yangy
90aec37461
- Created .env.example for environment variable configuration. - Added docker-compose.yml for service orchestration. - Implemented frequency response extraction in extract_frequency_response.py and convert_to_frequency_db.py. - Generated output files: frequency_response_detailed.json, frequency_response_points.json, frequency_response.csv, and frequency_response_curve.png. - Included sample measurement data for FiiO FA19 in CSV format.
121 lines
3.8 KiB
Python
121 lines
3.8 KiB
Python
import cv2
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import numpy as np
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import matplotlib.pyplot as plt
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import json
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# 读取图片
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img_path = r"C:\Users\yangy\.cursor\projects\h-soft-projects-luxsin-dashboard/assets/c__Users_yangy_AppData_Roaming_Cursor_User_workspaceStorage_b134a9df77916b35c1e5b1ece8dc14fe_images_Arcona-avg-0b78da44-4aaa-465b-9b2b-fba051a28742.png"
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img = cv2.imread(img_path)
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img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
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img_copy = img.copy()
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gray = cv2.cvtColor(img_copy, cv2.COLOR_RGB2GRAY)
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print(f"图片尺寸：{img_copy.shape}")
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height, width = gray.shape
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# 定义图表区域（排除顶部标题和底部图例）
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# 根据图片估算：顶部约 5%，底部约 8%
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chart_top = int(height * 0.05)
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chart_bottom = int(height * 0.92)
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chart_left = int(width * 0.02)
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chart_right = int(width * 0.98)
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# 裁剪出图表区域
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chart_roi = gray[chart_top:chart_bottom, chart_left:chart_right]
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# 使用阈值提取白色线条
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_, thresh = cv2.threshold(chart_roi, 200, 255, cv2.THRESH_BINARY)
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# 形态学操作，连接断开的线条
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kernel = np.ones((3,3), np.uint8)
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dilated_thresh = cv2.dilate(thresh, kernel, iterations=2)
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eroded_thresh = cv2.erode(dilated_thresh, kernel, iterations=1)
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# 查找轮廓
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contours, _ = cv2.findContours(eroded_thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
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# 找到最大的连续轮廓（应该是频响曲线）
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largest_contour = None
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max_area = 0
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for contour in contours:
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area = cv2.contourArea(contour)
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if area > max_area:
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max_area = area
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largest_contour = contour
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print(f"最大轮廓面积：{max_area}")
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# 提取频响曲线上的点
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frequency_points = []
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if largest_contour is not None:
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# 对于每个 x 坐标，找到对应的 y 坐标（取平均值）
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x_coords = {}
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for point in largest_contour:
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x, y = point[0]
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# 转换回原图坐标
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global_x = int(x) + chart_left
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global_y = int(y) + chart_top
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if global_x not in x_coords:
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x_coords[global_x] = []
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x_coords[global_x].append(global_y)
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# 对每个 x，计算平均 y 值
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for x in sorted(x_coords.keys()):
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y_values = x_coords[x]
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avg_y = sum(y_values) // len(y_values)
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frequency_points.append([int(x), int(avg_y)])
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print(f"提取的频响曲线点数：{len(frequency_points)}")
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# 创建可视化结果
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result = img_copy.copy()
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# 绘制检测到的点
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for i, (x, y) in enumerate(frequency_points):
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cv2.circle(result, (x, y), 1, (255, 0, 0), -1)
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# 显示结果
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plt.figure(figsize=(20, 10))
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plt.imshow(result)
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plt.title(f'Extracted Frequency Response Curve ({len(frequency_points)} points)')
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plt.axis('off')
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plt.tight_layout()
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plt.show()
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# 保存点到 JSON 文件
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output_data = {
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"frequency_points": frequency_points,
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"chart_area": {
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"top": int(chart_top),
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"bottom": int(chart_bottom),
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"left": int(chart_left),
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"right": int(chart_right)
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},
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"image_size": {
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"width": int(width),
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"height": int(height)
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}
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}
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with open('frequency_response_points.json', 'w', encoding='utf-8') as f:
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json.dump(output_data, f, ensure_ascii=False, indent=2)
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print(f"\n频响点位已保存到 frequency_response_points.json")
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print(f"\n前 50 个点的坐标 (x, y):")
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for i, point in enumerate(frequency_points[:50]):
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print(f"{i+1}: {point}")
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# 也保存为 CSV 格式，方便查看
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with open('frequency_response_points.csv', 'w', encoding='utf-8') as f:
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f.write("index,x,y\n")
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for i, point in enumerate(frequency_points):
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f.write(f"{i+1},{point[0]},{point[1]}\n")
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print(f"\n点位也已保存到 frequency_response_points.csv")
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else:
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print("未找到频响曲线")
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