341 lines
15 KiB
Python
341 lines
15 KiB
Python
# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
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"""
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Run inference on images, videos, directories, streams, etc.
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Usage:
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$ python path/to/detect.py --source path/to/img.jpg --weights yolov5s.pt --img 640
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"""
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import argparse
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import os
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import sys
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from pathlib import Path
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import cv2
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import numpy as np
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import torch
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import torch.backends.cudnn as cudnn
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FILE = Path(__file__).resolve()
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ROOT = FILE.parents[0] # YOLOv5 root directory
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if str(ROOT) not in sys.path:
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sys.path.append(str(ROOT)) # add ROOT to PATH
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ROOT = Path(os.path.relpath(ROOT, Path.cwd())) # relative
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from models.experimental import attempt_load
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from utils.datasets import LoadImages, LoadStreams
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from utils.general import (
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apply_classifier,
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check_img_size,
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check_imshow,
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check_requirements,
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check_suffix,
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colorstr,
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increment_path,
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non_max_suppression,
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print_args,
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save_one_box,
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scale_coords,
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set_logging,
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strip_optimizer,
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xyxy2xywh,
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)
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from utils.plots import Annotator, colors
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from utils.torch_utils import load_classifier, select_device, time_sync
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@torch.no_grad()
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def run(
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weights=ROOT / "yolov5s.pt", # model.pt path(s)
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source=ROOT / "data/images", # file/dir/URL/glob, 0 for webcam
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imgsz=640, # inference size (pixels)
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conf_thres=0.25, # confidence threshold
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iou_thres=0.45, # NMS IOU threshold
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max_det=1000, # maximum detections per image
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device="", # cuda device, i.e. 0 or 0,1,2,3 or cpu
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view_img=False, # show results
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save_txt=False, # save results to *.txt
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save_conf=False, # save confidences in --save-txt labels
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save_crop=False, # save cropped prediction boxes
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nosave=False, # do not save images/videos
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classes=None, # filter by class: --class 0, or --class 0 2 3
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agnostic_nms=False, # class-agnostic NMS
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augment=False, # augmented inference
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visualize=False, # visualize features
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update=False, # update all models
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project=ROOT / "runs/detect", # save results to project/name
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name="exp", # save results to project/name
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exist_ok=False, # existing project/name ok, do not increment
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line_thickness=3, # bounding box thickness (pixels)
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hide_labels=False, # hide labels
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hide_conf=False, # hide confidences
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half=False, # use FP16 half-precision inference
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dnn=False, # use OpenCV DNN for ONNX inference
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):
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source = str(source)
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save_img = not nosave and not source.endswith(".txt") # save inference images
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webcam = (
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source.isnumeric()
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or source.endswith(".txt")
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or source.lower().startswith(("rtsp://", "rtmp://", "http://", "https://"))
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)
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# Directories
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save_dir = increment_path(Path(project) / name, exist_ok=exist_ok) # increment run
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(save_dir / "labels" if save_txt else save_dir).mkdir(parents=True, exist_ok=True) # make dir
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# Initialize
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set_logging()
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device = select_device(device)
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half &= device.type != "cpu" # half precision only supported on CUDA
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# Load model
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w = str(weights[0] if isinstance(weights, list) else weights)
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classify, suffix, suffixes = False, Path(w).suffix.lower(), [".pt", ".onnx", ".tflite", ".pb", ""]
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check_suffix(w, suffixes) # check weights have acceptable suffix
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pt, onnx, tflite, pb, saved_model = (suffix == x for x in suffixes) # backend booleans
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stride, names = 64, [f"class{i}" for i in range(1000)] # assign defaults
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if pt:
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model = torch.jit.load(w) if "torchscript" in w else attempt_load(weights, map_location=device)
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stride = int(model.stride.max()) # model stride
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names = model.module.names if hasattr(model, "module") else model.names # get class names
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if half:
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model.half() # to FP16
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if classify: # second-stage classifier
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modelc = load_classifier(name="resnet50", n=2) # initialize
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modelc.load_state_dict(torch.load("resnet50.pt", map_location=device)["model"]).to(device).eval()
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elif onnx:
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if dnn:
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# check_requirements(('opencv-python>=4.5.4',))
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net = cv2.dnn.readNetFromONNX(w)
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else:
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check_requirements(("onnx", "onnxruntime"))
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import onnxruntime
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session = onnxruntime.InferenceSession(w, None)
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else: # TensorFlow models
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check_requirements(("tensorflow>=2.4.1",))
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import tensorflow as tf
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if pb: # https://www.tensorflow.org/guide/migrate#a_graphpb_or_graphpbtxt
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def wrap_frozen_graph(gd, inputs, outputs):
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x = tf.compat.v1.wrap_function(lambda: tf.compat.v1.import_graph_def(gd, name=""), []) # wrapped import
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return x.prune(
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tf.nest.map_structure(x.graph.as_graph_element, inputs),
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tf.nest.map_structure(x.graph.as_graph_element, outputs),
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)
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graph_def = tf.Graph().as_graph_def()
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graph_def.ParseFromString(open(w, "rb").read())
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frozen_func = wrap_frozen_graph(gd=graph_def, inputs="x:0", outputs="Identity:0")
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elif saved_model:
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model = tf.keras.models.load_model(w)
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elif tflite:
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interpreter = tf.lite.Interpreter(model_path=w) # load TFLite model
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interpreter.allocate_tensors() # allocate
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input_details = interpreter.get_input_details() # inputs
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output_details = interpreter.get_output_details() # outputs
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int8 = input_details[0]["dtype"] == np.uint8 # is TFLite quantized uint8 model
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imgsz = check_img_size(imgsz, s=stride) # check image size
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# Dataloader
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if webcam:
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view_img = check_imshow()
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cudnn.benchmark = True # set True to speed up constant image size inference
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dataset = LoadStreams(source, img_size=imgsz, stride=stride, auto=pt)
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bs = len(dataset) # batch_size
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else:
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dataset = LoadImages(source, img_size=imgsz, stride=stride, auto=pt)
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bs = 1 # batch_size
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vid_path, vid_writer = [None] * bs, [None] * bs
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# Run inference
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if pt and device.type != "cpu":
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model(torch.zeros(1, 3, *imgsz).to(device).type_as(next(model.parameters()))) # run once
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dt, seen = [0.0, 0.0, 0.0], 0
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for path, img, im0s, vid_cap in dataset:
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t1 = time_sync()
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if onnx:
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img = img.astype("float32")
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else:
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img = torch.from_numpy(img).to(device)
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img = img.half() if half else img.float() # uint8 to fp16/32
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img = img / 255.0 # 0 - 255 to 0.0 - 1.0
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if len(img.shape) == 3:
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img = img[None] # expand for batch dim
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t2 = time_sync()
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dt[0] += t2 - t1
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# Inference
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if pt:
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visualize = increment_path(save_dir / Path(path).stem, mkdir=True) if visualize else False
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pred = model(img, augment=augment, visualize=visualize)[0]
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elif onnx:
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if dnn:
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net.setInput(img)
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pred = torch.tensor(net.forward())
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else:
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pred = torch.tensor(session.run([session.get_outputs()[0].name], {session.get_inputs()[0].name: img}))
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else: # tensorflow model (tflite, pb, saved_model)
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imn = img.permute(0, 2, 3, 1).cpu().numpy() # image in numpy
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if pb:
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pred = frozen_func(x=tf.constant(imn)).numpy()
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elif saved_model:
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pred = model(imn, training=False).numpy()
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elif tflite:
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if int8:
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scale, zero_point = input_details[0]["quantization"]
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imn = (imn / scale + zero_point).astype(np.uint8) # de-scale
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interpreter.set_tensor(input_details[0]["index"], imn)
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interpreter.invoke()
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pred = interpreter.get_tensor(output_details[0]["index"])
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if int8:
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scale, zero_point = output_details[0]["quantization"]
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pred = (pred.astype(np.float32) - zero_point) * scale # re-scale
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pred[..., 0] *= imgsz[1] # x
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pred[..., 1] *= imgsz[0] # y
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pred[..., 2] *= imgsz[1] # w
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pred[..., 3] *= imgsz[0] # h
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pred = torch.tensor(pred)
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t3 = time_sync()
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dt[1] += t3 - t2
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# NMS
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pred = non_max_suppression(pred, conf_thres, iou_thres, classes, agnostic_nms, max_det=max_det)
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dt[2] += time_sync() - t3
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# Second-stage classifier (optional)
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if classify:
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pred = apply_classifier(pred, modelc, img, im0s)
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# Process predictions
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for i, det in enumerate(pred): # per image
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seen += 1
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if webcam: # batch_size >= 1
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p, s, im0, frame = path[i], f"{i}: ", im0s[i].copy(), dataset.count
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else:
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p, s, im0, frame = path, "", im0s.copy(), getattr(dataset, "frame", 0)
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p = Path(p) # to Path
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save_path = str(save_dir / p.name) # img.jpg
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txt_path = str(save_dir / "labels" / p.stem) + ("" if dataset.mode == "image" else f"_{frame}") # img.txt
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s += "{:g}x{:g} ".format(*img.shape[2:]) # print string
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gn = torch.tensor(im0.shape)[[1, 0, 1, 0]] # normalization gain whwh
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imc = im0.copy() if save_crop else im0 # for save_crop
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annotator = Annotator(im0, line_width=line_thickness, example=str(names))
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if len(det):
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# Rescale boxes from img_size to im0 size
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det[:, :4] = scale_coords(img.shape[2:], det[:, :4], im0.shape).round()
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# Print results
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for c in det[:, -1].unique():
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n = (det[:, -1] == c).sum() # detections per class
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s += f"{n} {names[int(c)]}{'s' * (n > 1)}, " # add to string
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# Write results
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for *xyxy, conf, cls in reversed(det):
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if save_txt: # Write to file
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xywh = (xyxy2xywh(torch.tensor(xyxy).view(1, 4)) / gn).view(-1).tolist() # normalized xywh
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line = (cls, *xywh, conf) if save_conf else (cls, *xywh) # label format
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with open(txt_path + ".txt", "a") as f:
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f.write(("%g " * len(line)).rstrip() % line + "\n")
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if save_img or save_crop or view_img: # Add bbox to image
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c = int(cls) # integer class
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label = None if hide_labels else (names[c] if hide_conf else f"{names[c]} {conf:.2f}")
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annotator.box_label(xyxy, label, color=colors(c, True))
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if save_crop:
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save_one_box(xyxy, imc, file=save_dir / "crops" / names[c] / f"{p.stem}.jpg", BGR=True)
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# Print time (inference-only)
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print(f"{s}Done. ({t3 - t2:.3f}s)")
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# Stream results
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im0 = annotator.result()
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if view_img:
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cv2.imshow(str(p), im0)
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cv2.waitKey(1) # 1 millisecond
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# Save results (image with detections)
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if save_img:
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if dataset.mode == "image":
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cv2.imwrite(save_path, im0)
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else: # 'video' or 'stream'
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if vid_path[i] != save_path: # new video
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vid_path[i] = save_path
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if isinstance(vid_writer[i], cv2.VideoWriter):
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vid_writer[i].release() # release previous video writer
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if vid_cap: # video
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fps = vid_cap.get(cv2.CAP_PROP_FPS)
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w = int(vid_cap.get(cv2.CAP_PROP_FRAME_WIDTH))
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h = int(vid_cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
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else: # stream
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fps, w, h = 30, im0.shape[1], im0.shape[0]
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save_path += ".mp4"
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vid_writer[i] = cv2.VideoWriter(save_path, cv2.VideoWriter_fourcc(*"mp4v"), fps, (w, h))
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vid_writer[i].write(im0)
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# Print results
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t = tuple(x / seen * 1e3 for x in dt) # speeds per image
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print(f"Speed: %.1fms pre-process, %.1fms inference, %.1fms NMS per image at shape {(1, 3, *imgsz)}" % t)
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if save_txt or save_img:
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s = f"\n{len(list(save_dir.glob('labels/*.txt')))} labels saved to {save_dir / 'labels'}" if save_txt else ""
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print(f"Results saved to {colorstr('bold', save_dir)}{s}")
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if update:
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strip_optimizer(weights) # update model (to fix SourceChangeWarning)
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def parse_opt():
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parser = argparse.ArgumentParser()
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parser.add_argument(
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"--weights",
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nargs="+",
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type=str,
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default=r"D:\lzy\yolov5\yolov5\runs\train\exp15\weights\best.pt",
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help="model path(s)",
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)
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parser.add_argument(
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"--source",
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type=str,
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default=r"D:\lzy\yolov5\yolov5\data_org\yolo_dataset\test\images",
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help="file/dir/URL/glob, 0 for webcam",
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)
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parser.add_argument("--imgsz", "--img", "--img-size", nargs="+", type=int, default=[640], help="inference size h,w")
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parser.add_argument("--conf-thres", type=float, default=0.25, help="confidence threshold")
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parser.add_argument("--iou-thres", type=float, default=0.45, help="NMS IoU threshold")
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parser.add_argument("--max-det", type=int, default=1000, help="maximum detections per image")
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parser.add_argument("--device", default="", help="cuda device, i.e. 0 or 0,1,2,3 or cpu")
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parser.add_argument("--view-img", action="store_true", help="show results")
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parser.add_argument("--save-txt", action="store_true", help="save results to *.txt")
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parser.add_argument("--save-conf", action="store_true", help="save confidences in --save-txt labels")
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parser.add_argument("--save-crop", action="store_true", help="save cropped prediction boxes")
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parser.add_argument("--nosave", action="store_true", help="do not save images/videos")
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parser.add_argument("--classes", nargs="+", type=int, help="filter by class: --classes 0, or --classes 0 2 3")
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parser.add_argument("--agnostic-nms", action="store_true", help="class-agnostic NMS")
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parser.add_argument("--augment", action="store_true", help="augmented inference")
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parser.add_argument("--visualize", action="store_true", help="visualize features")
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parser.add_argument("--update", action="store_true", help="update all models")
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parser.add_argument("--project", default=ROOT / "runs/detect", help="save results to project/name")
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parser.add_argument("--name", default="exp", help="save results to project/name")
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parser.add_argument("--exist-ok", action="store_true", help="existing project/name ok, do not increment")
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parser.add_argument("--line-thickness", default=3, type=int, help="bounding box thickness (pixels)")
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parser.add_argument("--hide-labels", default=False, action="store_true", help="hide labels")
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parser.add_argument("--hide-conf", default=False, action="store_true", help="hide confidences")
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parser.add_argument("--half", action="store_true", help="use FP16 half-precision inference")
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parser.add_argument("--dnn", action="store_true", help="use OpenCV DNN for ONNX inference")
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opt = parser.parse_args()
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opt.imgsz *= 2 if len(opt.imgsz) == 1 else 1 # expand
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print_args(FILE.stem, opt)
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return opt
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def main(opt):
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check_requirements(exclude=("tensorboard", "thop"))
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run(**vars(opt))
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if __name__ == "__main__":
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opt = parse_opt()
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main(opt)
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