YOLOV5實時檢測螢幕
2023-06-01 15:00:24
YOLOV5實時檢測螢幕
目錄
注：此為筆記
目的：保留模型載入和推理部分，完成實時螢幕檢測
實現思路：
1. 寫一個實時擷取螢幕的函數
2. 將擷取的螢幕在視窗顯示出來
3. 用OpenCV繪製一個視窗用來顯示擷取的螢幕
4. 在detect找出推理的程式碼，推理完成後得到中心點的xy座標，寬高組成box
5. 在建立的OpenCV視窗用得到的推理結果繪製方框
實現效果：
思考部分
先把原本的detect.py的程式碼貼在這裡
import argparse
import os
import platform
import sys
from pathlib import Path
import torch
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 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)
from utils.plots import Annotator, colors, save_one_box
from utils.torch_utils import select_device, smart_inference_mode
@smart_inference_mode()
def run(
weights=ROOT / 'yolov5s.pt', # model path or triton URL
source=ROOT / 'data/video/',
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_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
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=pt, 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(), Profile(), Profile())
for path, im, im0s, vid_cap, s 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
# Inference
with dt[1]:
visualize = increment_path(save_dir / Path(path).stem, mkdir=True) if visualize else False
pred = 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)
# Second-stage classifier (optional)
# pred = utils.general.apply_classifier(pred, classifier_model, im, im0s)
# 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 *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(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))
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 / 'yolov5s.pt', help='model path or triton URL')
parser.add_argument('--source', type=str, default=ROOT / '0', help='file/dir/URL/glob/screen/1(webcam)')
parser.add_argument('--data', type=str, default=ROOT / 'data/coco128.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=0.45, help='confidence threshold')
parser.add_argument('--iou-thres', type=float, default=0.2, 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: --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(exclude=('tensorboard', 'thop'))
run(**vars(opt))
if __name__ == '__main__':
opt = parse_opt()
main(opt)
分析程式碼並刪減不用的部分
import argparse
import os
import platform
import sys
from pathlib import Path
import torch
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 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)
from utils.plots import Annotator, colors, save_one_box
from utils.torch_utils import select_device, smart_inference_mode
做了一些包的匯入，定義了一些全域性變數，先保留下來，沒用的最後刪
向下
if __name__ == '__main__':
opt = parse_opt()
main(opt)
從if __name__ == '__main__開始
opt = parse_opt 就是一個獲取命令列引數的函數，我們並不需要,可以刪
進入main函數
def main(opt):
check_requirements(exclude=('tensorboard', 'thop'))
run(**vars(opt))
check_requirements函數檢查requirements是否全都安裝好了，無用，刪了
進入run函數
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
判斷source的型別，即要要推理的源是什麼，判斷源是檔案還是url還是webcam或者screenshot ，定義儲存資料夾，我不需要儲存，只需要實時檢測螢幕，刪除
繼續向下，是載入模型的程式碼
# Load model
device = select_device(device)
model = DetectMultiBackend(weights, device=device, dnn=dnn, data=data, fp16=half)
得知載入模型需要幾個引數，分別是weights, device=device, dnn=dnn, data=data, fp16=half
通過開始的形參可知：
weights=ROOT / 'yolov5s.pt'也就是模型的名稱device通過select_device函數得到dnn和fp16在run函數裡的引數都是FALSE
故載入模型的程式碼可以改寫成
def LoadModule():
device = select_device('')
weights = 'yolov5s.pt'
model = DetectMultiBackend(weights, device=device, dnn=False, fp16=False)
return model
繼續往下讀
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=pt, vid_stride=vid_stride)
vid_path, vid_writer = [None] * bs, [None] * bs
這裡如果是使用網路攝像頭作為輸入，會通過LoadStreams類載入視訊流，根據影象大小和步長取樣，如果使用截圖作為輸入，則通過LoadScreenshots載入截圖，都不是則通過LoadImages類載入圖片檔案
這是YOLOV5提供的載入dataset的部分，我們可以新增自己的dataset，所以刪掉
繼續往下
# Run inference
model.warmup(imgsz=(1 if pt or model.triton else bs, 3, *imgsz)) # warmup
seen, windows, dt = 0, [], (Profile(), Profile(), Profile())
for path, im, im0s, vid_cap, s 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
# Inference
with dt[1]:
visualize = increment_path(save_dir / Path(path).stem, mkdir=True) if visualize else False
pred = 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)
# Second-stage classifier (optional)
# pred = utils.general.apply_classifier(pred, classifier_model, im, im0s)
# Process predictions
model.warmup(imgsz=(1 if pt or model.triton else bs, 3, *imgsz))
用於模型預熱，傳入形狀為(1, 3, *imgsz)的影象進行預熱操作，沒用刪了
seen, windows, dt = 0, [], (Profile(), Profile(), Profile())
未知作用，刪了
for path, im, im0s, vid_cap, s 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
# Inference
with dt[1]:
visualize = increment_path(save_dir / Path(path).stem, mkdir=True) if visualize else False
pred = 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)
上面這段for迴圈用於遍歷資料集中的每個影象或視訊幀進行推理，在迴圈的開頭，將路徑、影象、原始影象、視訊捕獲物件和步長傳遞給path, im, im0s, vid_cap, s。推理實時螢幕只需要傳一張圖片，所以不存在將遍歷推理，所以要進行改寫，改寫成
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
visualize = increment_path(save_dir / Path(path).stem, mkdir=True) if visualize else False
pred = model(im, augment=augment, visualize=visualize)
pred = non_max_suppression(pred, conf_thres, iou_thres, classes, agnostic_nms, max_det=max_det)
這裡是對 im 進行轉換和推理，而改寫的程式碼中沒有im變數，則尋找im的來源
for path, im, im0s, vid_cap, s in dataset:
im來源於dataset
dataset = LoadImages(source, img_size=imgsz, stride=stride, auto=pt, vid_stride=vid_stride)
dataset來源於LoadImages的返回值
檢視LoadImages的函數返回值和返回值的來源
在dataloaders.py中可以看到
if self.transforms:
im = self.transforms(im0) # transforms
else:
im = letterbox(im0, self.img_size, stride=self.stride, auto=self.auto)[0] # padded resize
im = im.transpose((2, 0, 1))[::-1] # HWC to CHW, BGR to RGB
im = np.ascontiguousarray(im) # contiguous
return path, im, im0, self.cap, s
如果transforms存在，則轉換，如果transforms不存在，則呼叫letterbox函數對影象im0進行縮放和填充，使其符合模型要求的影象大小，將影象的通道順序由HWC轉換為CHW，將影象的通道順序由BGR轉換為RGB，將影象轉換為連續的記憶體佈局
其中需要的引數是im0, self.img_size, stride=self.stride, auto=self.auto
im0則是未經處理的圖片，img_size填640（因為模型的圖片大小訓練的是640），stride填64（預設引數為64），auto填True
則得到改寫程式碼為
im = letterbox(img0, 640, stride=32, auto=True)[0] # padded resize
im = im.transpose((2, 0, 1))[::-1] # HWC to CHW, BGR to RGB
im = np.ascontiguousarray(im) # contiguous
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
pred = model(im, augment=False, visualize=False)
pred = non_max_suppression(pred, conf_thres=conf_thres, iou_thres=iou_thres, classes=None, agnostic=False,
max_det=1000)
繼續向下
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 *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(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))
if save_crop:
save_one_box(xyxy, imc, file=save_dir / 'crops' / names[c] / f'{p.stem}.jpg', BGR=True)
這段程式碼將推理後的結果進行轉換，轉換為label format，成為人能看懂的格式，刪去輸出結果，留下寫入結果中的，格式轉換，刪掉儲存為txt檔案，得到需要的box，然後自己寫一個boxs=[]，將結果append進去，方便在OpenCV中繪畫識別方框，改寫結果為
boxs=[]
for i, det in enumerate(pred): # per image
im0 = img0.copy()
s = ' '
s += '%gx%g ' % im.shape[2:] # print string
gn = torch.tensor(img0.shape)[[1, 0, 1, 0]] # normalization gain whwh
imc = img0 # for save_crop
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 *xyxy, conf, cls in reversed(det):
xywh = (xyxy2xywh(torch.tensor(xyxy).view(1, 4)) / gn).view(-1).tolist() # normalized xywh
line = (cls, *xywh) # label format
box = ('%g ' * len(line)).rstrip() % line
box = box.split(' ')
boxs.append(box)
就此完成了推理部分的刪減和重寫
把螢幕的截圖通過OpenCV進行顯示
寫一個螢幕截圖的檔案
寫成 grabscreen.py
# 檔名:grabscreen.py
import cv2
import numpy as np
import win32gui
import win32print
import win32ui
import win32con
import win32api
import mss
def grab_screen_win32(region):
hwin = win32gui.GetDesktopWindow()
left, top, x2, y2 = region
width = x2 - left + 1
height = y2 - top + 1
hwindc = win32gui.GetWindowDC(hwin)
srcdc = win32ui.CreateDCFromHandle(hwindc)
memdc = srcdc.CreateCompatibleDC()
bmp = win32ui.CreateBitmap()
bmp.CreateCompatibleBitmap(srcdc, width, height)
memdc.SelectObject(bmp)
memdc.BitBlt((0, 0), (width, height), srcdc, (left, top), win32con.SRCCOPY)
signedIntsArray = bmp.GetBitmapBits(True)
img = np.fromstring(signedIntsArray, dtype='uint8')
img.shape = (height, width, 4)
srcdc.DeleteDC()
memdc.DeleteDC()
win32gui.ReleaseDC(hwin, hwindc)
win32gui.DeleteObject(bmp.GetHandle())
return cv2.cvtColor(img, cv2.COLOR_BGRA2BGR)
通過img0 = grab_screen_win32(region=(0, 0, 1920, 1080))來作為im的引數傳入，即可讓螢幕截圖作為推理圖片
用OpenCV繪製視窗並顯示
if len(boxs):
for i, det in enumerate(boxs):
_, x_center, y_center, width, height = det
x_center, width = re_x * float(x_center), re_x * float(width)
y_center, height = re_y * float(y_center), re_y * float(height)
top_left = (int(x_center - width / 2.), int(y_center - height / 2.))
bottom_right = (int(x_center + width / 2.), int(y_center + height / 2.))
color = (0, 0, 255) # RGB
cv2.rectangle(img0, top_left, bottom_right, color, thickness=thickness)
和
cv2.namedWindow('windows', cv2.WINDOW_NORMAL)
cv2.resizeWindow('windows', re_x // 2, re_y // 2)
cv2.imshow('windows', img0)
HWND = win32gui.FindWindow(None, "windows")
win32gui.SetWindowPos(HWND, win32con.HWND_TOPMOST, 0, 0, 0, 0, win32con.SWP_NOMOVE | win32con.SWP_NOSIZE)
結合在一起
最終程式碼
import torch, pynput
import numpy as np
import win32gui, win32con, cv2
from grabscreen import grab_screen_win32 # 本地檔案
from utils.augmentations import letterbox
from models.common import DetectMultiBackend
from utils.torch_utils import select_device
from utils.general import non_max_suppression, scale_boxes, xyxy2xywh
# 可調引數
conf_thres = 0.25
iou_thres = 0.05
thickness = 2
x, y = (1920, 1080)
re_x, re_y = (1920, 1080)
def LoadModule():
device = select_device('')
weights = 'yolov5s.pt'
model = DetectMultiBackend(weights, device=device, dnn=False, fp16=False)
return model
model = LoadModule()
while True:
names = model.names
img0 = grab_screen_win32(region=(0, 0, 1920, 1080))
im = letterbox(img0, 640, stride=32, auto=True)[0] # padded resize
im = im.transpose((2, 0, 1))[::-1] # HWC to CHW, BGR to RGB
im = np.ascontiguousarray(im) # contiguous
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
pred = model(im, augment=False, visualize=False)
pred = non_max_suppression(pred, conf_thres=conf_thres, iou_thres=iou_thres, classes=None, agnostic=False,
max_det=1000)
boxs=[]
for i, det in enumerate(pred): # per image
im0 = img0.copy()
s = ' '
s += '%gx%g ' % im.shape[2:] # print string
gn = torch.tensor(img0.shape)[[1, 0, 1, 0]] # normalization gain whwh
imc = img0 # for save_crop
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 *xyxy, conf, cls in reversed(det):
xywh = (xyxy2xywh(torch.tensor(xyxy).view(1, 4)) / gn).view(-1).tolist() # normalized xywh
line = (cls, *xywh) # label format
box = ('%g ' * len(line)).rstrip() % line
box = box.split(' ')
boxs.append(box)
if len(boxs):
for i, det in enumerate(boxs):
_, x_center, y_center, width, height = det
x_center, width = re_x * float(x_center), re_x * float(width)
y_center, height = re_y * float(y_center), re_y * float(height)
top_left = (int(x_center - width / 2.), int(y_center - height / 2.))
bottom_right = (int(x_center + width / 2.), int(y_center + height / 2.))
color = (0, 0, 255) # RGB
cv2.rectangle(img0, top_left, bottom_right, color, thickness=thickness)
if cv2.waitKey(1) & 0xFF == ord('q'):
cv2.destroyWindow()
break
cv2.namedWindow('windows', cv2.WINDOW_NORMAL)
cv2.resizeWindow('windows', re_x // 2, re_y // 2)
cv2.imshow('windows', img0)
HWND = win32gui.FindWindow(None, "windows")
win32gui.SetWindowPos(HWND, win32con.HWND_TOPMOST, 0, 0, 0, 0, win32con.SWP_NOMOVE | win32con.SWP_NOSIZE)
End.