批量裁剪证件照，用在学生学籍、书法考级等场合，python编写，加了图片中心头像检测，实用高效

import tkinter as tk from tkinter import filedialog import cv2 import os import dlib import io import numpy as np from tkinter import messagebox # system运行shell命令(发生中文乱码情况，这是字符编码问题，默认字符编码是GBK这个中文编码，但是很奇怪会乱码。解决方案1.使用chcp 65001将编码改变成utf-8) os.system('chcp 65001') os.system('dir') # 创建主窗口 root = tk.Tk() root.title("证件照裁剪工具") # 设置窗口大小 root.geometry("400x200") # 创建标签 label = tk.Label(root, text="选择输入文件夹:") label.pack() # 创建输入文件夹路径文本框 input_folder_var = tk.StringVar() input_folder_entry = tk.Entry(root, textvariable=input_folder_var) input_folder_entry.pack() # 创建按钮，用于选择输入文件夹 def browse_input_folder(): folder_selected = filedialog.askdirectory() input_folder_var.set(folder_selected) input_folder_button = tk.Button(root, text="浏览", command=browse_input_folder) input_folder_button.pack() # 创建标签 label = tk.Label(root, text="选择输出文件夹:") label.pack() # 创建输出文件夹路径文本框 output_folder_var = tk.StringVar() output_folder_entry = tk.Entry(root, textvariable=output_folder_var) output_folder_entry.pack() # 创建按钮，用于选择输出文件夹 def browse_output_folder(): folder_selected = filedialog.askdirectory() output_folder_var.set(folder_selected) output_folder_button = tk.Button(root, text="浏览", command=browse_output_folder) output_folder_button.pack() # 创建按钮，用于执行裁剪操作 def crop_photos(): input_folder = input_folder_var.get() output_folder = output_folder_var.get() # 添加裁剪代码，将您之前的裁剪代码放在这里 # 加载Caffe模型 model_file = 'deploy.prototxt' weights_file = 'res10_300x300_ssd_iter_140000.caffemodel' net = cv2.dnn.readNetFromCaffe(model_file, weights_file) # 确保输出文件夹存在，如果不存在则创建 if not os.path.exists(output_folder): os.makedirs(output_folder) if input_folder==output_folder: result = messagebox.askyesno('注意', '输入与输出目录相同，请换一个输出目录，防止覆盖原文件！') return # 处理输入文件夹中的每张照片 for filename in os.listdir(input_folder): if filename.lower().endswith(".jpg"): # 忽略大小写，仅处理".jpg"后缀的文件 print("Crop："+filename) # 读取原始图片 #original_image = cv2.imread(os.path.join(input_folder, filename)) original_image = cv2.imdecode(np.fromfile(os.path.join(input_folder, filename),dtype=np.uint8),-1) # 将图片转换为blob以输入Caffe模型 blob = cv2.dnn.blobFromImage(cv2.resize(original_image, (300, 300)), 1.0, (300, 300), (104, 177, 123)) # 将blob输入模型 net.setInput(blob) # 进行前向传播 detections = net.forward() central_face = None # 用于存储中央人像 for i in range(detections.shape[2]): confidence = detections[0, 0, i, 2] if confidence > 0.5: # 可以根据置信度阈值调整 x1, y1, x2, y2 = map(int, detections[0, 0, i, 3:7] * [original_image.shape[1], original_image.shape[0], original_image.shape[1], original_image.shape[0]]) # 计算人脸中心坐标 center_x, center_y = (x1 + x2) // 2, (y1 + y2) // 2 # 检查人脸是否在图像中央 if abs(center_x - original_image.shape[1] // 2) <= original_image.shape[1] // 4 and abs(center_y - original_image.shape[0] // 2) <= original_image.shape[0] // 4: # 如果人脸位于中央，将其保存为中央人像 central_face = original_image[y1:y2, x1:x2] break if central_face is not None: # 处理中央人像，例如裁剪、修改背景等 # 计算人脸宽度和高度 face_width = x2 - x1 face_height = y2 - y1 # 根据脸型大小动态调整裁剪区域的大小 target_width = int(face_width * 1.8) # 脸型大的裁剪范围大一些 target_height = int(face_height * 2) target_width=max(int(target_height*5/7),target_width) target_height=max(int(target_width*7/5),target_height) # 计算裁剪区域的中心坐标 center_x, center_y = (x1 + x2) // 2, (y1 + y2) // 2 # 根据脸型大小微调裁剪区域 #if center_y - target_height // 2 > 0: #center_y -= target_height // 50 # 向上微调 # 计算裁剪区域的左上角和右下角坐标，使人脸位于中央 crop_x1 = max(center_x - target_width // 2, 0) crop_x2 = min(center_x + target_width // 2, original_image.shape[1]) crop_y1 = max(center_y - target_height // 2, 0) crop_y2 = min(center_y + target_height // 2, original_image.shape[0]) # 裁剪并调整人脸大小为标准尺寸 resized_face = cv2.resize(original_image[crop_y1:crop_y2, crop_x1:crop_x2], (target_width, target_height)) # 保存证件照，保持文件后缀名不变 output_filename = os.path.join(output_folder, filename) _,image_data = cv2.imencode('.jpg',resized_face) with open(output_filename,'wb') as f: f.write(image_data) print("End!") crop_button = tk.Button(root, text="开始裁剪", command=crop_photos) crop_button.pack() # 启动GUI事件循环 root.mainloop()