import argparse import sys from pathlib import Path import cv2 import numpy as np import torch import torch.backends.cudnn as cudnn from models.experimental import attempt_load from utils.datasets import LoadImages, LoadStreams from utils.general import ( apply_classifier, check_img_size, check_imshow, check_requirements, check_suffix, colorstr, increment_path, is_ascii, non_max_suppression, save_one_box, scale_coords, set_logging, strip_optimizer, xyxy2xywh, ) from utils.plots import Annotator, colors from utils.torch_utils import load_classifier, select_device, time_sync # 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 @torch.no_grad() def run_yolo_v5( weights="yolov5s.pt", # model.pt path(s) source="data/images", # file/dir/URL/glob, 0 for webcam imgsz=640, # inference size (pixels) 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_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="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 ): save_img = not nosave and not source.endswith( ".txt" ) # save inference images webcam = ( source.isnumeric() or source.endswith(".txt") or source.lower().startswith( ("rtsp://", "rtmp://", "http://", "https://") ) ) # 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 # Initialize set_logging() device = select_device(device) half &= device.type != "cpu" # half precision only supported on CUDA # Load model w = weights[0] if isinstance(weights, list) else weights classify, suffix, suffixes = ( False, Path(w).suffix.lower(), [".pt", ".onnx", ".tflite", ".pb", ""], ) check_suffix(w, suffixes) # check weights have acceptable suffix pt, onnx, tflite, pb, saved_model = ( suffix == x for x in suffixes ) # backend booleans stride, names = 64, [f"class{i}" for i in range(1000)] # assign defaults if pt: model = attempt_load(weights, map_location=device) # load FP32 model stride = int(model.stride.max()) # model stride names = ( model.module.names if hasattr(model, "module") else model.names ) # get class names if half: model.half() # to FP16 if classify: # second-stage classifier modelc = load_classifier(name="resnet50", n=2) # initialize modelc.load_state_dict( torch.load("resnet50.pt", map_location=device)["model"] ).to(device).eval() elif onnx: check_requirements(("onnx", "onnxruntime")) import onnxruntime session = onnxruntime.InferenceSession(w, None) else: # TensorFlow models check_requirements(("tensorflow>=2.4.1",)) import tensorflow as tf if ( pb ): # https://www.tensorflow.org/guide/migrate#a_graphpb_or_graphpbtxt def wrap_frozen_graph(gd, inputs, outputs): x = tf.compat.v1.wrap_function( lambda: tf.compat.v1.import_graph_def(gd, name=""), [] ) # wrapped import return x.prune( tf.nest.map_structure(x.graph.as_graph_element, inputs), tf.nest.map_structure(x.graph.as_graph_element, outputs), ) graph_def = tf.Graph().as_graph_def() graph_def.ParseFromString(open(w, "rb").read()) frozen_func = wrap_frozen_graph( gd=graph_def, inputs="x:0", outputs="Identity:0" ) elif saved_model: model = tf.keras.models.load_model(w) elif tflite: interpreter = tf.lite.Interpreter( model_path=w ) # load TFLite model interpreter.allocate_tensors() # allocate input_details = interpreter.get_input_details() # inputs output_details = interpreter.get_output_details() # outputs int8 = ( input_details[0]["dtype"] == np.uint8 ) # is TFLite quantized uint8 model imgsz = check_img_size(imgsz, s=stride) # check image size ascii = is_ascii(names) # names are ascii (use PIL for UTF-8) # Dataloader print("Loading data from the source", source) if webcam: view_img = check_imshow() cudnn.benchmark = ( True # set True to speed up constant image size inference ) dataset = LoadStreams(source, img_size=imgsz, stride=stride, auto=pt) bs = len(dataset) # batch_size else: dataset = LoadImages(source, img_size=imgsz, stride=stride, auto=pt) bs = 1 # batch_size vid_path, vid_writer = [None] * bs, [None] * bs # Run inference if pt and device.type != "cpu": model( torch.zeros(1, 3, *imgsz) .to(device) .type_as(next(model.parameters())) ) # run once dt, seen = [0.0, 0.0, 0.0], 0 results = [] for path, img, im0s, vid_cap in dataset: t1 = time_sync() if onnx: img = img.astype("float32") else: img = torch.from_numpy(img).to(device) img = img.half() if half else img.float() # uint8 to fp16/32 img = img / 255.0 # 0 - 255 to 0.0 - 1.0 if len(img.shape) == 3: img = img[None] # expand for batch dim t2 = time_sync() dt[0] += t2 - t1 # Inference if pt: visualize = ( increment_path(save_dir / Path(path).stem, mkdir=True) if visualize else False ) pred = model(img, augment=augment, visualize=visualize)[0] elif onnx: pred = torch.tensor( session.run( [session.get_outputs()[0].name], {session.get_inputs()[0].name: img}, ) ) else: # tensorflow model (tflite, pb, saved_model) imn = img.permute(0, 2, 3, 1).cpu().numpy() # image in numpy if pb: pred = frozen_func(x=tf.constant(imn)).numpy() elif saved_model: pred = model(imn, training=False).numpy() elif tflite: if int8: scale, zero_point = input_details[0]["quantization"] imn = (imn / scale + zero_point).astype( np.uint8 ) # de-scale interpreter.set_tensor(input_details[0]["index"], imn) interpreter.invoke() pred = interpreter.get_tensor(output_details[0]["index"]) if int8: scale, zero_point = output_details[0]["quantization"] pred = ( pred.astype(np.float32) - zero_point ) * scale # re-scale pred[..., 0] *= imgsz[1] # x pred[..., 1] *= imgsz[0] # y pred[..., 2] *= imgsz[1] # w pred[..., 3] *= imgsz[0] # h pred = torch.tensor(pred) t3 = time_sync() dt[1] += t3 - t2 # NMS pred = non_max_suppression( pred, conf_thres, iou_thres, classes, agnostic_nms, max_det=max_det ) dt[2] += time_sync() - t3 # Second-stage classifier (optional) if classify: pred = apply_classifier(pred, modelc, img, im0s) # Process predictions for i, det in enumerate(pred): # per image seen += 1 if webcam: # batch_size >= 1 p, s, im0, frame = ( path[i], f"{i}: ", im0s[i].copy(), dataset.count, ) else: p, s, im0, frame = ( path, "", im0s.copy(), getattr(dataset, "frame", 0), ) p = Path(p) # to Path save_path = str(save_dir / p.name) # img.jpg txt_path = str(save_dir / "labels" / p.stem) + ( "" if dataset.mode == "image" else f"_{frame}" ) # img.txt s += "%gx%g " % img.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, pil=not ascii ) if len(det): # Rescale boxes from img_size to im0 size det[:, :4] = scale_coords( img.shape[2:], det[:, :4], im0.shape ).round() results.append((im0, det)) # Print results for c in det[:, -1].unique(): n = (det[:, -1] == c).sum() # detections per class s += f"{n} {names[int(c)]}{'s' * (n > 1)}, " # add to string # Write results for *xyxy, conf, cls in reversed(det): 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(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)) if save_crop: save_one_box( xyxy, imc, file=save_dir / "crops" / names[c] / f"{p.stem}.jpg", BGR=True, ) # Print time (inference-only) print(f"{s}Done. ({t3 - t2:.3f}s)") # Stream results im0 = annotator.result() if view_img: 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 += ".mp4" vid_writer[i] = cv2.VideoWriter( save_path, cv2.VideoWriter_fourcc(*"mp4v"), fps, (w, h), ) vid_writer[i].write(im0) # Print results t = tuple(x / seen * 1e3 for x in dt) # speeds per image print( f"Speed: %.1fms pre-process, %.1fms inference, %.1fms NMS per image at shape {(1, 3, *imgsz)}" % t ) return results # 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 '' # print(f"Results saved to {colorstr('bold', save_dir)}{s}") # if update: # strip_optimizer(weights) # update model (to fix SourceChangeWarning)