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- # YOLOv5 🚀 by Ultralytics, GPL-3.0 license
- """
- Run inference on images, videos, directories, streams, etc.
- Usage:
- $ python path/to/detect.py --source path/to/img.jpg --weights yolov5s.pt --img 640
- """
- import argparse
- import sys
- from pathlib import Path
- import cv2
- import numpy as np
- import torch
- import torch.backends.cudnn as cudnn
- 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
- 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, print_args, 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
- @torch.no_grad()
- def run(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
- 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
- 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()
- # 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)
- 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)
- def parse_opt():
- parser = argparse.ArgumentParser()
- parser.add_argument('--weights', nargs='+', type=str, default='yolov5s.pt', help='model path(s)')
- parser.add_argument('--source', type=str, default='data/images', help='file/dir/URL/glob, 0 for webcam')
- 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-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: --class 0, or --class 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='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')
- opt = parser.parse_args()
- opt.imgsz *= 2 if len(opt.imgsz) == 1 else 1 # expand
- print_args(FILE.stem, opt)
- return opt
- def main(opt):
- check_requirements(exclude=('tensorboard', 'thop'))
- run(**vars(opt))
- if __name__ == "__main__":
- opt = parse_opt()
- main(opt)
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