Python相片图片编辑工具-翻转旋转亮度磨皮裁剪添加文字
Python相片图片编辑工具-翻转旋转亮度磨皮裁剪添加文字
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前言
这篇博客针对<
文章目录
一、所需工具软件
二、使用步骤
1. 引入库
2. 识别图像
3. 运行结果
三、在线协助
一、所需工具软件
1. Pycharm, Python
2. Qt, OpenCV
二、使用步骤
1.引入库
代码如下（示例）：
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.识别图像特征
代码如下（示例）：
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. 运行结果如下
三、在线协助：
如需安装运行环境或远程调试，见文章底部个人 QQ 名片，由专业技术人员远程协助！
1）远程安装运行环境，代码调试
2）Qt, C++, Python入门指导
3）界面美化
4）软件制作
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