330 lines
12 KiB
Python
330 lines
12 KiB
Python
#!/usr/bin/env python3
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"""
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提示词迭代测试脚本
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直接调用视觉模型测试 identify_pieces 的提示词效果
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"""
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import base64, json, os, sys
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# 加载环境
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sys.path.insert(0, os.path.dirname(__file__))
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from dotenv import load_dotenv
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load_dotenv()
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from openai import OpenAI
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from app.core.config import settings
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# ==================== 配置 ====================
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GARMENT_IMG = "debug_images/1770525898_input_0.jpg" # 成衣照片
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CANVAS_IMG = "debug_images/1770525898_input_1.jpg" # 裁片轮廓
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LAYERS_INFO = """组 1 (group, 4120x5280px, 位于路径: /组 1)
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图层 1 (LayerKind.NORMAL, 4120x5280px, 位于路径: /组 1/图层 1)
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组 2 (group, 3999x4561px, 位于路径: /组 2)
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图层 2 (LayerKind.NORMAL, 3999x4561px, 位于路径: /组 2/图层 2)
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组 3 (group, 3529x333px, 位于路径: /组 3)
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图层 3 (LayerKind.NORMAL, 3529x333px, 位于路径: /组 3/图层 3)
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组 4 (group, 2423x2004px, 位于路径: /组 4)
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图层 4 (LayerKind.NORMAL, 2423x2004px, 位于路径: /组 4/图层 4)
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组 5 (group, 2422x2003px, 位于路径: /组 5)
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图层 1 拷贝 (LayerKind.NORMAL, 2422x2003px, 位于路径: /组 5/图层 1 拷贝)"""
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# ==================== 提示词版本 ====================
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def make_prompt_v2(layers_info: str) -> str:
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"""v2 提示词：新增 pattern_mode + 不可见部位推理"""
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# 动态提取图层名
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layer_names = []
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for line in layers_info.strip().split('\n'):
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name = line.split('(')[0].strip()
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if name:
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layer_names.append(name)
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ex1 = layer_names[0] if len(layer_names) > 0 else "图层名1"
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ex2 = layer_names[1] if len(layer_names) > 1 else "图层名2"
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ex3 = layer_names[2] if len(layer_names) > 2 else "图层名3"
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return f"""你需要完成三个任务：
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**任务1：识别裁片部位**
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Image 2 是 PS 文档中的裁片轮廓图。以下是图层信息：
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{layers_info}
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请根据形状、大小识别每个**图层组**是什么服装部位（前片、后片、袖子、领口等）。
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⚠️ mapping 的 key 必须是上面图层信息中的**实际图层组名称**（如 "{ex1}"、"{ex2}" 等），不要自己编名字！
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**任务2：判断花型覆盖模式 pattern_mode**
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Image 1 是成衣照片。请判断这件衣服的花型属于哪种模式：
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- "all_over"：所有裁片来自同一块面料（碎花/格子/渐变等整体花型覆盖全身）
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- "placement"：各裁片独立处理（有的印花有的纯色，如卡通卫衣）
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- "color_block"：全部纯色拼接
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如果是 all_over，还需要提供 fabric_info：
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- fabric_type: 面料印花类型（数码印花/提花/丝网印/...）
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- pattern_direction: 花型方向（均匀铺满/上下渐变/左右渐变/斜向）
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- pattern_elements: 花型元素描述
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- color_transition: 色彩过渡描述
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- density: 花型密度描述
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**任务3：分析每个裁片的图案类型**
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对每个裁片分析：
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- type: solid / fill_pattern / theme_pattern / mixed_pattern / all_over
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- solid 和 theme_pattern 必须给 color（hex值）
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- 如果某个裁片在照片中**看不到**（如后片），设置 visible: false，并根据前片推理：
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- 全身花型 → 后片与前片相同
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- 局部印花 → 后片通常为底色纯色
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- 给出推理理由 inference_reason 和置信度 confidence（high/medium/low）
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- 如果左右对称（如左右袖），标记 mirror_of 字段
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用 JSON 回答（注意：key 用实际图层名）：
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```json
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{{
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"pattern_mode": "all_over",
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"fabric_info": {{
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"fabric_type": "数码印花",
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"pattern_direction": "上下渐变",
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"pattern_elements": "白色百合花朵 + 灰白格纹底",
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"color_transition": "从黑色渐变到灰白色",
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"density": "上部花朵密集大朵，下部稀疏小朵"
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}},
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"mapping": {{
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"{ex1}": "左袖",
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"{ex2}": "前片"
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}},
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"analysis": [
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{{"layer": "{ex1}", "piece": "左袖", "type": "all_over", "visible": true, "description": "黑底白花渐变"}},
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{{"layer": "{ex2}", "piece": "前片", "type": "all_over", "visible": true, "description": "上半黑底大花+下半灰白格纹"}},
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{{"layer": "{ex3}", "piece": "后片", "type": "all_over", "visible": false, "inferred_from": "{ex2}", "inference_reason": "全身花型面料，后片花型与前片相同", "confidence": "high"}}
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],
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"symmetric_pairs": [["{ex1}", "其他对称片"]],
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"base_color": "#1A1A1A"
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}}
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```
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⚠️ 重要：
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- mapping 和 analysis 中的 layer 必须用**实际图层组名称**
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- 看不到的部位设 visible: false 并说明推理依据
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- 左右对称的片标记 symmetric_pairs
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只返回 JSON，不要其他文字。"""
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# ==================== 调用模型 ====================
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def call_vision(prompt: str, garment_b64: str, canvas_b64: str, model: str = None):
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"""调用视觉模型"""
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use_model = model or settings.AI_VISION_MODEL
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client = OpenAI(
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api_key=settings.AI_API_KEY,
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base_url=settings.AI_BASE_URL or "https://api.openai.com/v1",
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)
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content = [
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{"type": "image_url", "image_url": {"url": f"data:image/jpeg;base64,{garment_b64}"}},
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{"type": "image_url", "image_url": {"url": f"data:image/jpeg;base64,{canvas_b64}"}},
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{"type": "text", "text": prompt},
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]
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print(f"\n{'='*60}")
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print(f"模型: {use_model}")
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print(f"提示词长度: {len(prompt)} 字符")
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print(f"{'='*60}\n")
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completion = client.chat.completions.create(
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model=use_model,
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messages=[{"role": "user", "content": content}],
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)
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return completion.choices[0].message.content or ""
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def parse_result(raw: str) -> dict:
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"""解析 JSON 结果"""
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clean = raw.strip()
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if "```" in clean:
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clean = clean.split("```")[1]
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if clean.startswith("json"):
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clean = clean[4:]
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clean = clean.strip()
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try:
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return json.loads(clean)
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except json.JSONDecodeError as e:
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print(f"⚠️ JSON 解析失败: {e}")
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print(f"原始回复:\n{raw[:500]}")
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return {}
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def evaluate_result(result: dict) -> list:
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"""评估结果质量，返回问题列表"""
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issues = []
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if not result:
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issues.append("❌ 解析失败，无结果")
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return issues
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# 检查 pattern_mode
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mode = result.get("pattern_mode")
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if not mode:
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issues.append("❌ 缺少 pattern_mode")
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elif mode not in ("all_over", "placement", "color_block"):
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issues.append(f"⚠️ pattern_mode 值异常: {mode}")
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# 检查 mapping 是否用了真实图层名
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mapping = result.get("mapping", {})
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for key in mapping:
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if key.startswith("M-") or key.startswith("Layer"):
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issues.append(f"❌ mapping key '{key}' 不是真实图层名（应为 '组 X' 格式）")
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# 检查 analysis
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analysis = result.get("analysis", [])
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if not analysis:
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issues.append("❌ 缺少 analysis")
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else:
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has_invisible = any(a.get("visible") == False for a in analysis)
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if not has_invisible:
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issues.append("⚠️ 没有标记不可见部位（后片通常看不到）")
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has_mirror = any("mirror_of" in a for a in analysis)
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symmetric = result.get("symmetric_pairs", [])
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if not has_mirror and not symmetric:
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issues.append("⚠️ 没有标记对称片（左右袖应该对称）")
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for a in analysis:
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if a.get("type") in ("solid", "theme_pattern") and not a.get("color"):
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issues.append(f"⚠️ {a.get('layer')} 类型为 {a['type']} 但缺少 color")
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# 检查 fabric_info（仅 all_over）
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if mode == "all_over":
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fi = result.get("fabric_info")
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if not fi:
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issues.append("❌ all_over 模式缺少 fabric_info")
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else:
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for key in ("fabric_type", "pattern_direction", "pattern_elements", "color_transition", "density"):
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if not fi.get(key):
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issues.append(f"⚠️ fabric_info 缺少 {key}")
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if not issues:
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issues.append("✅ 完美！所有字段齐全")
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return issues
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# ==================== 主流程 ====================
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def main():
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# 修复 Windows GBK 输出
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import io
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sys.stdout = io.TextIOWrapper(sys.stdout.buffer, encoding='utf-8', errors='replace')
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# 加载测试图片
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print("[1] Loading test images...")
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with open(GARMENT_IMG, "rb") as f:
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garment_b64 = base64.b64encode(f.read()).decode()
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with open(CANVAS_IMG, "rb") as f:
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canvas_b64 = base64.b64encode(f.read()).decode()
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print(f" garment: {len(garment_b64)//1024}KB, canvas: {len(canvas_b64)//1024}KB")
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# 生成提示词
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prompt = make_prompt_v2(LAYERS_INFO)
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# 调用模型
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print("\n[2] Calling vision model...")
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raw = call_vision(prompt, garment_b64, canvas_b64)
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# 解析
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print("\n[3] Model response:")
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print(raw[:2000])
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print("\n" + "="*60)
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result = parse_result(raw)
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if result:
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print("\n[4] Parsed result:")
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print(json.dumps(result, ensure_ascii=False, indent=2))
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# 评估
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print("\n[5] Quality check:")
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issues = evaluate_result(result)
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for issue in issues:
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print(f" {issue}")
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# ==================== 阶段 2: Gemini 生图 ====================
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if result and result.get("pattern_mode") == "all_over" and result.get("fabric_info"):
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print("\n" + "="*60)
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print("[6] Testing Gemini image generation (all_over fabric)...")
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fi = result["fabric_info"]
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gen_prompt = (
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f"Please generate a high-resolution flat fabric pattern image based on this garment photo.\n"
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f"\n"
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f"Requirements:\n"
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f"1. Output a RECTANGULAR flat fabric image (not garment shape, just the fabric laid flat)\n"
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f"2. Fabric type: {fi.get('fabric_type', 'digital print')}\n"
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f"3. Pattern elements: {fi.get('pattern_elements', 'floral')}\n"
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f"4. Pattern direction: {fi.get('pattern_direction', 'uniform')}\n"
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f"5. Color transition: {fi.get('color_transition', 'none')}\n"
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f"6. Pattern density: {fi.get('density', 'medium')}\n"
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f"7. Remove all garment wrinkles/shadows - show flat fabric only\n"
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f"8. The pattern must be clear, colors accurate, suitable for garment piece overlay\n"
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f"9. Aspect ratio: 3:4\n"
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f"10. The image should look like a piece of fabric photographed from directly above on a flat surface"
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)
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print(f" Prompt: {gen_prompt[:200]}...")
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print(f" Model: gemini-3-pro-image-preview")
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# 用 Gemini OpenAI 兼容方式生图
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from openai import OpenAI as GeminiClient
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gemini = GeminiClient(
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api_key=settings.GEMINI_API_KEY,
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base_url=f"{settings.GEMINI_BASE_URL}/v1",
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)
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content_parts = [
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{"type": "text", "text": gen_prompt},
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{"type": "image_url", "image_url": {"url": f"data:image/jpeg;base64,{garment_b64}"}},
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]
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print(" Calling Gemini 3 Pro Image...")
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import time
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t0 = time.time()
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completion = gemini.chat.completions.create(
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model="gemini-3-pro-image-preview",
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messages=[{"role": "user", "content": content_parts}],
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)
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elapsed = time.time() - t0
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resp_content = completion.choices[0].message.content or ""
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print(f" Response length: {len(resp_content)} chars, time: {elapsed:.1f}s")
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# 提取图片
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import re
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match = re.search(r'!\[.*?\]\((data:image/(\w+);base64,([^)]+))\)', resp_content)
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if not match:
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match = re.search(r'(data:image/(\w+);base64,([A-Za-z0-9+/=]+))', resp_content)
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if match:
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img_format = match.group(2)
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img_b64 = match.group(3)
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# 修复 padding
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padding = 4 - len(img_b64) % 4
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if padding != 4:
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img_b64 += '=' * padding
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# 保存
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out_path = f"debug_images/test_fabric_allover.{img_format}"
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with open(out_path, "wb") as f:
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f.write(base64.b64decode(img_b64))
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print(f" [OK] Fabric image saved: {out_path} ({len(img_b64)//1024}KB)")
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else:
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print(f" [FAIL] No image in response")
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if resp_content:
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print(f" Response preview: {resp_content[:300]}")
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print(f"\n{'='*60}")
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print("Done.")
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if __name__ == "__main__":
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main()
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