + save_path = str(save_dir / p.name) # im.jpg + txt_path = str(save_dir / 'labels' / p.stem) + ('' if dataset.mode == 'image' else f'_{frame}') # im.txt + s += '%gx%g ' % im.shape[2:] # print string + gn = torch.tensor(im0.shape)[[1, 0, 1, 0]] # normalization gain whwh + imc = im0.copy() if save_crop else im0 # for save_crop + annotator = Annotator(im0, line_width=line_thickness, example=str(names)) + if len(det): + # Rescale boxes from img_size to im0 size + det[:, :4] = scale_boxes(im.shape[2:], det[:, :4], im0.shape).round() + + # Print results + for c in det[:, 5].unique(): + n = (det[:, 5] == c).sum() # detections per class + s += f"{n} {names[int(c)]}{'s' * (n > 1)}, " # add to string + # Write results + for count, (*xyxy, conf, cls) in enumerate(det): + c = int(cls) # integer class + label = names[c] if hide_conf else f'{names[c]}' + confidence = float(conf) + confidence_str = f'{confidence:.2f}' + + if save_csv: + x_min,y_min,x_max,y_max = xyxy + + # Scaling factors + scale_width = orig_img.shape[1] / 640 + scale_height = orig_img.shape[0] / 640 + + # Convert bounding box coordinates to 800x448 image + x_min_new = int(x_min * scale_width) + y_min_new = int(y_min * scale_height) + x_max_new = int(x_max * scale_width) + y_max_new = int(y_max * scale_height) + + write_to_csv(p.name, label, confidence_str, + pred_Nuclear_Chromatin_array[count],pred_Nuclear_Shape_array[count], + pred_Nucleus_array[count],pred_Cytoplasm_array[count],pred_Cytoplasmic_Basophilia_array[count], + pred_Cytoplasmic_Vacuoles_array[count], + int(x_min_new),int(y_min_new), + int(x_max_new),int(y_max_new)) + + + if save_txt: # Write to file + xywh = (xyxy2xywh(torch.tensor(xyxy).view(1, 4)) / gn).view(-1).tolist() # normalized xywh + line = (cls, *xywh, conf) if save_conf else (cls, *xywh) # label format + with open(f'{txt_path}.txt', 'a') as f: + f.write(('%g ' * len(line)).rstrip() % line + '\n') + + if save_img or save_crop or view_img: # Add bbox to image + c = int(cls) # integer class + label = None if hide_labels else (names[c] if hide_conf else f'{names[c]} {conf:.2f}') + annotator.box_label(xyxy, label, color=colors(c, True)) + # annotator.my_box_label(xyxy, label, color=colors(c, True), att1=pred_Nuclear_Chromatin_array[0], + # att2 = pred_Nuclear_Shape_array[0], att3 = pred_Nucleus_array[0], + # att4 = pred_Cytoplasm_array[0], att5 = pred_Cytoplasmic_Basophilia_array[0], + # att6 = pred_Cytoplasmic_Vacuoles_array[0] + # ) + + if save_crop: + save_one_box(xyxy, imc, file=save_dir / 'crops' / names[c] / f'{p.stem}.jpg', BGR=True) + + # Stream results + im0 = annotator.result() + if view_img: + if platform.system() == 'Linux' and p not in windows: + windows.append(p) + cv2.namedWindow(str(p), cv2.WINDOW_NORMAL | cv2.WINDOW_KEEPRATIO) # allow window resize (Linux) + cv2.resizeWindow(str(p), im0.shape[1], im0.shape[0]) + cv2.imshow(str(p), im0) + cv2.waitKey(1) # 1 millisecond + + # Save results (image with detections) + if save_img: + if dataset.mode == 'image': + cv2.imwrite(save_path, im0) + else: # 'video' or 'stream' + if vid_path[i] != save_path: # new video + vid_path[i] = save_path + if isinstance(vid_writer[i], cv2.VideoWriter): + vid_writer[i].release() # release previous video writer + if vid_cap: # video + fps = vid_cap.get(cv2.CAP_PROP_FPS) + w = int(vid_cap.get(cv2.CAP_PROP_FRAME_WIDTH)) + h = int(vid_cap.get(cv2.CAP_PROP_FRAME_HEIGHT)) + else: # stream + fps, w, h = 30, im0.shape[1], im0.shape[0] + save_path = str(Path(save_path).with_suffix('.mp4')) # force *.mp4 suffix on results videos + vid_writer[i] = cv2.VideoWriter(save_path, cv2.VideoWriter_fourcc(*'mp4v'), fps, (w, h)) + vid_writer[i].write(im0) + + # Print time (inference-only) + LOGGER.info(f"{s}{'' if len(det) else '(no detections), '}{dt[1].dt * 1E3:.1f}ms") + + # Print results + t = tuple(x.t / seen * 1E3 for x in dt) # speeds per image + LOGGER.info(f'Speed: %.1fms pre-process, %.1fms inference, %.1fms NMS per image at shape {(1, 3, *imgsz)}' % t) + if save_txt or save_img: + s = f"\n{len(list(save_dir.glob('labels/*.txt')))} labels saved to {save_dir / 'labels'}" if save_txt else '' + LOGGER.info(f"Results saved to {colorstr('bold', save_dir)}{s}") + if update: + strip_optimizer(weights[0]) # update model (to fix SourceChangeWarning) + + +def parse_opt(): + parser = argparse.ArgumentParser() + parser.add_argument('--weights', nargs='+', type=str, default=ROOT / 'runs/train/yolov5x_300Epochs_training/weights/best.pt', help='model path or triton URL') + parser.add_argument('--source', type=str, default='/home/iml/Desktop/bc_experiment/HCM_V3/HCM_840_attribute/images/test/', help='file/dir/URL/glob/screen/0(webcam)') + parser.add_argument('--data', type=str, default=ROOT / 'data/WBC_v1.yaml', help='(optional) dataset.yaml path') + parser.add_argument('--imgsz', '--img', '--img-size', nargs='+', type=int, default=[640], help='inference size h,w') + parser.add_argument('--conf-thres', type=float, default=0.25, help='confidence threshold') + parser.add_argument('--iou-thres', type=float, default=0.45, help='NMS IoU threshold') + parser.add_argument('--max-det', type=int, default=1000, help='maximum detections per image') + parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu') + parser.add_argument('--view-img', action='store_true', help='show results') + parser.add_argument('--save-txt', action='store_true', help='save results to *.txt') + parser.add_argument('--save-csv', action='store_true', help='save results in CSV format') + parser.add_argument('--save-conf', action='store_true', help='save confidences in --save-txt labels') + parser.add_argument('--save-crop', action='store_true', help='save cropped prediction boxes') + parser.add_argument('--nosave', action='store_true', help='do not save images/videos') + parser.add_argument('--classes', nargs='+', type=int, help='filter by class: --classes 0, or --classes 0 2 3') + parser.add_argument('--agnostic-nms', action='store_true', help='class-agnostic NMS') + parser.add_argument('--augment', action='store_true', help='augmented inference') + parser.add_argument('--visualize', action='store_true', help='visualize features') + parser.add_argument('--update', action='store_true', help='update all models') + parser.add_argument('--project', default=ROOT / 'runs/detect', help='save results to project/name') + parser.add_argument('--name', default='exp', help='save results to project/name') + parser.add_argument('--exist-ok', action='store_true', help='existing project/name ok, do not increment') + parser.add_argument('--line-thickness', default=3, type=int, help='bounding box thickness (pixels)') + parser.add_argument('--hide-labels', default=False, action='store_true', help='hide labels') + parser.add_argument('--hide-conf', default=False, action='store_true', help='hide confidences') + parser.add_argument('--half', action='store_true', help='use FP16 half-precision inference') + parser.add_argument('--dnn', action='store_true', help='use OpenCV DNN for ONNX inference') + parser.add_argument('--vid-stride', type=int, default=1, help='video frame-rate stride') + opt = parser.parse_args() + opt.imgsz *= 2 if len(opt.imgsz) == 1 else 1 # expand + print_args(vars(opt)) + return opt + + +def main(opt): + check_requirements(ROOT / 'requirements.txt', exclude=('tensorboard', 'thop')) + run(**vars(opt)) + + +if __name__ == '__main__': + opt = parse_opt() + main(opt)