feat: publish ocr_utils, vision_sop, ui_detect to public repo

memory/ui_detect.py

@@ -0,0 +1,135 @@
+#!/usr/bin/env python3
+"""
+极简UI元素检测脚本 - 基于OmniParser的YOLO模型
+依赖: ultralytics, rapidocr-onnxruntime, pillow, numpy
+"""
+import sys
+from pathlib import Path
+from ultralytics import YOLO
+from PIL import Image, ImageDraw
+import numpy as np
+
+DEFAULT_MODEL = str(Path(__file__).resolve().parent.parent / 'temp' / 'weights' / 'icon_detect' / 'model.pt')
+
+# 可选：使用rapidocr做OCR
+try:
+    from rapidocr_onnxruntime import RapidOCR
+    ocr_engine = RapidOCR()
+    HAS_OCR = True
+except ImportError:
+    HAS_OCR = False
+    print("警告: rapidocr未安装，跳过OCR功能")
+
+def detect_ui_elements(image_path, model_path=None, conf_threshold=0.25):
+    model_path = model_path or DEFAULT_MODEL
+    """检测UI元素并返回边界框"""
+    # 加载模型
+    model = YOLO(model_path)
+    
+    # 推理
+    results = model(image_path, conf=conf_threshold, verbose=False)
+    
+    # 提取检测结果
+    detections = []
+    for result in results:
+        boxes = result.boxes
+        for box in boxes:
+            x1, y1, x2, y2 = box.xyxy[0].cpu().numpy()
+            conf = float(box.conf[0])
+            cls = int(box.cls[0])
+            detections.append({
+                'bbox': [int(x1), int(y1), int(x2), int(y2)],
+                'confidence': conf,
+                'class': cls
+            })
+    
+    return detections
+
+def ocr_text(image_path):
+    """OCR识别文本"""
+    if not HAS_OCR:
+        return []
+    
+    result, _ = ocr_engine(image_path)
+    if not result:
+        return []
+    
+    texts = []
+    for item in result:
+        bbox, text, conf = item
+        texts.append({
+            'text': text,
+            'bbox': bbox,
+            'confidence': conf
+        })
+    return texts
+
+def visualize(image_path, detections, ocr_results=None, output_path=None):
+    """可视化检测结果"""
+    img = Image.open(image_path)
+    draw = ImageDraw.Draw(img)
+    
+    # 画UI元素框（红色）
+    for det in detections:
+        x1, y1, x2, y2 = det['bbox']
+        draw.rectangle([x1, y1, x2, y2], outline='red', width=2)
+        draw.text((x1, y1-10), f"{det['confidence']:.2f}", fill='red')
+    
+    # 画OCR文本框（蓝色）
+    if ocr_results:
+        for ocr in ocr_results:
+            bbox = ocr['bbox']
+            points = [(bbox[i][0], bbox[i][1]) for i in range(4)]
+            draw.polygon(points, outline='blue')
+            draw.text((points[0][0], points[0][1]-10), ocr['text'][:10], fill='blue')
+    
+    if output_path:
+        img.save(output_path)
+    return img
+
+def main():
+    if len(sys.argv) < 2:
+        print("用法: python ui_detect.py <图片路径> <模型路径> [输出路径]")
+        print("示例: python ui_detect.py screenshot.png weights/icon_detect/model.pt output.png")
+        sys.exit(1)
+    
+    image_path = sys.argv[1]
+    model_path = sys.argv[2] if len(sys.argv) > 2 else DEFAULT_MODEL
+    output_path = sys.argv[3] if len(sys.argv) > 3 else "output.png"
+    
+    print(f"检测图片: {image_path}")
+    print(f"使用模型: {model_path}")
+    
+    # UI元素检测
+    print("\n[1/2] YOLO检测UI元素...")
+    detections = detect_ui_elements(image_path, model_path)
+    print(f"检测到 {len(detections)} 个UI元素")
+    for i, det in enumerate(detections, 1):
+        print(f"  {i}. bbox={det['bbox']}, conf={det['confidence']:.3f}")
+    
+    # OCR文本识别
+    ocr_results = None
+    if HAS_OCR:
+        print("\n[2/2] OCR识别文本...")
+        ocr_results = ocr_text(image_path)
+        print(f"识别到 {len(ocr_results)} 个文本区域")
+        for i, ocr in enumerate(ocr_results, 1):
+            print(f"  {i}. text='{ocr['text']}', conf={ocr['confidence']:.3f}")
+    
+    # 可视化
+    print(f"\n保存结果到: {output_path}")
+    visualize(image_path, detections, ocr_results, output_path)
+    
+    # 输出JSON格式结果
+    import json
+    result = {
+        'ui_elements': detections,
+        'ocr_texts': ocr_results or []
+    }
+    json_path = output_path.replace('.png', '.json')
+    with open(json_path, 'w', encoding='utf-8') as f:
+        json.dump(result, f, ensure_ascii=False, indent=2)
+    print(f"JSON结果: {json_path}")
+
+if __name__ == "__main__":
+    main()