Create batch-app.py

batch-app.py

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+import gradio as gr
+import os
+from PIL import Image, ImageChops, ImageFilter
+from transformers import CLIPProcessor, CLIPModel, BlipProcessor, BlipForConditionalGeneration
+import torch
+import matplotlib.pyplot as plt
+
+# 初始化模型
+clip_model = CLIPModel.from_pretrained("openai/clip-vit-base-patch32")
+clip_processor = CLIPProcessor.from_pretrained("openai/clip-vit-base-patch32")
+blip_processor = BlipProcessor.from_pretrained("Salesforce/blip-image-captioning-base")
+blip_model = BlipForConditionalGeneration.from_pretrained("Salesforce/blip-image-captioning-base")
+
+# 图像处理函数
+def compute_difference_images(img_a, img_b):
+    def extract_sketch(image):
+        grayscale = image.convert("L")
+        inverted = ImageChops.invert(grayscale)
+        sketch = ImageChops.screen(grayscale, inverted)
+        return sketch
+    
+    def compute_normal_map(image):
+        edges = image.filter(ImageFilter.FIND_EDGES)
+        return edges
+
+    diff_overlay = ImageChops.difference(img_a, img_b)
+    return {
+        "original_a": img_a,
+        "original_b": img_b,
+        "sketch_a": extract_sketch(img_a),
+        "sketch_b": extract_sketch(img_b),
+        "normal_a": compute_normal_map(img_a),
+        "normal_b": compute_normal_map(img_b),
+        "diff_overlay": diff_overlay
+    }
+
+# 保存图像到文件
+def save_images(images):
+    paths = []
+    for key, img in images.items():
+        path = f"{key}.png"
+        img.save(path)
+        paths.append((path, key.replace("_", " ").capitalize()))
+    return paths
+
+# BLIP生成更详尽描述
+def generate_detailed_caption(image):
+    inputs = blip_processor(image, return_tensors="pt")
+    caption = blip_model.generate(**inputs, max_length=128, num_beams=5, no_repeat_ngram_size=2)
+    return blip_processor.decode(caption[0], skip_special_tokens=True)
+
+# 特征差异可视化
+def plot_feature_differences(latent_diff):
+    diff_magnitude = [abs(x) for x in latent_diff[0]]
+    indices = range(len(diff_magnitude))
+
+    plt.figure(figsize=(8, 4))
+    plt.bar(indices, diff_magnitude, alpha=0.7)
+    plt.xlabel("Feature Index")
+    plt.ylabel("Magnitude of Difference")
+    plt.title("Feature Differences (Bar Chart)")
+    bar_chart_path = "bar_chart.png"
+    plt.savefig(bar_chart_path)
+    plt.close()
+
+    plt.figure(figsize=(6, 6))
+    plt.pie(diff_magnitude[:10], labels=range(10), autopct="%1.1f%%", startangle=140)
+    plt.title("Top 10 Feature Differences (Pie Chart)")
+    pie_chart_path = "pie_chart.png"
+    plt.savefig(pie_chart_path)
+    plt.close()
+
+    return bar_chart_path, pie_chart_path
+
+# 生成详细分析
+def generate_text_analysis(api_key, api_type, caption_a, caption_b):
+    import openai
+
+    if api_type == "DeepSeek":
+        from openai import OpenAI
+        client = OpenAI(api_key=api_key, base_url="https://api.deepseek.com")
+    else:
+        client = openai
+
+    response = client.ChatCompletion.create(
+        model="gpt-4" if api_type == "GPT" else "deepseek-chat",
+        messages=[
+            {"role": "system", "content": "You are a helpful assistant."},
+            {"role": "user", "content": f"图片A的描述为：{caption_a}。图片B的描述为：{caption_b}。\n请对两张图片的内容和潜在特征区别进行详细分析，并输出一个简洁但富有条理的总结。"}
+        ]
+    )
+    return response['choices'][0]['message']['content'].strip()
+
+# 分析函数
+def analyze_images(img_a, img_b, api_key, api_type):
+    images_diff = compute_difference_images(img_a, img_b)
+    saved_images = save_images(images_diff)
+
+    caption_a = generate_detailed_caption(img_a)
+    caption_b = generate_detailed_caption(img_b)
+
+    inputs = clip_processor(images=img_a, return_tensors="pt")
+    features_a = clip_model.get_image_features(**inputs).detach().numpy()
+
+    inputs = clip_processor(images=img_b, return_tensors="pt")
+    features_b = clip_model.get_image_features(**inputs).detach().numpy()
+
+    latent_diff = np.abs(features_a - features_b).tolist()
+
+    bar_chart, pie_chart = plot_feature_differences(latent_diff)
+    text_analysis = generate_text_analysis(api_key, api_type, caption_a, caption_b)
+
+    return {
+        "saved_images": saved_images,
+        "caption_a": caption_a,
+        "caption_b": caption_b,
+        "text_analysis": text_analysis,
+        "bar_chart": bar_chart,
+        "pie_chart": pie_chart
+    }
+
+# 批量分析
+def batch_analyze(folder_a, folder_b, api_key, api_type):
+    def load_images(folder_path):
+        files = sorted([os.path.join(folder_path, f) for f in os.listdir(folder_path) if f.lower().endswith(('.png', '.jpg', '.jpeg'))])
+        return [Image.open(f).convert("RGB") for f in files]
+
+    images_a = load_images(folder_a)
+    images_b = load_images(folder_b)
+    num_pairs = min(len(images_a), len(images_b))
+
+    results = []
+    for i in range(num_pairs):
+        result = analyze_images(images_a[i], images_b[i], api_key, api_type)
+        results.append({
+            "pair": (f"Image A-{i+1}", f"Image B-{i+1}"),
+            **result
+        })
+    return results
+
+# Gradio界面
+with gr.Blocks() as demo:
+    gr.Markdown("# 批量图像对比分析工具")
+
+    api_key_input = gr.Textbox(label="API Key", placeholder="输入您的 API Key", type="password")
+    api_type_input = gr.Dropdown(label="API 类型", choices=["GPT", "DeepSeek"], value="GPT")
+    folder_a_input = gr.Textbox(label="文件夹A路径", placeholder="输入包含图片A的文件夹路径")
+    folder_b_input = gr.Textbox(label="文件夹B路径", placeholder="输入包含图片B的文件夹路径")
+    analyze_button = gr.Button("开始批量分析")
+
+    with gr.Row():
+        result_gallery = gr.Gallery(label="差异图像").style(grid=3)
+    result_text_analysis = gr.Textbox(label="详细分析", interactive=False, lines=5)
+
+    def process_batch_analysis(folder_a, folder_b, api_key, api_type):
+        results = batch_analyze(folder_a, folder_b, api_key, api_type)
+        all_images = []
+        all_texts = []
+
+        for result in results:
+            all_images.extend(result["saved_images"])
+            all_images.append((result["bar_chart"], "Bar Chart"))
+            all_images.append((result["pie_chart"], "Pie Chart"))
+            all_texts.append(f"{result['pair'][0]} vs {result['pair'][1]}:\n{result['text_analysis']}")
+
+        return all_images, "\n\n".join(all_texts)
+
+    analyze_button.click(
+        fn=process_batch_analysis,
+        inputs=[folder_a_input, folder_b_input, api_key_input, api_type_input],
+        outputs=[result_gallery, result_text_analysis]
+    )
+
+demo.launch()