dashboard/convert_to_frequency_db.py

import cv2
import numpy as np
import matplotlib.pyplot as plt
import json

# 读取图片
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"
img = cv2.imread(img_path)
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
img_copy = img.copy()
gray = cv2.cvtColor(img_copy, cv2.COLOR_RGB2GRAY)

height, width = gray.shape
print(f"图片尺寸：{height}x{width}")

# 定义图表区域
chart_top = int(height * 0.05)
chart_bottom = int(height * 0.92)
chart_left = int(width * 0.02)
chart_right = int(width * 0.98)

chart_height = chart_bottom - chart_top
chart_width = chart_right - chart_left

print(f"图表区域：top={chart_top}, bottom={chart_bottom}, left={chart_left}, right={chart_right}")
print(f"图表尺寸：{chart_width}x{chart_height}")

# 频率轴映射（对数刻度）
# 从图中可以看到：20Hz 在最左边，20kHz 在最右边
# 使用对数刻度映射
freq_min = 20  # Hz
freq_max = 20000  # Hz

# dB 轴映射（线性刻度）
# 从图中可以看到：顶部约 120dB，底部约 70dB
db_max = 120  # 图表顶部对应的 dB 值（y 坐标最小）
db_min = 70   # 图表底部对应的 dB 值（y 坐标最大）

# 提取白色线条
chart_roi = gray[chart_top:chart_bottom, chart_left:chart_right]
_, thresh = cv2.threshold(chart_roi, 200, 255, cv2.THRESH_BINARY)

kernel = np.ones((3,3), np.uint8)
dilated_thresh = cv2.dilate(thresh, kernel, iterations=2)
eroded_thresh = cv2.erode(dilated_thresh, kernel, iterations=1)

contours, _ = cv2.findContours(eroded_thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)

largest_contour = None
max_area = 0
for contour in contours:
    area = cv2.contourArea(contour)
    if area > max_area:
        max_area = area
        largest_contour = contour

frequency_data = []

if largest_contour is not None:
    x_coords = {}
    
    for point in largest_contour:
        x, y = point[0]
        global_x = int(x) + chart_left
        global_y = int(y) + chart_top
        
        if global_x not in x_coords:
            x_coords[global_x] = []
        x_coords[global_x].append(global_y)
    
    # 对每个 x，计算平均 y 值，并转换为频率和 dB 值
    for x in sorted(x_coords.keys()):
        y_values = x_coords[x]
        avg_y = sum(y_values) // len(y_values)
        
        # 计算在图表中的相对位置
        relative_x = x - chart_left
        relative_y = avg_y - chart_top
        
        # 转换为频率（对数刻度）
        # log10(f) = log10(f_min) + (relative_x / chart_width) * (log10(f_max) - log10(f_min))
        log_freq = np.log10(freq_min) + (relative_x / chart_width) * (np.log10(freq_max) - np.log10(freq_min))
        frequency_hz = 10 ** log_freq
        
        # 转换为 dB 值（线性刻度，注意 y 轴是反向的）
        db_value = db_max - (relative_y / chart_height) * (db_max - db_min)
        
        frequency_data.append({
            "pixel_x": int(x),
            "pixel_y": int(avg_y),
            "frequency_hz": round(frequency_hz, 2),
            "db_value": round(db_value, 2)
        })
    
    print(f"\n提取的频响曲线点数：{len(frequency_data)}")
    
    # 创建可视化结果
    result = img_copy.copy()
    for point in frequency_data:
        cv2.circle(result, (point["pixel_x"], point["pixel_y"]), 1, (255, 0, 0), -1)
    
    plt.figure(figsize=(20, 10))
    plt.imshow(result)
    plt.title(f'Extracted Frequency Response ({len(frequency_data)} points)')
    plt.axis('off')
    plt.tight_layout()
    plt.show()
    
    # 保存详细数据到 JSON
    output_data = {
        "metadata": {
            "image_size": {"width": width, "height": height},
            "chart_area": {
                "top": chart_top,
                "bottom": chart_bottom,
                "left": chart_left,
                "right": chart_right
            },
            "frequency_range": {"min": freq_min, "max": freq_max},
            "db_range": {"min": db_min, "max": db_max}
        },
        "data_points": frequency_data
    }
    
    with open('frequency_response_detailed.json', 'w', encoding='utf-8') as f:
        json.dump(output_data, f, ensure_ascii=False, indent=2)
    
    print(f"\n详细数据已保存到 frequency_response_detailed.json")
    
    # 显示前 50 个点
    print(f"\n前 50 个频响点位（像素坐标 -> 实际值）:")
    print(f"{'序号':<6} {'X':<8} {'Y':<8} {'频率 (Hz)':<12} {'dB 值':<8}")
    print("-" * 50)
    for i, point in enumerate(frequency_data[:50]):
        print(f"{i+1:<6} {point['pixel_x']:<8} {point['pixel_y']:<8} {point['frequency_hz']:<12.2f} {point['db_value']:<8.2f}")
    
    # 保存为 CSV 格式
    with open('frequency_response.csv', 'w', encoding='utf-8') as f:
        f.write("index,pixel_x,pixel_y,frequency_hz,db_value\n")
        for i, point in enumerate(frequency_data):
            f.write(f"{i+1},{point['pixel_x']},{point['pixel_y']},{point['frequency_hz']},{point['db_value']}\n")
    
    print(f"\nCSV 数据已保存到 frequency_response.csv")
    
    # 绘制频率响应曲线图
    frequencies = [p["frequency_hz"] for p in frequency_data]
    db_values = [p["db_value"] for p in frequency_data]
    
    plt.figure(figsize=(15, 8))
    plt.semilogx(frequencies, db_values, linewidth=1)
    plt.grid(True, which='both', linestyle='-', alpha=0.7)
    plt.xlabel('Frequency (Hz)')
    plt.ylabel('Amplitude (dB)')
    plt.title('Extracted Frequency Response Curve')
    plt.xlim(20, 20000)
    plt.ylim(70, 120)
    plt.tight_layout()
    plt.savefig('frequency_response_curve.png', dpi=150)
    plt.show()
    
    print(f"\n频响曲线图已保存到 frequency_response_curve.png")
else:
    print("未找到频响曲线")