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import gradio as gr
import torch
from transformers import CLIPProcessor, CLIPModel, BlipProcessor, BlipForConditionalGeneration
from PIL import Image
import numpy as np
import openai # GPT API 调用
# 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")
# GPT API 配置
openai.api_key = "your_openai_api_key"
# 定义功能函数
def analyze_images(image_a, image_b):
# BLIP生成描述
def generate_caption(image):
inputs = blip_processor(image, return_tensors="pt")
caption = blip_model.generate(**inputs)
return blip_processor.decode(caption[0], skip_special_tokens=True)
# CLIP特征提取
def extract_features(image):
inputs = clip_processor(images=image, return_tensors="pt")
features = clip_model.get_image_features(**inputs)
return features.detach().numpy()
# 加载图片
img_a = Image.open(image_a).convert("RGB")
img_b = Image.open(image_b).convert("RGB")
# 生成描述
caption_a = generate_caption(img_a)
caption_b = generate_caption(img_b)
# 提取特征
features_a = extract_features(img_a)
features_b = extract_features(img_b)
# 计算嵌入相似性
cosine_similarity = np.dot(features_a, features_b.T) / (np.linalg.norm(features_a) * np.linalg.norm(features_b))
latent_diff = np.abs(features_a - features_b).tolist()
# GPT API 调用生成文字描述
gpt_prompt = (
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。"
)
gpt_response = openai.Completion.create(
engine="text-davinci-003",
prompt=gpt_prompt,
max_tokens=150
)
textual_analysis = gpt_response['choices'][0]['text'].strip()
# 返回结果
return {
"caption_a": caption_a,
"caption_b": caption_b,
"similarity": cosine_similarity[0][0],
"latent_diff": latent_diff,
"text_analysis": textual_analysis
}
# 定义Gradio界面
with gr.Blocks() as demo:
gr.Markdown("# 图片对比分析工具")
with gr.Row():
with gr.Column():
image_a = gr.Image(label="图片A", type="file")
with gr.Column():
image_b = gr.Image(label="图片B", type="file")
analyze_button = gr.Button("分析图片")
result_caption_a = gr.Textbox(label="图片A描述", interactive=False)
result_caption_b = gr.Textbox(label="图片B描述", interactive=False)
result_similarity = gr.Number(label="图片相似性", interactive=False)
result_latent_diff = gr.DataFrame(label="潜在特征差异", interactive=False)
result_text_analysis = gr.Textbox(label="详细分析", interactive=False, lines=5)
# 分析逻辑
def process_analysis(img_a, img_b):
results = analyze_images(img_a, img_b)
return results["caption_a"], results["caption_b"], results["similarity"], results["latent_diff"], results["text_analysis"]
analyze_button.click(
fn=process_analysis,
inputs=[image_a, image_b],
outputs=[result_caption_a, result_caption_b, result_similarity, result_latent_diff, result_text_analysis]
)
demo.launch()
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