Python photo image editing tool – flip, rotate, brightness, skin, crop, add text
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Foreword
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Article directory
1. Required tools and software
2. Use steps
1. Import library
2. Identify image
3. Running results
3. Online assistance
1. Required tool software
1. Pycharm, Python
2. Qt, OpenCV
2. Steps to use
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_size if half:
model.half() # to FP16# Second-stage classifier
classify = False if 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 Classifier if classify:
pred = apply_classifier(pred, modelc, img, im0s)
# Process detections for 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 results for *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 + '\
')
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"\
{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:
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Business cards, remotely assisted by professional technicians!
1) Remote installation of the operating environment, code debugging
2) Qt, C++, Python Getting Started Guide
3) Interface beautification
4) Software production
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