|
|
@@ -0,0 +1,378 @@
|
|
|
+import os
|
|
|
+import json
|
|
|
+import cv2
|
|
|
+import numpy as np
|
|
|
+import random
|
|
|
+from dataclasses import dataclass, field
|
|
|
+from typing import Dict, List, Optional, Any, Tuple
|
|
|
+from collections import defaultdict
|
|
|
+
|
|
|
+def fry_algo_print(level_str: str, info_str: str):
|
|
|
+ print(f"[{level_str}] : {info_str}")
|
|
|
+
|
|
|
+
|
|
|
+def fry_cv2_imread(filename, flags=cv2.IMREAD_COLOR):
|
|
|
+ try:
|
|
|
+ with open(filename, 'rb') as f:
|
|
|
+ chunk = f.read()
|
|
|
+ chunk_arr = np.frombuffer(chunk, dtype=np.uint8)
|
|
|
+ img = cv2.imdecode(chunk_arr, flags)
|
|
|
+ if img is None:
|
|
|
+ fry_algo_print("警告", f"Warning: Unable to decode image: {filename}")
|
|
|
+ return img
|
|
|
+ except IOError as e:
|
|
|
+ fry_algo_print("错误", f"IOError: Unable to read file: {filename}")
|
|
|
+ fry_algo_print("错误", f"Error details: {str(e)}")
|
|
|
+ return None
|
|
|
+
|
|
|
+
|
|
|
+def fry_cv2_imwrite(filename, img, params=None):
|
|
|
+ try:
|
|
|
+ ext = os.path.splitext(filename)[1].lower()
|
|
|
+ result, encoded_img = cv2.imencode(ext, img, params)
|
|
|
+ if result:
|
|
|
+ with open(filename, 'wb') as f:
|
|
|
+ encoded_img.tofile(f)
|
|
|
+ return True
|
|
|
+ else:
|
|
|
+ fry_algo_print("警告", f"Warning: Unable to encode image: {filename}")
|
|
|
+ return False
|
|
|
+ except Exception as e:
|
|
|
+ fry_algo_print("错误", f"Error: Unable to write file: {filename}")
|
|
|
+ fry_algo_print("错误", f"Error details: {str(e)}")
|
|
|
+ return False
|
|
|
+
|
|
|
+
|
|
|
+def fry_opencv_Chinese_path_init():
|
|
|
+ cv2.imread = fry_cv2_imread
|
|
|
+ cv2.imwrite = fry_cv2_imwrite
|
|
|
+
|
|
|
+
|
|
|
+OPENCV_IO_ALREADY_INIT = False
|
|
|
+if not OPENCV_IO_ALREADY_INIT:
|
|
|
+ fry_opencv_Chinese_path_init()
|
|
|
+ OPENCV_IO_ALREADY_INIT = True
|
|
|
+
|
|
|
+
|
|
|
+def to_json_serializable(obj):
|
|
|
+ if isinstance(obj, (np.ndarray,)): return obj.tolist()
|
|
|
+ if isinstance(obj, (np.integer,)): return int(obj)
|
|
|
+ if isinstance(obj, (np.floating,)): return float(obj)
|
|
|
+ if hasattr(obj, 'to_dict'): return obj.to_dict()
|
|
|
+ try:
|
|
|
+ return json.dumps(obj)
|
|
|
+ except TypeError:
|
|
|
+ return str(obj)
|
|
|
+
|
|
|
+@dataclass
|
|
|
+class DefectInfo:
|
|
|
+ """单个缺陷的详细信息"""
|
|
|
+ label: str
|
|
|
+ pixel_area: float
|
|
|
+ actual_area: float # 平方毫米
|
|
|
+ width: float # 毫米
|
|
|
+ height: float # 毫米
|
|
|
+ contour: List[List[int]]
|
|
|
+ min_rect: Tuple[Tuple[float, float], Tuple[float, float], float] # 最小外接矩形
|
|
|
+
|
|
|
+ def to_dict(self) -> Dict[str, Any]:
|
|
|
+ return {
|
|
|
+ "label": self.label,
|
|
|
+ "pixel_area": self.pixel_area,
|
|
|
+ "actual_area": self.actual_area,
|
|
|
+ "width": self.width,
|
|
|
+ "height": self.height,
|
|
|
+ "contour": self.contour,
|
|
|
+ "min_rect": self.min_rect
|
|
|
+ }
|
|
|
+
|
|
|
+
|
|
|
+@dataclass
|
|
|
+class AnalysisResult:
|
|
|
+ """封装单次分析的所有结果，包括缺陷列表和统计信息"""
|
|
|
+ defects: List[DefectInfo] = field(default_factory=list)
|
|
|
+ total_defect_count: int = 0
|
|
|
+ total_pixel_area = float = 0.0
|
|
|
+ total_defect_area: float = 0.0 # 所有缺陷的总面积 (mm^2)
|
|
|
+ area_by_label: Dict[str, float] = field(default_factory=lambda: defaultdict(float))
|
|
|
+ count_by_label: Dict[str, int] = field(default_factory=lambda: defaultdict(int))
|
|
|
+
|
|
|
+ def to_dict(self) -> Dict[str, Any]:
|
|
|
+ return {
|
|
|
+ "defects": [d.to_dict() for d in self.defects],
|
|
|
+ "statistics": {
|
|
|
+ "total_defect_count": self.total_defect_count,
|
|
|
+ "total_pixel_area": self.total_pixel_area,
|
|
|
+ "total_defect_area_mm2": self.total_defect_area,
|
|
|
+ "area_by_label_mm2": dict(self.area_by_label),
|
|
|
+ "count_by_label": dict(self.count_by_label)
|
|
|
+ }
|
|
|
+ }
|
|
|
+
|
|
|
+
|
|
|
+@dataclass
|
|
|
+class DrawingParams:
|
|
|
+ """封装所有绘图相关的参数"""
|
|
|
+ draw_min_rect: bool = True
|
|
|
+ font_scale: float = 0.5
|
|
|
+ font_thickness: int = 1
|
|
|
+ contour_thickness: int = 1
|
|
|
+ rect_thickness: int = 1
|
|
|
+ info_bg_alpha: float = 0.5
|
|
|
+ contour_color: Tuple[int, int, int] = (0, 255, 0)
|
|
|
+ rect_color: Tuple[int, int, int] = (255, 0, 0)
|
|
|
+ text_color: Tuple[int, int, int] = (255, 255, 255)
|
|
|
+ bg_color: Tuple[int, int, int] = (0, 0, 0)
|
|
|
+
|
|
|
+
|
|
|
+class DefectVisualizer:
|
|
|
+ """一个专门用于在图像上可视化缺陷信息的类。"""
|
|
|
+
|
|
|
+ def __init__(self, params: DrawingParams):
|
|
|
+ self.params = params
|
|
|
+
|
|
|
+ def draw_defects_on_image(self, image: np.ndarray, defects: List[DefectInfo]) -> np.ndarray:
|
|
|
+ vis_image = image.copy()
|
|
|
+ for i, defect in enumerate(defects):
|
|
|
+ self._draw_single_defect(vis_image, defect, i)
|
|
|
+ return vis_image
|
|
|
+
|
|
|
+ def _draw_single_defect(self, image: np.ndarray, defect: DefectInfo, idx: int):
|
|
|
+ contour = np.array(defect.contour, dtype=np.int32)
|
|
|
+ cv2.drawContours(image, [contour], -1, self.params.contour_color, self.params.contour_thickness)
|
|
|
+ if self.params.draw_min_rect:
|
|
|
+ box = np.intp(cv2.boxPoints(defect.min_rect))
|
|
|
+ cv2.drawContours(image, [box], 0, self.params.rect_color, self.params.rect_thickness)
|
|
|
+ info_text = [
|
|
|
+ f"L: {defect.label}", f"A: {defect.actual_area:.3f} mm2",
|
|
|
+ f"W: {defect.width:.3f} mm", f"H: {defect.height:.3f} mm"
|
|
|
+ ]
|
|
|
+ M = cv2.moments(contour)
|
|
|
+ cx = int(M["m10"] / M["m00"]) if M["m00"] != 0 else contour[0][0][0]
|
|
|
+ cy = int(M["m01"] / M["m00"]) if M["m00"] != 0 else contour[0][0][1]
|
|
|
+ cx += random.randint(-30, 10);
|
|
|
+ cy += random.randint(-30, 10)
|
|
|
+ text_size, _ = cv2.getTextSize(info_text[0], cv2.FONT_HERSHEY_SIMPLEX, self.params.font_scale,
|
|
|
+ self.params.font_thickness)
|
|
|
+ cx = max(10, min(cx, image.shape[1] - text_size[0] - 10))
|
|
|
+ cy = max(text_size[1] * len(info_text) + 10, min(cy, image.shape[0] - 10))
|
|
|
+ y_offset = cy - (text_size[1] + 10) * (len(info_text) - 1)
|
|
|
+ for text in info_text:
|
|
|
+ self._draw_text_with_background(image, text, (cx, y_offset))
|
|
|
+ y_offset += text_size[1] + 10
|
|
|
+
|
|
|
+ def _draw_text_with_background(self, image: np.ndarray, text: str, position: Tuple[int, int]):
|
|
|
+ text_size, _ = cv2.getTextSize(text, cv2.FONT_HERSHEY_SIMPLEX, self.params.font_scale,
|
|
|
+ self.params.font_thickness)
|
|
|
+ x, y = position
|
|
|
+ overlay = image.copy()
|
|
|
+ cv2.rectangle(overlay, (x - 2, y - text_size[1] - 2), (x + text_size[0] + 2, y + 2), self.params.bg_color, -1)
|
|
|
+ cv2.addWeighted(overlay, self.params.info_bg_alpha, image, 1 - self.params.info_bg_alpha, 0, image)
|
|
|
+ cv2.putText(image, text, (x, y), cv2.FONT_HERSHEY_SIMPLEX, self.params.font_scale, self.params.text_color,
|
|
|
+ self.params.font_thickness, cv2.LINE_AA)
|
|
|
+
|
|
|
+
|
|
|
+class DefectProcessor:
|
|
|
+ """
|
|
|
+ 缺陷处理器，专注于单次分析和可视化任务。
|
|
|
+ 此类是无状态的，不处理批处理，使其功能更专一、更易于测试和复用。
|
|
|
+ """
|
|
|
+
|
|
|
+ def __init__(self, pixel_resolution: float):
|
|
|
+ """
|
|
|
+ 初始化处理器。
|
|
|
+ Args:
|
|
|
+ pixel_resolution (float): 像素分辨率，单位是 μm/pixel。
|
|
|
+ """
|
|
|
+ if pixel_resolution <= 0:
|
|
|
+ raise ValueError("Pixel resolution must be a positive number.")
|
|
|
+ self.pixel_to_mm = pixel_resolution / 1000.0
|
|
|
+
|
|
|
+ @staticmethod
|
|
|
+ def _calculate_metrics(contour: np.ndarray, pixel_to_mm: float) -> Tuple[float, float, float, float, Any]:
|
|
|
+ """静态辅助方法，计算单个轮廓的各项指标。"""
|
|
|
+ pixel_area = cv2.contourArea(contour)
|
|
|
+ actual_area = pixel_area * (pixel_to_mm ** 2)
|
|
|
+ min_rect = cv2.minAreaRect(contour)
|
|
|
+ # 确保宽度总是较小的一边
|
|
|
+ width_pixels, height_pixels = sorted(min_rect[1])
|
|
|
+ width, height = width_pixels * pixel_to_mm, height_pixels * pixel_to_mm
|
|
|
+ return pixel_area, actual_area, width, height, min_rect
|
|
|
+
|
|
|
+ def analyze_from_json(self, json_data: Dict[str, Any]) -> AnalysisResult:
|
|
|
+ """
|
|
|
+ [需求 1] 仅根据JSON数据计算缺陷面积并统计，返回包含详细信息的JSON友好对象。
|
|
|
+
|
|
|
+ Args:
|
|
|
+ json_data (Dict[str, Any]): 从labelme JSON文件加载的字典数据。
|
|
|
+
|
|
|
+ Returns:
|
|
|
+ AnalysisResult: 包含所有缺陷信息和统计结果的数据对象。
|
|
|
+ """
|
|
|
+ if not json_data or 'shapes' not in json_data:
|
|
|
+ return AnalysisResult()
|
|
|
+
|
|
|
+ result = AnalysisResult()
|
|
|
+
|
|
|
+ for shape in json_data['shapes']:
|
|
|
+ label = shape.get('label', 'unlabeled')
|
|
|
+ points = shape.get('points')
|
|
|
+ if not points:
|
|
|
+ continue
|
|
|
+
|
|
|
+ contour = np.array(points, dtype=np.int32)
|
|
|
+ pixel_area, actual_area, width, height, min_rect = self._calculate_metrics(contour, self.pixel_to_mm)
|
|
|
+
|
|
|
+ defect = DefectInfo(
|
|
|
+ label=label,
|
|
|
+ pixel_area=pixel_area,
|
|
|
+ actual_area=actual_area,
|
|
|
+ width=width,
|
|
|
+ height=height,
|
|
|
+ contour=contour.tolist(),
|
|
|
+ min_rect=min_rect
|
|
|
+ )
|
|
|
+ result.defects.append(defect)
|
|
|
+
|
|
|
+ # 更新统计信息
|
|
|
+ result.total_defect_count += 1
|
|
|
+ result.total_pixel_area += pixel_area
|
|
|
+ result.total_defect_area += actual_area
|
|
|
+ result.count_by_label[label] += 1
|
|
|
+ result.area_by_label[label] += actual_area
|
|
|
+
|
|
|
+ return result
|
|
|
+
|
|
|
+ def analyze_and_draw(self, image: np.ndarray, json_data: Dict[str, Any], drawing_params: DrawingParams) -> Tuple[
|
|
|
+ np.ndarray, AnalysisResult]:
|
|
|
+ """
|
|
|
+ [需求 2] 输入图片和JSON数据，返回绘制好的图片和分析结果。
|
|
|
+
|
|
|
+ Args:
|
|
|
+ image (np.ndarray): OpenCV格式的BGR图像。
|
|
|
+ json_data (Dict[str, Any]): 从labelme JSON文件加载的字典数据。
|
|
|
+ drawing_params (DrawingParams): 控制绘图样式的参数对象。
|
|
|
+
|
|
|
+ Returns:
|
|
|
+ Tuple[np.ndarray, AnalysisResult]:
|
|
|
+ - 绘制了缺陷信息的新图像。
|
|
|
+ - 包含所有缺陷信息和统计结果的数据对象。
|
|
|
+ """
|
|
|
+ # 1. 首先，执行纯JSON分析以获取所有计算结果
|
|
|
+ analysis_result = self.analyze_from_json(json_data)
|
|
|
+
|
|
|
+ # 2. 如果没有缺陷，直接返回原图和分析结果
|
|
|
+ if not analysis_result.defects:
|
|
|
+ return image, analysis_result
|
|
|
+
|
|
|
+ # 3. 使用DefectVisualizer进行绘图
|
|
|
+ visualizer = DefectVisualizer(drawing_params)
|
|
|
+ drawn_image = visualizer.draw_defects_on_image(image, analysis_result.defects)
|
|
|
+
|
|
|
+ return drawn_image, analysis_result
|
|
|
+
|
|
|
+
|
|
|
+def run_json_only_analysis_example(json_path: str, output_json_path: str):
|
|
|
+ """示例1: 演示如何仅使用JSON文件进行分析。"""
|
|
|
+ fry_algo_print("重要", f"--- 场景1: 仅JSON分析 ---")
|
|
|
+
|
|
|
+ # 1. 加载JSON数据
|
|
|
+ try:
|
|
|
+ with open(json_path, 'r', encoding='utf-8') as f:
|
|
|
+ labelme_data = json.load(f)
|
|
|
+ except Exception as e:
|
|
|
+ fry_algo_print("错误", f"无法加载JSON文件 '{json_path}': {e}")
|
|
|
+ return
|
|
|
+
|
|
|
+ # 2. 初始化处理器并执行分析
|
|
|
+ processor = DefectProcessor(pixel_resolution=24.54)
|
|
|
+ analysis_result = processor.analyze_from_json(labelme_data)
|
|
|
+
|
|
|
+ # 3. 打印统计结果
|
|
|
+ fry_algo_print("信息", f"分析完成: {os.path.basename(json_path)}")
|
|
|
+ stats = analysis_result.to_dict()["statistics"]
|
|
|
+ print(json.dumps(stats, indent=2, ensure_ascii=False))
|
|
|
+
|
|
|
+ # 4. 将完整结果保存为新的JSON文件
|
|
|
+ with open(output_json_path, 'w', encoding='utf-8') as f:
|
|
|
+ json.dump(analysis_result.to_dict(), f, ensure_ascii=False, indent=2, default=to_json_serializable)
|
|
|
+ fry_algo_print("成功", f"详细分析结果已保存到: {output_json_path}")
|
|
|
+
|
|
|
+
|
|
|
+def run_image_and_json_analysis_example(image_path: str, json_path: str, output_dir: str):
|
|
|
+ """示例2: 演示如何结合图像和JSON进行分析与绘图。"""
|
|
|
+ fry_algo_print("重要", f"--- 场景2: 图像与JSON结合分析和绘图 ---")
|
|
|
+
|
|
|
+ # 1. 加载图像和JSON数据
|
|
|
+ image = cv2.imread(image_path)
|
|
|
+ if image is None:
|
|
|
+ fry_algo_print("错误", f"无法加载图片: {image_path}")
|
|
|
+ return
|
|
|
+
|
|
|
+ try:
|
|
|
+ with open(json_path, 'r', encoding='utf-8') as f:
|
|
|
+ labelme_data = json.load(f)
|
|
|
+ except Exception as e:
|
|
|
+ fry_algo_print("错误", f"无法加载JSON文件 '{json_path}': {e}")
|
|
|
+ return
|
|
|
+
|
|
|
+ # 2. 初始化处理器
|
|
|
+ processor = DefectProcessor(pixel_resolution=24.54)
|
|
|
+
|
|
|
+ # --- 2a. 测试绘制最小外接矩形 ---
|
|
|
+ fry_algo_print("信息", "子场景 2a: 绘制最小外接矩形")
|
|
|
+ drawing_params_with_rect = DrawingParams(draw_min_rect=True)
|
|
|
+ drawn_image_rect, result_rect = processor.analyze_and_draw(image, labelme_data, drawing_params_with_rect)
|
|
|
+
|
|
|
+ # 保存结果
|
|
|
+ base_name = os.path.splitext(os.path.basename(image_path))[0]
|
|
|
+ output_image_path_rect = os.path.join(output_dir, f"{base_name}_with_rect.jpg")
|
|
|
+ output_json_path_rect = os.path.join(output_dir, f"{base_name}_with_rect_results.json")
|
|
|
+
|
|
|
+ cv2.imwrite(output_image_path_rect, drawn_image_rect)
|
|
|
+ with open(output_json_path_rect, 'w', encoding='utf-8') as f:
|
|
|
+ json.dump(result_rect.to_dict(), f, ensure_ascii=False, indent=2, default=to_json_serializable)
|
|
|
+
|
|
|
+ fry_algo_print("成功", f"带矩形的图片已保存到: {output_image_path_rect}")
|
|
|
+ fry_algo_print("成功", f"对应的分析结果已保存到: {output_json_path_rect}")
|
|
|
+
|
|
|
+ # --- 2b. 测试不绘制最小外接矩形 ---
|
|
|
+ # fry_algo_print("信息", "子场景 2b: 不绘制最小外接矩形")
|
|
|
+ # drawing_params_no_rect = DrawingParams(draw_min_rect=False)
|
|
|
+ # drawn_image_no_rect, result_no_rect = processor.analyze_and_draw(image, labelme_data, drawing_params_no_rect)
|
|
|
+ #
|
|
|
+ # # 保存结果
|
|
|
+ # output_image_path_no_rect = os.path.join(output_dir, f"{base_name}_no_rect.png")
|
|
|
+ # output_json_path_no_rect = os.path.join(output_dir, f"{base_name}_no_rect_results.json")
|
|
|
+ #
|
|
|
+ # cv2.imwrite(output_image_path_no_rect, drawn_image_no_rect)
|
|
|
+ # # 注意：分析结果 `result_no_rect` 和 `result_rect` 是一样的，因为分析和绘图是分离的
|
|
|
+ # with open(output_json_path_no_rect, 'w', encoding='utf-8') as f:
|
|
|
+ # json.dump(result_no_rect.to_dict(), f, ensure_ascii=False, indent=2, default=to_json_serializable)
|
|
|
+ #
|
|
|
+ # fry_algo_print("成功", f"不带矩形的图片已保存到: {output_image_path_no_rect}")
|
|
|
+
|
|
|
+
|
|
|
+if __name__ == "__main__":
|
|
|
+ image_file_path = r"C:\Code\ML\Project\卡片缺陷检测项目组\计算边角缺陷大小\测试数据\250805_pokemon_0001.jpg"
|
|
|
+ json_file_path = r"C:\Code\ML\Project\卡片缺陷检测项目组\计算边角缺陷大小\测试数据\250805_pokemon_0001.json"
|
|
|
+
|
|
|
+ output_dir = r"C:\Code\ML\Project\卡片缺陷检测项目组\计算边角缺陷大小\测试数据_my"
|
|
|
+ os.makedirs(output_dir, exist_ok=True)
|
|
|
+
|
|
|
+ # 1. 仅JSON分析
|
|
|
+ # run_json_only_analysis_example(
|
|
|
+ # json_path=json_file_path,
|
|
|
+ # output_json_path=os.path.join(output_dir, "json_only_analysis_result.json")
|
|
|
+ # )
|
|
|
+ #
|
|
|
+ # print("\n" + "=" * 50 + "\n")
|
|
|
+
|
|
|
+ # 2. 图像和JSON结合分析
|
|
|
+ run_image_and_json_analysis_example(
|
|
|
+ image_path=image_file_path,
|
|
|
+ json_path=json_file_path,
|
|
|
+ output_dir=output_dir
|
|
|
+ )
|
|
|
+
|
|
|
+ fry_algo_print("重要", "所有示例运行完毕！")
|
AnlaAnla