feat: 新增 account 和 plan 目录

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

account/admin/skills/image-understanding/scripts/image_understander.py

@@ -0,0 +1,177 @@
+"""
+图片理解模块 - 使用 Dashscope Qwen-VL 模型
+"""
+import os
+import base64
+import json
+import requests
+
+# 配置
+API_KEY = os.environ.get("DASHSCOPE_API_KEY", "")
+if not API_KEY:
+    print("错误: 请设置 DASHSCOPE_API_KEY 环境变量")
+    exit(1)
+
+BASE_URL = "https://dashscope.aliyuncs.com/api/v1/services/aigc/multimodal-generation/generation"
+
+
+def encode_image(image_path):
+    """将图片编码为base64"""
+    with open(image_path, "rb") as f:
+        return base64.b64encode(f.read()).decode("utf-8")
+
+
+def call_dashscope(messages, model="qwen-vl-plus", max_tokens=1000):
+    """调用Dashscope API"""
+    headers = {
+        "Authorization": f"Bearer {API_KEY}",
+        "Content-Type": "application/json"
+    }
+
+    payload = {
+        "model": model,
+        "input": {
+            "messages": messages
+        },
+        "parameters": {
+            "max_tokens": max_tokens
+        }
+    }
+
+    response = requests.post(BASE_URL, headers=headers, json=payload)
+    result = response.json()
+
+    if "output" in result and "choices" in result["output"]:
+        return result["output"]["choices"][0]["message"]["content"]
+    else:
+        return f"错误: {json.dumps(result, ensure_ascii=False, indent=2)}"
+
+
+def describe_image(image_path, model="qwen-vl-plus"):
+    """描述图片内容"""
+    image_b64 = encode_image(image_path)
+
+    messages = [
+        {
+            "role": "user",
+            "content": [
+                {
+                    "type": "text",
+                    "text": "请详细描述这张图片的内容，包括场景、人物、物体、颜色等所有细节。"
+                },
+                {
+                    "type": "image",
+                    "image": f"data:image/jpeg;base64,{image_b64}"
+                }
+            ]
+        }
+    ]
+
+    return call_dashscope(messages, model, max_tokens=1000)
+
+
+def extract_text(image_path, model="qwen-vl-plus"):
+    """提取图片中的文字 (OCR)"""
+    image_b64 = encode_image(image_path)
+
+    messages = [
+        {
+            "role": "user",
+            "content": [
+                {
+                    "type": "text",
+                    "text": "请提取图片中的所有文字，保持原格式，不要遗漏任何内容。"
+                },
+                {
+                    "type": "image",
+                    "image": f"data:image/jpeg;base64,{image_b64}"
+                }
+            ]
+        }
+    ]
+
+    return call_dashscope(messages, model, max_tokens=2000)
+
+
+def identify_objects(image_path, model="qwen-vl-plus"):
+    """识别图片中的物体"""
+    image_b64 = encode_image(image_path)
+
+    messages = [
+        {
+            "role": "user",
+            "content": [
+                {
+                    "type": "text",
+                    "text": "请列出图片中的所有物体、人物、元素，用清晰的列表格式。"
+                },
+                {
+                    "type": "image",
+                    "image": f"data:image/jpeg;base64,{image_b64}"
+                }
+            ]
+        }
+    ]
+
+    return call_dashscope(messages, model, max_tokens=500)
+
+
+def answer_question(image_path, question, model="qwen-vl-plus"):
+    """回答关于图片的问题"""
+    image_b64 = encode_image(image_path)
+
+    messages = [
+        {
+            "role": "user",
+            "content": [
+                {
+                    "type": "text",
+                    "text": question
+                },
+                {
+                    "type": "image",
+                    "image": f"data:image/jpeg;base64,{image_b64}"
+                }
+            ]
+        }
+    ]
+
+    return call_dashscope(messages, model, max_tokens=500)
+
+
+if __name__ == "__main__":
+    import sys
+
+    if len(sys.argv) < 3:
+        print("用法:")
+        print("  python image_understander.py -i <图片路径> -m describe   # 描述图片")
+        print("  python image_understander.py -i <图片路径> -m ocr        # 提取文字")
+        print("  python image_understander.py -i <图片路径> -m objects    # 识别物体")
+        print("  python image_understander.py -i <图片路径> -m qa -q '问题'  # 图片问答")
+        sys.exit(1)
+
+    image_path = sys.argv[2]
+
+    if "-m" in sys.argv:
+        mode = sys.argv[sys.argv.index("-m") + 1]
+    else:
+        mode = "describe"
+
+    if "-q" in sys.argv:
+        question = sys.argv[sys.argv.index("-q") + 1]
+    else:
+        question = None
+
+    if mode == "describe":
+        print(describe_image(image_path))
+    elif mode == "ocr":
+        print(extract_text(image_path))
+    elif mode == "objects":
+        print(identify_objects(image_path))
+    elif mode == "qa":
+        if question:
+            print(answer_question(image_path, question))
+        else:
+            print("错误: 请使用 -q 指定问题")
+    else:
+        print(f"未知模式: {mode}")