91 lines
3.5 KiB
Python
91 lines
3.5 KiB
Python
import time
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from pathlib import Path
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import cv2
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import torch
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from PIL import ImageGrab
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import numpy as np
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import os
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import glob
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from models.common import DetectMultiBackend
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from utils.general import non_max_suppression, scale_boxes
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from utils.plots import Annotator, colors
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from utils.augmentations import letterbox
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from utils.torch_utils import select_device
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FILE = Path(__file__).resolve()
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folder_path = R'C:\Users\lengdan\Desktop\yolov5-master\data\images' # 本地文件夹
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camera = 2 # 0调用本地相机, 1检测文件夹中的图像, 2检测屏幕上内容
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class mydataload:
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def __init__(self):
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self.count = 0
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if camera == 0:
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self.cap = cv2.VideoCapture(0)
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def __iter__(self):
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return self
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def __next__(self):
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if camera == 0:
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_, im0 = self.cap.read()
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elif camera == 1:
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file_list = glob.glob(os.path.join(folder_path, '*.jpg'))
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# 对文件列表按时间戳排序，以确保最新添加的图像排在最后面
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file_list.sort(key=os.path.getmtime)
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im0 = cv2.imread(file_list[self.count])
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self.count = self.count if self.count == len(file_list) - 1 else self.count + 1
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else: # camera 检测屏幕上的内容
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# 指定截图区域的左上角和右下角坐标
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x1, y1 = 1000, 100 # 左上角
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x2, y2 = 1900, 1000 # 右下角
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# 截取屏幕区域
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img = ImageGrab.grab(bbox=(x1, y1, x2, y2))
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im0 = np.array(img)
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im = letterbox(im0, 640, auto=True)[0] # padded resize
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im = im.transpose((2, 0, 1)) # HWC to CHW, BGR to RGB
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if camera != 2:
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im = im[::-1]
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im = np.ascontiguousarray(im) # contiguous
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return im, im0
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def get_image(model, im, im0s, conf_thres=0.5, iou_thres=0.5, line_thickness=3):
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# temp_list = []
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pred = model(im, visualize=False)
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pred = non_max_suppression(pred, conf_thres, iou_thres, None, False, max_det=1000)
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for i, det in enumerate(pred):
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im0, names = im0s.copy(), model.names
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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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det[:, :4] = scale_boxes(im.shape[2:], det[:, :4], im0.shape).round()
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for *xyxy, conf, cls in reversed(det):
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c = int(cls)
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# temp_list.append((xyxy[0].item(), xyxy[1].item(), xyxy[2].item(), xyxy[3].item()))
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label = f'{names[c]} {conf:.2f}'
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annotator.box_label(xyxy, label, color=colors(c, True))
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return annotator.result()
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if __name__ == "__main__":
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device = select_device('0')
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model = DetectMultiBackend('7_29_last.pt', device=device, dnn=False, data='', fp16=False)
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dataset = mydataload()
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model.warmup(imgsz=(1, 3, 640, 640)) # warmup
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for im, im0s in dataset:
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t0 = time.time()
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im = torch.from_numpy(im).to(model.device)
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im = im.half() if model.fp16 else im.float() # uint8 to fp16/32
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im /= 255 # 0 - 255 to 0.0 - 1.0
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if len(im.shape) == 3:
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im = im[None] # expand for batch dim
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im0 = get_image(model, im, im0s)
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if camera == 2:
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im0 = cv2.cvtColor(im0, cv2.COLOR_BGR2RGB)
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cv2.namedWindow('1', cv2.WINDOW_NORMAL | cv2.WINDOW_KEEPRATIO) # allow window resize (Linux)
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cv2.resizeWindow('1', im0.shape[1] // 2, im0.shape[0] // 2)
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cv2.imshow('1', im0)
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if cv2.waitKey(1) == ord('Q'):
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exit(0)
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print(time.time() - t0) |