各種不同的模糊處理

import os
import cv2def apply_blur_to_images(input_folder_path, output_folder_path):# 遍歷文件夾下的所有文件for filename in os.listdir(input_folder_path):# 檢查文件類型是否為圖片if filename.endswith('.jpg') or filename.endswith('.jpeg') or filename.endswith('.png'):# 構建輸入圖片的完整路徑input_image_path = os.path.join(input_folder_path, filename)# 讀取圖片image = cv2.imread(input_image_path)# 根據不同的模糊方法進行處理for blur_method in ['gaussian', 'mean', 'median', 'bilateral']:# 創建對應的模糊文件夾output_blur_folder_path = os.path.join(output_folder_path, blur_method)os.makedirs(output_blur_folder_path, exist_ok=True)# 根據選擇的模糊方法進行處理if blur_method == 'mean':blurred_image = cv2.blur(image, (15, 15))elif blur_method == 'median':blurred_image = cv2.medianBlur(image, 15)elif blur_method == 'bilateral':blurred_image = cv2.bilateralFilter(image, 15, 75, 75)else:blurred_image = cv2.GaussianBlur(image, (15, 15), 0)# 構建輸出圖片的完整路徑output_image_path = os.path.join(output_blur_folder_path, filename)# 保存模糊處理后的圖片cv2.imwrite(output_image_path, blurred_image)if __name__ == '__main__':# 文件夾不要有中文！！！！！！！！！# 輸入文件夾路徑input_folder_path = './data'# 輸出文件夾路徑output_folder_path = './output'# 調用函數apply_blur_to_images(input_folder_path, output_folder_path)

resize 下采樣

import os
import cv2def reduce_resolution(input_folder_path, output_folder_path, scale_factor, interpolation):# 遍歷文件夾下的所有文件for filename in os.listdir(input_folder_path):# 檢查文件類型是否為圖片if filename.endswith('.jpg') or filename.endswith('.jpeg') or filename.endswith('.png'):# 構建輸入圖片的完整路徑input_image_path = os.path.join(input_folder_path, filename)# 讀取圖片image = cv2.imread(input_image_path)# 計算目標寬度和高度target_width = int(image.shape[1] * scale_factor)target_height = int(image.shape[0] * scale_factor)# 調整圖像尺寸resized_image = cv2.resize(image, (target_width, target_height), interpolation=interpolation)# 構建輸出圖片的完整路徑interpolation_name = get_interpolation_name(interpolation)output_folder = os.path.join(output_folder_path, interpolation_name)os.makedirs(output_folder, exist_ok=True)  # 創建輸出文件夾（如果不存在）output_image_path = os.path.join(output_folder, filename)# 保存調整尺寸后的圖片cv2.imwrite(output_image_path, resized_image)def get_interpolation_name(interpolation):if interpolation == cv2.INTER_NEAREST:return 'INTER_NEAREST'elif interpolation == cv2.INTER_LINEAR:return 'INTER_LINEAR'elif interpolation == cv2.INTER_CUBIC:return 'INTER_CUBIC'elif interpolation == cv2.INTER_LANCZOS4:return 'INTER_LANCZOS4'else:return 'UNKNOWN'if __name__ == '__main__':# 文件夾不要有中文！！！！！！！！！# 輸入文件夾路徑input_folder_path = './data'# 輸出文件夾路徑output_folder_path = './output'# 比例系數scale_factor = 0.5  # 調整為原始圖像的一半# 插值方法列表interpolations = [cv2.INTER_NEAREST, cv2.INTER_LINEAR, cv2.INTER_CUBIC, cv2.INTER_LANCZOS4]# 遍歷插值方法for interpolation in interpolations:# 調用函數進行圖像尺寸調整reduce_resolution(input_folder_path, output_folder_path, scale_factor, interpolation)

遍歷文件夾，結果以名字命令，方便區分