企业级AI证件照批量生成方案

import cv2
import numpy as np

def process_certificate_photo(image_path, output_path):
    # 读取图像
    image = cv2.imread(image_path)

    # 人脸检测
    face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml')
    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
    faces = face_cascade.detectMultiScale(gray, 1.1, 4)

    for (x, y, w, h) in faces:
        # 裁剪人脸
        face = image[y:y+h, x:x+w]

        # 调整大小
        face = cv2.resize(face, (200, 250))

        # 创建背景
        background = np.ones((300, 250, 3), dtype=np.uint8) * 255

        # 将人脸粘贴到背景上
        y_offset = int((300 - 250) / 2)
        background[y_offset:y_offset+250, :] = face

        # 保存处理后的图像
        cv2.imwrite(output_path, background)
        break  # 只处理第一张检测到的人脸

# 使用示例
process_certificate_photo('input.jpg', 'output.jpg')

from tensorflow.keras.preprocessing.image import ImageDataGenerator

# 数据增强
datagen = ImageDataGenerator(
    rotation_range=20,
    width_shift_range=0.2,
    height_shift_range=0.2,
    shear_range=0.2,
    zoom_range=0.2,
    horizontal_flip=True,
    brightness_range=[0.5, 1.5]
)

# 应用数据增强
image = cv2.imread('input.jpg')
image = image.reshape((1,) + image.shape)
i = 0
for batch in datagen.flow(image, batch_size=1, save_to_dir='augmented_images', save_prefix='aug', save_format='jpg'):
    i += 1
    if i > 20:
        break

import concurrent.futures
import os

def process_photo(image_path, output_dir):
    output_path = os.path.join(output_dir, os.path.basename(image_path))
    process_certificate_photo(image_path, output_path)

def batch_process_photos(input_dir, output_dir):
    with concurrent.futures.ThreadPoolExecutor() as executor:
        futures = []
        for filename in os.listdir(input_dir):
            if filename.endswith(('.png', '.jpg', '.jpeg')):
                image_path = os.path.join(input_dir, filename)
                futures.append(executor.submit(process_photo, image_path, output_dir))

        for future in concurrent.futures.as_completed(futures):
            future.result()

# 使用示例
batch_process_photos('input_directory', 'output_directory')