from albumentations.core.transforms_interface import ImageOnlyTransform
    
class InsectAugmentation(ImageOnlyTransform):
    """
    將昆蟲的圖像強加到目標圖像上
    -----------------------------------------------
    參數(shù):
        insects (int): 昆蟲的最大數(shù)量
        insects_folder (str): 昆蟲圖片文件夾的路徑
    """
 
    def __init__(self, insects=2, dark_insect=False, always_apply=False, p=0.5):
        super().__init__(always_apply, p)
        self.insects = insects
        self.dark_insect = dark_insect
        self.insects_folder = "/kaggle/input/bee-augmentation/"
 
    def apply(self, image, **kwargs):
        """
        參數(shù):
            image (PIL Image): 畫昆蟲的圖像。
        Returns:
            PIL Image: 帶昆蟲的圖像。
        """
        n_insects = random.randint(1, self.insects) # 在這個例子中，我用1而不是0來說明增強效果
        
        if not n_insects:
            return image
        
        height, width, _ = image.shape  # 目標圖像的寬度和高度
        insects_images = [im for im in os.listdir(self.insects_folder) if 'png' in im]
        
        for _ in range(n_insects):
            insect = cv2.cvtColor(cv2.imread(os.path.join(self.insects_folder, random.choice(insects_images))), cv2.COLOR_BGR2RGB)
            insect = cv2.flip(insect, random.choice([-1, 0, 1]))
            insect = cv2.rotate(insect, random.choice([0, 1, 2]))
 
            h_height, h_width, _ = insect.shape  # 昆蟲圖像的寬度和高度
            roi_ho = random.randint(0, image.shape[0] - insect.shape[0])
            roi_wo = random.randint(0, image.shape[1] - insect.shape[1])
            roi = image[roi_ho:roi_ho + h_height, roi_wo:roi_wo + h_width]
 
            # 創(chuàng)建掩碼和反掩碼
            img2gray = cv2.cvtColor(insect, cv2.COLOR_BGR2GRAY)
            ret, mask = cv2.threshold(img2gray, 10, 255, cv2.THRESH_BINARY)
            mask_inv = cv2.bitwise_not(mask)
 
            # 現(xiàn)在黑掉的區(qū)域是昆蟲
            img_bg = cv2.bitwise_and(roi, roi, mask=mask_inv)
 
            # 從昆蟲圖像中只選取昆蟲區(qū)域。
            if self.dark_insect:
                img_bg = cv2.bitwise_and(roi, roi, mask=mask_inv)
                insect_fg = cv2.bitwise_and(img_bg, img_bg, mask=mask)
            else:
                insect_fg = cv2.bitwise_and(insect, insect, mask=mask)
 
            # 添加
            dst = cv2.add(img_bg, insect_fg, dtype=cv2.CV_64F)
 
            image[roi_ho:roi_ho + h_height, roi_wo:roi_wo + h_width] = dst
                
        return image

def NeedleAugmentation(image, n_needles=2, dark_needles=False, p=0.5, needle_folder='../input/xray-needle-augmentation'):
    aug_prob = random.random()
    if aug_prob < p:
        height, width, _ = image.shape  # 目標圖像的寬度和高度
        needle_images = [im for im in os.listdir(needle_folder) if 'png' in im]
 
        for _ in range(1, n_needles):
            needle = cv2.cvtColor(cv2.imread(os.path.join(needle_folder, random.choice(needle_images))), cv2.COLOR_BGR2RGB)
            needle = cv2.flip(needle, random.choice([-1, 0, 1]))
            needle = cv2.rotate(needle, random.choice([0, 1, 2]))
 
            h_height, h_width, _ = needle.shape  # 針圖像的寬度和高度
            roi_ho = random.randint(0, abs(image.shape[0] - needle.shape[0]))
            roi_wo = random.randint(0, abs(image.shape[1] - needle.shape[1]))
            roi = image[roi_ho:roi_ho + h_height, roi_wo:roi_wo + h_width]
 
            # 創(chuàng)建掩碼和反掩碼
            img2gray = cv2.cvtColor(needle, cv2.COLOR_BGR2GRAY)
            ret, mask = cv2.threshold(img2gray, 10, 255, cv2.THRESH_BINARY)
            mask_inv = cv2.bitwise_not(mask)
 
            # 現(xiàn)在黑掉的區(qū)域是針
            img_bg = cv2.bitwise_and(roi, roi, mask=mask_inv)
 
            # 只選取針區(qū)域。
            if dark_needles:
                img_bg = cv2.bitwise_and(roi, roi, mask=mask_inv)
                needle_fg = cv2.bitwise_and(img_bg, img_bg, mask=mask)
            else:
                needle_fg = cv2.bitwise_and(needle, needle, mask=mask)
 
            # 添加
            dst = cv2.add(img_bg, needle_fg, dtype=cv2.CV_64F)
 
            image[roi_ho:roi_ho + h_height, roi_wo:roi_wo + h_width] = dst
 
    return image