473 lines
16 KiB
Python
473 lines
16 KiB
Python
import os
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import uuid
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import time
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import requests
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import numpy as np
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import json
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import torch
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import cv2
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import ast
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import re
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from PIL import Image
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from dashscope import MultiModalConversation
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# 配置 (与 fastAPI_tarot.py 保持一致或通过参数传入)
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# 这里的常量可以根据需要调整，或者从主文件传入
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QWEN_MODEL = 'qwen-vl-max'
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def load_image_from_url(url: str) -> Image.Image:
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"""
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从 URL 下载图片并转换为 RGB 格式
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"""
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try:
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headers = {'User-Agent': 'Mozilla/5.0'}
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response = requests.get(url, headers=headers, stream=True, timeout=10)
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response.raise_for_status()
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image = Image.open(response.raw).convert("RGB")
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return image
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except Exception as e:
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raise Exception(f"无法下载图片: {str(e)}")
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def crop_head_with_padding(image: Image.Image, box, padding_ratio=0.1) -> Image.Image:
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"""
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根据 bounding box 裁剪图片，并添加一定的 padding
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box格式: [x1, y1, x2, y2]
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"""
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img_w, img_h = image.size
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x1, y1, x2, y2 = box
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w = x2 - x1
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h = y2 - y1
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# 计算 padding
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pad_w = w * padding_ratio
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pad_h = h * padding_ratio
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# 应用 padding 并确保不越界
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new_x1 = max(0, int(x1 - pad_w))
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new_y1 = max(0, int(y1 - pad_h))
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new_x2 = min(img_w, int(x2 + pad_w))
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new_y2 = min(img_h, int(y2 + pad_h))
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return image.crop((new_x1, new_y1, new_x2, new_y2))
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def create_highlighted_visualization(image: Image.Image, masks, output_path: str):
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"""
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创建一个突出显示头部（Mask区域）的可视化图，背景变暗
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"""
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# Convert PIL to numpy RGB
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img_np = np.array(image)
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# Create darkened background (e.g., 30% brightness)
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darkened_np = (img_np * 0.3).astype(np.uint8)
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# Combine all masks
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if len(masks) > 0:
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combined_mask = np.zeros(img_np.shape[:2], dtype=bool)
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for mask in masks:
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# Handle tensor/numpy conversions
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if isinstance(mask, torch.Tensor):
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m = mask.cpu().numpy().squeeze()
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else:
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m = mask.squeeze()
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# Ensure 2D
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if m.ndim > 2:
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m = m[0]
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# Threshold if probability or float
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if m.dtype != bool:
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m = m > 0.5
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# Resize mask if it doesn't match image size (rare but possible with some internal resizing)
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if m.shape != img_np.shape[:2]:
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# resize to match image
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m = cv2.resize(m.astype(np.uint8), (img_np.shape[1], img_np.shape[0]), interpolation=cv2.INTER_NEAREST).astype(bool)
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combined_mask = np.logical_or(combined_mask, m)
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# Expand mask to 3 channels for broadcasting
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mask_3ch = np.stack([combined_mask]*3, axis=-1)
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# Composite: Original where mask is True, Darkened where False
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result_np = np.where(mask_3ch, img_np, darkened_np)
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else:
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result_np = darkened_np # No masks, just dark
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# Save
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Image.fromarray(result_np).save(output_path)
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def extract_json_from_response(text: str) -> dict:
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"""
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Robustly extract JSON from text, handling:
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1. Markdown code blocks (```json ... ```)
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2. Single quotes (Python dict style) via ast.literal_eval
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"""
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try:
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# 1. Try to find JSON block
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json_match = re.search(r'```json\s*(.*?)\s*```', text, re.DOTALL)
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if json_match:
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clean_text = json_match.group(1).strip()
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else:
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# Try to find { ... } block if no markdown
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match = re.search(r'\{.*\}', text, re.DOTALL)
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if match:
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clean_text = match.group(0).strip()
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else:
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clean_text = text.strip()
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# 2. Try standard JSON
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return json.loads(clean_text)
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except Exception as e1:
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# 3. Try ast.literal_eval for single quotes
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try:
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return ast.literal_eval(clean_text)
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except Exception as e2:
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# 4. Fail
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raise ValueError(f"Could not parse JSON: {e1} | {e2} | Content: {text[:100]}...")
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def analyze_demographics_with_qwen(image_path: str, model_name: str = 'qwen-vl-max', prompt_template: str = None) -> dict:
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"""
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调用 Qwen-VL 模型分析人物的年龄和性别
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"""
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try:
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# 确保路径是绝对路径
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abs_path = os.path.abspath(image_path)
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file_url = f"file://{abs_path}"
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# 默认 Prompt
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default_prompt = """请仔细观察这张图片中的人物头部/面部特写：
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1. 识别性别 (Gender)：男性/女性
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2. 预估年龄 (Age)：请给出一个合理的年龄范围，例如 "25-30岁"
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3. 简要描述：发型、发色、是否有眼镜等显著特征。
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请以 JSON 格式返回，包含 'gender', 'age', 'description' 字段。
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不要包含 Markdown 标记。"""
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final_prompt = prompt_template if prompt_template else default_prompt
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# 构造 Prompt
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messages = [
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{
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"role": "user",
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"content": [
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{"image": file_url},
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{"text": final_prompt}
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]
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}
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]
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# 调用模型
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response = MultiModalConversation.call(model=model_name, messages=messages)
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if response.status_code == 200:
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content = response.output.choices[0].message.content[0]['text']
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try:
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result = extract_json_from_response(content)
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result["model_used"] = model_name
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return result
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except Exception as e:
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print(f"JSON Parse Error in face analysis: {e}")
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return {"raw_analysis": content, "error": str(e), "model_used": model_name}
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else:
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return {"error": f"API Error: {response.code} - {response.message}"}
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except Exception as e:
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return {"error": f"分析失败: {str(e)}"}
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import asyncio
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def process_face_segmentation_and_analysis(
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processor,
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image: Image.Image,
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prompt: str = "head",
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output_base_dir: str = "static/results",
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qwen_model: str = "qwen-vl-max",
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analysis_prompt: str = None
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) -> dict:
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"""
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核心处理逻辑：
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1. SAM3 分割 (默认提示词 "head" 以包含头发)
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2. 裁剪图片
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3. Qwen-VL 识别性别年龄 (并发)
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4. 返回结果
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"""
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# 1. SAM3 推理 (同步，因为涉及 GPU 操作)
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inference_state = processor.set_image(image)
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output = processor.set_text_prompt(state=inference_state, prompt=prompt)
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masks, boxes, scores = output["masks"], output["boxes"], output["scores"]
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detected_count = len(masks)
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if detected_count == 0:
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return {
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"status": "success",
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"message": "未检测到目标",
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"detected_count": 0,
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"results": []
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}
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# 准备结果目录
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request_id = f"{int(time.time())}_{uuid.uuid4().hex[:8]}"
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output_dir = os.path.join(output_base_dir, request_id)
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os.makedirs(output_dir, exist_ok=True)
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# --- 生成可视化图 ---
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vis_filename = f"seg_{uuid.uuid4().hex}.jpg"
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vis_path = os.path.join(output_dir, vis_filename)
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try:
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create_highlighted_visualization(image, masks, vis_path)
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full_vis_relative_path = f"results/{request_id}/{vis_filename}"
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except Exception as e:
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print(f"可视化生成失败: {e}")
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full_vis_relative_path = None
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# ------------------
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# 转换 boxes 和 scores
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if isinstance(boxes, torch.Tensor):
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boxes_np = boxes.cpu().numpy()
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else:
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boxes_np = boxes
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if isinstance(scores, torch.Tensor):
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scores_list = scores.tolist()
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else:
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scores_list = scores if isinstance(scores, list) else [float(scores)]
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# 准备异步任务
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async def run_analysis_tasks():
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loop = asyncio.get_event_loop()
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tasks = []
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temp_results = [] # 存储 (index, filename, score) 以便后续排序组合
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for i, box in enumerate(boxes_np):
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# 2. 裁剪 (同步)
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cropped_img = crop_head_with_padding(image, box, padding_ratio=0.1)
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filename = f"face_{i}.jpg"
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save_path = os.path.join(output_dir, filename)
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cropped_img.save(save_path)
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# 3. 准备识别任务
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task = loop.run_in_executor(
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None,
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analyze_demographics_with_qwen,
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save_path,
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qwen_model,
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analysis_prompt
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)
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tasks.append(task)
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temp_results.append({
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"filename": filename,
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"relative_path": f"results/{request_id}/{filename}",
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"score": float(scores_list[i]) if i < len(scores_list) else 0.0
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})
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# 等待所有任务完成
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if tasks:
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analysis_results = await asyncio.gather(*tasks)
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else:
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analysis_results = []
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# 组合结果
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final_results = []
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for i, item in enumerate(temp_results):
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item["analysis"] = analysis_results[i]
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final_results.append(item)
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return final_results
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# 运行异步任务
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# 注意：由于本函数被 FastAPI (异步环境) 中的同步或异步函数调用，
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# 如果上层是 async def，我们可以直接 await。
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# 但由于这个函数定义没有 async，且之前的调用是同步的，
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# 为了兼容性，我们需要检查当前是否在事件循环中。
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# 然而，查看 fastAPI_tarot.py，这个函数是在 async def segment_face 中被调用的。
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# 但它是作为普通函数被导入和调用的。
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# 为了不破坏现有签名，我们可以使用 asyncio.run() 或者在新循环中运行，
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# 但这在已经运行的 loop 中是不允许的。
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# 最佳方案：修改本函数为 async，并在 fastAPI_tarot.py 中 await 它。
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# 但这需要修改 fastAPI_tarot.py 的调用处。
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# 既然我们已经修改了 fastAPI_tarot.py，我们也可以顺便修改这里的签名。
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# 但为了稳妥，我们可以用一种 hack：
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# 如果在一个正在运行的 loop 中调用，我们必须返回 awaitable 或者使用 loop.run_until_complete (会报错)
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# 让我们先把这个函数改成 async，然后去修改 fastAPI_tarot.py 的调用。
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# 这是最正确的做法。
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pass # 占位，实际代码在下面
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async def process_face_segmentation_and_analysis_async(
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processor,
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image: Image.Image,
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prompt: str = "head",
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output_base_dir: str = "static/results",
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qwen_model: str = "qwen-vl-max",
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analysis_prompt: str = None
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) -> dict:
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# ... (同上逻辑，只是是 async)
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# 1. SAM3 推理
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inference_state = processor.set_image(image)
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output = processor.set_text_prompt(state=inference_state, prompt=prompt)
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masks, boxes, scores = output["masks"], output["boxes"], output["scores"]
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detected_count = len(masks)
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if detected_count == 0:
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return {
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"status": "success",
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"message": "未检测到目标",
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"detected_count": 0,
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"results": []
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}
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request_id = f"{int(time.time())}_{uuid.uuid4().hex[:8]}"
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output_dir = os.path.join(output_base_dir, request_id)
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os.makedirs(output_dir, exist_ok=True)
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vis_filename = f"seg_{uuid.uuid4().hex}.jpg"
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vis_path = os.path.join(output_dir, vis_filename)
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try:
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create_highlighted_visualization(image, masks, vis_path)
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full_vis_relative_path = f"results/{request_id}/{vis_filename}"
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except Exception as e:
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print(f"可视化生成失败: {e}")
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full_vis_relative_path = None
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if isinstance(boxes, torch.Tensor):
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boxes_np = boxes.cpu().numpy()
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else:
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boxes_np = boxes
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if isinstance(scores, torch.Tensor):
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scores_list = scores.tolist()
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else:
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scores_list = scores if isinstance(scores, list) else [float(scores)]
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loop = asyncio.get_event_loop()
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tasks = []
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results = []
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for i, box in enumerate(boxes_np):
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cropped_img = crop_head_with_padding(image, box, padding_ratio=0.1)
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filename = f"face_{i}.jpg"
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save_path = os.path.join(output_dir, filename)
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cropped_img.save(save_path)
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task = loop.run_in_executor(
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None,
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analyze_demographics_with_qwen,
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save_path,
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qwen_model,
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analysis_prompt
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)
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tasks.append(task)
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results.append({
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"filename": filename,
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"relative_path": f"results/{request_id}/{filename}",
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"score": float(scores_list[i]) if i < len(scores_list) else 0.0
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})
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if tasks:
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analysis_results = await asyncio.gather(*tasks)
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else:
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analysis_results = []
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for i, item in enumerate(results):
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item["analysis"] = analysis_results[i]
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return {
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"status": "success",
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"message": f"成功检测并分析 {detected_count} 个人脸",
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"detected_count": detected_count,
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"request_id": request_id,
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"full_visualization": full_vis_relative_path,
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"scores": scores_list,
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"results": results
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}
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# 保留旧的同步接口以兼容其他潜在调用者，但内部实现可能会有问题如果它在 loop 中运行
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# 既然我们主要关注 fastAPI_tarot.py，我们可以直接替换 process_face_segmentation_and_analysis
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# 或者让它只是一个 wrapper
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def process_face_segmentation_and_analysis(
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processor,
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image: Image.Image,
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prompt: str = "head",
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output_base_dir: str = "static/results",
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qwen_model: str = "qwen-vl-max",
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analysis_prompt: str = None
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) -> dict:
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"""
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同步版本 (保留以兼容)
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注意：如果在 async loop 中调用此函数，且此函数内部没有异步操作，则会阻塞 loop。
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如果需要异步并发，请使用 process_face_segmentation_and_analysis_async
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"""
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# 这里我们简单地复用逻辑，但去除异步部分，退化为串行
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# 1. SAM3 推理
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inference_state = processor.set_image(image)
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output = processor.set_text_prompt(state=inference_state, prompt=prompt)
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masks, boxes, scores = output["masks"], output["boxes"], output["scores"]
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detected_count = len(masks)
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if detected_count == 0:
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return {
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"status": "success",
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"message": "未检测到目标",
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"detected_count": 0,
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"results": []
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}
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request_id = f"{int(time.time())}_{uuid.uuid4().hex[:8]}"
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output_dir = os.path.join(output_base_dir, request_id)
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os.makedirs(output_dir, exist_ok=True)
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vis_filename = f"seg_{uuid.uuid4().hex}.jpg"
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vis_path = os.path.join(output_dir, vis_filename)
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try:
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create_highlighted_visualization(image, masks, vis_path)
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full_vis_relative_path = f"results/{request_id}/{vis_filename}"
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except Exception as e:
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print(f"可视化生成失败: {e}")
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full_vis_relative_path = None
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if isinstance(boxes, torch.Tensor):
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boxes_np = boxes.cpu().numpy()
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else:
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boxes_np = boxes
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if isinstance(scores, torch.Tensor):
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scores_list = scores.tolist()
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else:
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scores_list = scores if isinstance(scores, list) else [float(scores)]
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results = []
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for i, box in enumerate(boxes_np):
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cropped_img = crop_head_with_padding(image, box, padding_ratio=0.1)
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filename = f"face_{i}.jpg"
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save_path = os.path.join(output_dir, filename)
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cropped_img.save(save_path)
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# 同步调用
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analysis = analyze_demographics_with_qwen(save_path, model_name=qwen_model, prompt_template=analysis_prompt)
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results.append({
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"filename": filename,
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"relative_path": f"results/{request_id}/{filename}",
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"analysis": analysis,
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"score": float(scores_list[i]) if i < len(scores_list) else 0.0
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})
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return {
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"status": "success",
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"message": f"成功检测并分析 {detected_count} 个人脸",
|
||
"detected_count": detected_count,
|
||
"request_id": request_id,
|
||
"full_visualization": full_vis_relative_path,
|
||
"scores": scores_list,
|
||
"results": results
|
||
}
|
||
|