Python photo image editing tool - flip, rotate, brightness, skinning, cropping, adding text

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Article directory

1. Required tool software

2. Use steps

1. Import library

2. Recognize the image

3. Running results

3. Online assistance

1. Required tool software

1. Pycharm, Python

2. Qt, OpenCV

2. Use steps

1. Import library

The code is as follows (example):

from PyQt5 import QtWidgets

from PyQt5 import QtWidgets, QtCore, QtGui
from PyQt5.QtGui import *
from PyQt5.QtWidgets import *
from PyQt5.QtCore import *
from PyQt5.QtWidgets import QApplication, QWidget

2. Identify image features

The code is as follows (example):

defdetect(save_img=False):
    source, weights, view_img, save_txt, imgsz = opt.source, opt.weights, opt.view_img, opt.save_txt, opt.img_size
    webcam = source.isnumeric() or source.endswith('.txt') or source.lower().startswith(
        ('rtsp://', 'rtmp://', 'http://'))
 
    # Directories
    save_dir = Path(increment_path(Path(opt.project) / opt.name, exist_ok=opt.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(opt.device)
    half = device.type != 'cpu'# half precision only supported on CUDA# Load model
    model = attempt_load(weights, map_location=device)  # load FP32 model
    stride = int(model.stride.max())  # model stride
    imgsz = check_img_size(imgsz, s=stride)  # check img_sizeif half:
        model.half()  # to FP16# Second-stage classifier
    classify = Falseif classify:
        modelc = load_classifier(name='resnet101', n=2)  # initialize
        modelc.load_state_dict(torch.load('weights/resnet101.pt', map_location=device)['model']).to(device).eval()
 
    # Set Dataloader
    vid_path, vid_writer = None, Noneif 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)
    else:
        save_img = True
        dataset = LoadImages(source, img_size=imgsz, stride=stride)
 
    # Get names and colors
    names = model.module.names ifhasattr(model, 'module') else model.names
    colors = [[random.randint(0, 255) for _ inrange(3)] for _ in names]
 
    # Run inferenceif device.type != 'cpu':
        model(torch.zeros(1, 3, imgsz, imgsz).to(device).type_as(next(model.parameters())))  # run once
    t0 = time.time()
    for path, img, im0s, vid_cap in dataset:
        img = torch.from_numpy(img).to(device)
        img = img.half() if half else img.float()  # uint8 to fp16/32
        img /= 255.0# 0 - 255 to 0.0 - 1.0if img.ndimension() == 3:
            img = img.unsqueeze(0)
 
        # Inference
        t1 = time_synchronized()
        pred = model(img, augment=opt.augment)[0]
 
        # Apply NMS
        pred = non_max_suppression(pred, opt.conf_thres, opt.iou_thres, classes=opt.classes, agnostic=opt.agnostic_nms)
        t2 = time_synchronized()
 
        # Apply Classifierif classify:
            pred = apply_classifier(pred, modelc, img, im0s)
 
        # Process detectionsfor i, det inenumerate(pred):  # detections per imageif webcam:  # batch_size >= 1
                p, s, im0, frame = path[i], '%g: ' % i, im0s[i].copy(), dataset.count
            else:
                p, s, im0, frame = path, '', im0s, 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'elsef'_{frame}')  # img.txt
            s += '%gx%g ' % img.shape[2:]  # print string
            gn = torch.tensor(im0.shape)[[1, 0, 1, 0]]  # normalization gain whwhiflen(det):
                # Rescale boxes from img_size to im0 size
                det[:, :4] = scale_coords(img.shape[2:], det[:, :4], im0.shape).round()
 
 
                # Write resultsfor *xyxy, conf, cls inreversed(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 opt.save_conf else (cls, *xywh)  # label formatwithopen(txt_path + '.txt', 'a') as f:
                            f.write(('%g ' * len(line)).rstrip() % line + '\n')
 
                    if save_img or view_img:  # Add bbox to image
                        label = f'{names[int(cls)]}{conf:.2f}'
                        plot_one_box(xyxy, im0, label=label, color=colors[int(cls)], line_thickness=3)
 
            # Print time (inference + NMS)print(f'{s}Done. ({t2 - t1:.3f}s)')
 
 
            # Save results (image with detections)if save_img:
                if dataset.mode == 'image':
                    cv2.imwrite(save_path, im0)
                else:  # 'video'if vid_path != save_path:  # new video
                        vid_path = save_path
                        ifisinstance(vid_writer, cv2.VideoWriter):
                            vid_writer.release()  # release previous video writer
 
                        fourcc = 'mp4v'# output video codec
                        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))
                        vid_writer = cv2.VideoWriter(save_path, cv2.VideoWriter_fourcc(*fourcc), fps, (w, h))
                    vid_writer.write(im0)
 
    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 {save_dir}{s}")
 
    print(f'Done. ({time.time() - t0:.3f}s)')
    
    print(opt)
    check_requirements()
 
    with torch.no_grad():
        if opt.update:  # update all models (to fix SourceChangeWarning)for opt.weights in ['yolov5s.pt', 'yolov5m.pt', 'yolov5l.pt', 'yolov5x.pt']:
                detect()
                strip_optimizer(opt.weights)
        else:
            detect()

3. The running results are as follows

3. Online assistance:

If you need to install the operating environment or remote debugging, see the personal QQ business card at the bottom of the article, and professional and technical personnel will assist remotely!
1) Remote installation and operation environment, code debugging
2) Qt, C++, Python entry guide
3) Interface beautification
4) Software production