from PIL import Image
import os
import cv2
import numpy as np

class SmartCropper:
    def __init__(self):
        self.face_cascade = cv2.CascadeClassifier(
            cv2.data.haarcascades + 'haarcascade_frontalface_default.xml')
        
    def detect_interest_area(self, image_path):
        """檢測圖片中需要保留的關(guān)鍵區(qū)域"""
        img = cv2.imread(image_path)
        gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
        
        # 人臉檢測
        faces = self.face_cascade.detectMultiScale(gray, 1.1, 4)
        if len(faces) > 0:
            x, y, w, h = faces[0]
            return (x, y, x+w, y+h)
        
        # 如果沒有檢測到人臉，使用顯著性檢測
        saliency = cv2.saliency.StaticSaliencyFineGrained_create()
        _, saliency_map = saliency.computeSaliency(img)
        _, max_loc = cv2.minMaxLoc(saliency_map)
        
        return (max_loc[0]-100, max_loc[1]-100, 
                max_loc[0]+100, max_loc[1]+100)

    def smart_crop(self, image_path, output_path, aspect_ratio='1:1'):
        """智能裁剪主函數(shù)"""
        try:
            img = Image.open(image_path)
            width, height = img.size
            
            # 計算目標(biāo)尺寸
            if aspect_ratio == '1:1':
                new_size = min(width, height)
            elif aspect_ratio == '4:3':
                new_size = (min(width, height*4/3), 
                           min(height, width*3/4))
            else:  # 16:9
                new_size = (min(width, height*16/9),
                           min(height, width*9/16))
            
            # 獲取興趣區(qū)域
            left, top, right, bottom = self.detect_interest_area(image_path)
            center_x = (left + right) // 2
            center_y = (top + bottom) // 2
            
            # 計算裁剪區(qū)域
            if isinstance(new_size, tuple):  # 矩形裁剪
                crop_width, crop_height = new_size
                left = max(0, center_x - crop_width//2)
                top = max(0, center_y - crop_height//2)
                right = min(width, center_x + crop_width//2)
                bottom = min(height, center_y + crop_height//2)
            else:  # 正方形裁剪
                left = max(0, center_x - new_size//2)
                top = max(0, center_y - new_size//2)
                right = min(width, center_x + new_size//2)
                bottom = min(height, center_y + new_size//2)
            
            # 執(zhí)行裁剪
            cropped = img.crop((left, top, right, bottom))
            cropped.save(output_path)
            
        except Exception as e:
            print(f"裁剪{image_path}失敗: {str(e)}")

    def batch_crop(self, input_dir, output_dir, ratio='1:1'):
        """批量處理文件夾中的圖片"""
        if not os.path.exists(output_dir):
            os.makedirs(output_dir)
            
        for filename in os.listdir(input_dir):
            if filename.lower().endswith(('.jpg', '.jpeg', '.png')):
                input_path = os.path.join(input_dir, filename)
                output_path = os.path.join(output_dir, filename)
                print(f"正在處理: {filename}")
                self.smart_crop(input_path, output_path, ratio)

cropper = SmartCropper()

# 單張圖片裁剪
cropper.smart_crop('input.jpg', 'output.jpg', '4:3')

# 批量裁剪
cropper.batch_crop('/path/to/input', '/path/to/output', '1:1')

# 以中心點為基準(zhǔn)的裁剪邏輯示例
center_x = (left + right) // 2
center_y = (top + bottom) // 2

# 保證裁剪區(qū)域不超出圖片邊界
left = max(0, center_x - target_width//2)
right = min(original_width, center_x + target_width//2)

# 批量處理配置
cropper = SmartCropper()
cropper.batch_crop(
    '/data/ecommerce/raw',
    '/data/ecommerce/cropped',
    '1:1'
)

class IDPhotoCropper(SmartCropper):
    def detect_interest_area(self, image_path):
        # 重寫檢測邏輯，嚴(yán)格定位頭部位置
        faces = self.face_cascade.detectMultiScale(...)
        if len(faces) > 0:
            x, y, w, h = faces[0]
            # 計算標(biāo)準(zhǔn)證件照頭部位置
            return (x-w//2, y-h*2, x+w*2, y+h*3)

# 在smart_crop方法中添加校驗邏輯
if w < 50 or h < 50:  # 忽略太小的"人臉"
    return self.fallback_detection(image_path)

# 先縮小檢測再還原坐標(biāo)
def detect_interest_area(self, image_path):
    img = cv2.imread(image_path)
    small = cv2.resize(img, (800, 600))
    # 在縮小后的圖片上檢測
    # 將坐標(biāo)映射回原圖尺寸

from PIL.ExifTags import TAGS

def save_with_exif(img, output_path):
    # 保留原始EXIF數(shù)據(jù)
    exif = img.info.get('exif')
    if exif:
        img.save(output_path, exif=exif)
    else:
        img.save(output_path)

{
  "preserveAreas": [
    {"x":100,"y":100,"width":200,"height":200},
    {"x":400,"y":300,"width":150,"height":150}
  ],
  "margin": 20,
  "aspectRatio": "16:9"
}