import cv2
import matplotlib.pyplot as plt

def plot_histogram_and_draw_lines(image_path, column_indices, y_range):
    # 讀取圖像
    image = cv2.imread(image_path, cv2.IMREAD_GRAYSCALE)

    # 檢查圖像是否成功讀取
    if image is None:
        print(f"Error: Unable to read the image '{image_path}'.")
        return

    # 打印圖像大小
    print("Image shape:", image.shape)

    # 創建彩色圖像以便畫線
    image_color = cv2.cvtColor(image, cv2.COLOR_GRAY2BGR)

    for column_index in column_indices:
        # 檢查 column_index 是否在圖像範圍內
        if column_index >= image.shape[1]:
            print(f"Error: column_index {column_index} is out of bounds for image width {image.shape[1]}.")
            continue

        # 檢查 y_range 是否在圖像範圍內
        if max(y_range) >= image.shape[0]:
            print(f"Error: y_range exceeds image height {image.shape[0]}.")
            continue

        # 選擇特定的列和 y 坐標範圍
        column_data = image[y_range, column_index]

        # 反轉 y 坐標範圍
        y_coordinates_reversed = list(y_range)[::-1]

        # 檢查 column_data 的值
        unique_values = set(column_data)
        print(f"\nUnique grayscale values in the selected range for column {column_index}: {unique_values}")

        # 打印每段的開始和結束點
        print(f"Y-coordinate start: {y_coordinates_reversed[0]}, Grayscale Value start: {column_data[0]}")
        print(f"Y-coordinate end: {y_coordinates_reversed[-1]}, Grayscale Value end: {column_data[-1]}")

        # 查找從灰度值 250 開始並遵循規則的最高點
        start_index = next((i for i, value in enumerate(column_data) if value <= 250), None)
        if start_index is None:
            print("No grayscale value below or equal to 250 found in the selected range.")
            continue

        start_y_coord = y_coordinates_reversed[start_index]
        start_grayscale_value = column_data[start_index]
        print(f"Start point: Y-coordinate: {start_y_coord}, Grayscale Value: {start_grayscale_value}")

        decreasing = True
        highest_point = None
        potential_highest = None

        for i in range(start_index, len(column_data) - 40):
            current_value = column_data[i]
            next_values = column_data[i + 1:i + 5]  #4

            if decreasing:
                if all(next_value > current_value for next_value in next_values):
                    decreasing = False
                    potential_highest = (y_coordinates_reversed[i + 3], column_data[i + 3])
            else:
                if all(next_value > potential_highest[1] for next_value in next_values):
                    potential_highest = (y_coordinates_reversed[i + 2], column_data[i + 2])
                elif all(next_value < current_value for next_value in next_values):
                    highest_point = potential_highest
                    break

        if highest_point:
            highest_y_coord, highest_grayscale_value = highest_point
            print(
                f"Highest point after decrease and subsequent increase: Y-coordinate: {highest_y_coord}, Grayscale Value: {highest_grayscale_value}")

            # 計算並打印Y坐標的差值
            y_difference = start_y_coord - highest_y_coord
            print(f"Y-coordinate difference: {y_difference}")

            # 計算並打印Y坐標差值乘以2.4/1000的結果
            result = y_difference * 2.4 / 1000
            print(f"Result: {result}")


            # 在圖像上畫線，從起點畫到最高點
            cv2.line(image_color, (column_index, image.shape[0] - start_y_coord), (column_index, image.shape[0] - highest_y_coord), (0, 0, 255), 1)

            # 在圖像上顯示結果值
            text_position = (column_index, image.shape[0] - highest_y_coord - 10)
            cv2.putText(image_color, f'Result: {result:.4f}mm', text_position, cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 255, 0), 1)
        else:
            print("No valid highest point found following the specified rules.")

        # 繪製直方圖
        # plt.plot(column_data, y_coordinates_reversed, color='gray')
        # plt.xlabel('Pixel Intensity')
        # plt.ylabel('Y-axis Coordinate (Reversed)')
        # plt.title(f'Grayscale Distribution at X-axis = {column_index} (Y-coordinate: {y_range.start}-{y_range.stop})')
        # plt.show()

    # 顯示並保存帶有畫線的圖像
    plt.imshow(cv2.cvtColor(image_color, cv2.COLOR_BGR2RGB))
    plt.title('Image with lines')
    plt.show()
    cv2.imwrite('image_with_lines.png', image_color)

if __name__ == '__main__':
    # image_path = r"02_1_20000.bmp"
    image_path = r"D:\Code\Project\Medeologix\Python\Size\TIS_test\joe\516\01_1_20000.bmp"
    x_range = 5472
    num_segments = 10

    column_indices = [x * x_range // num_segments for x in range(num_segments)]

    y_range_partial = range(0, 3648)  # 嘗試不同的範圍
    plot_histogram_and_draw_lines(image_path, column_indices, y_range_partial)