Question

import cv2
import numpy as np
from PIL import Image, ImageChops, ImageEnhance, UnidentifiedImageError
import os
import tensorflow as tf
import binascii

class ImageAnalyzer:
    def __init__(self, model_path="../scripts/results/image/Model-2/MobileNetV2.keras"):
        self.model = tf.keras.models.load_model(model_path)

    def generate_unique_filename(self, extension=".jpg"):
        return binascii.hexlify(os.urandom(8)).decode() + extension

    def perform_ela(self, image_path, quality=90, threshold=30, save_path="output/"):
        if not os.path.exists(save_path):
            os.makedirs(save_path)
        
        unique_filename = self.generate_unique_filename(".jpg")
        temp_path = os.path.join(save_path, unique_filename)
        
        original = Image.open(image_path).convert("RGB")
        original.save(temp_path, "JPEG", quality=quality)
        compressed = Image.open(temp_path)
        
        ela_image = ImageChops.difference(original, compressed)
        extrema = ela_image.getextrema()
        max_diff = max([ex[1] for ex in extrema])
        
        if max_diff == 0:
            max_diff = 1
        
        scale = 255.0 / max_diff
        ela_image = ImageEnhance.Brightness(ela_image).enhance(scale)
        ela_image_path = os.path.join(save_path, self.generate_unique_filename(".png"))
        ela_image.save(ela_image_path)
        
        ela_cv = cv2.imread(ela_image_path)
        gray = cv2.cvtColor(ela_cv, cv2.COLOR_BGR2GRAY)
        _, thresh = cv2.threshold(gray, threshold, 255, cv2.THRESH_BINARY)
        
        total_pixels = thresh.size
        forged_pixels = np.count_nonzero(thresh)
        forged_percentage = (forged_pixels / total_pixels) * 100
        
        highlight_path = os.path.join(save_path, self.generate_unique_filename(".png"))
        cv2.imwrite(highlight_path, thresh)
        
        is_forged = "Yes" if forged_percentage > 0.5 else "No"
        
        return {
            "forged": is_forged,
            "percentage": round(forged_percentage, 2),
            "ela_image": ela_image_path,
            "highlighted_image": highlight_path
        }
    
    def predict_image(self, image_path):
        image = Image.open(image_path).resize((224, 224))
        image = np.array(image) / 255.0
        image = np.expand_dims(image, axis=0)
        
        prediction = self.model.predict(image)[0]
        confidence_real = prediction[0]
        confidence_fake = prediction[1]
        
        label = "Real" if confidence_real > confidence_fake else "Fake"
        confidence = max(confidence_real, confidence_fake) * 100
        
        return {"label": label, "confidence": round(confidence, 2)}
    
    def def_image(self, image):
        try:
            if not isinstance(image, Image.Image):
                raise ValueError("Invalid image format. Please upload a valid image file.")

            # Convert to RGB to remove alpha channels and ensure compatibility
            image = image.convert("RGB")

            # Save image as a temporary JPEG file
            image_path = "image/temp_image.jpg"
            image.save(image_path, format="JPEG")

            # Perform ELA analysis and prediction
            ela_result = self.perform_ela(image_path)
            prediction_result = self.predict_image(image_path)

            return {
                "forged": ela_result["forged"],
                "percentage": ela_result["percentage"],
                "ela_image": ela_result["ela_image"],
                "highlighted_image": ela_result["highlighted_image"],
                "prediction_label": prediction_result["label"],
                "prediction_confidence": prediction_result["confidence"]
            }
        
        except UnidentifiedImageError:
            raise ValueError("Corrupt or unsupported image file. Please upload a valid image.")
        except Exception as e:
            raise ValueError(f"An unexpected error occurred: {e}")

eRror：

PermissionError: [Errno 13] Permission denied: 'D:\\Uni\\FYP\\Model\\model\\Demo_Gradio' 我尝试创建一个新的目录，并且还对代码进行了更改，但到目前为止没有什么真正奏效的。

trory使用Python OS模块打开并读取文件夹数据