111 lines
4.6 KiB
Python
111 lines
4.6 KiB
Python
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import cv2
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import numpy as np
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class test_main():
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def __init__(self):
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# 讀取原始影像
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self.original_image = cv2.imread(r'D:\Code\Project\Medeologix\Python\Size\01_10fps_0.041ms_0db_1092\01.bmp')
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self.test()
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def test(self):
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# 複製原始影像
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img = self.original_image.copy()
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o_img_black = img.copy()
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o_img_black[:, :, :] = 0 # 將影像填充為黑色
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# 將圖片轉換為灰度圖
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gray_image = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
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# 使用 Canny 邊緣檢測
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edges = cv2.Canny(gray_image, 250, 250)
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# 檢測直線
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lines = cv2.HoughLinesP(edges, 1, np.pi / 180, threshold=100, minLineLength=100, maxLineGap=200)
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# 建立黑色影像
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black_img = gray_image.copy()
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black_img[:, :] = 0
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# 如果檢測到直線，繪製在黑色影像上
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if lines is not None:
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for line in lines:
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x1, y1, x2, y2 = line[0]
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cv2.line(black_img, (x1, y1), (x2, y2), (255, 255, 0), 2)
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# 找出黑色影像中白色點的座標
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y_list, x_list = np.where(black_img == 255)
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# 繪製紅色的兩條垂直線
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cv2.line(o_img_black, (300, 0), (300, len(o_img_black)), (0, 0, 255), 2)
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cv2.line(o_img_black, (len(o_img_black[0]) - 300, 0), (len(o_img_black[0]) - 300, len(o_img_black)), (0, 0, 255), 2)
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# 像素大小（5.5μm）
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pixel_size_um = 5.5
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# 找到 x 座標等於 300 的所有索引
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indices_300 = np.where(x_list == 300)[0]
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# 從 y_list 中獲取這些索引對應的 y 值
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y_values_300 = y_list[indices_300]
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# 找出最大和最小的 Y 值
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max_y_300 = np.max(y_values_300)
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min_y_300 = np.min(y_values_300)
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print("For x = 300:")
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print("Max Y:", max_y_300, "Min Y:", min_y_300)
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# 計算相減結果
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diff_300 = max_y_300 - min_y_300
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# 將距離從像素轉換為毫米
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diameter_300 = diff_300 * pixel_size_um / 1000
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print("直徑:{:.3f} mm".format(diameter_300))
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# 找到 x 座標等於 o_img_black.shape[1] - 300 的所有索引
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indices_other = np.where(x_list == o_img_black.shape[1] - 300)[0]
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# 從 y_list 中獲取這些索引對應的 y 值
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y_values_other = y_list[indices_other]
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# 找出最大和最小的 Y 值
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max_y_other = np.max(y_values_other)
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min_y_other = np.min(y_values_other)
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print("For x =", o_img_black.shape[1] - 300)
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print("Max Y:", max_y_other, "Min Y:", min_y_other)
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# 計算相減結果
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diff_other = max_y_other - min_y_other
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# 將距離從像素轉換為毫米
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diameter_other = diff_other * pixel_size_um / 1000
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print("直徑:{:.3f} mm".format(diameter_other))
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# 從 y_list 中獲取這些索引對應的 y 值
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intersection_points_300 = [(300, y_list[i]) for i in indices_300]
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intersection_points_other = [(o_img_black.shape[1] - 300, y_list[i]) for i in indices_other]
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# 合併所有相交的點
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intersection_points = intersection_points_300 + intersection_points_other
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# 在原圖上標記相交的點並連接紅色垂直線
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for point in intersection_points:
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cv2.circle(img, point, 10, (0, 255, 0), -1)
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# 在垂直相交點之間繪製紅線
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for i in range(len(intersection_points_300) - 1):
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cv2.line(img, intersection_points_300[i], intersection_points_300[i + 1], (69, 27, 203), 2)
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for i in range(len(intersection_points_other) - 1):
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cv2.line(img, intersection_points_other[i], intersection_points_other[i + 1], (69, 27, 203), 2)
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# 將所有找到的點也標記出來
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for i in range(len(x_list)):
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point = (x_list[i], y_list[i])
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cv2.circle(o_img_black, point, 1, (102, 193, 134), -1)
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# 顯示直徑數據在原圖上
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cv2.putText(img, "Diameter at 300: {:.3f} mm".format(diameter_300), (10, 100), cv2.FONT_HERSHEY_SIMPLEX, 2, (0, 0, 0), 2)
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cv2.putText(img, "Diameter at {}: {:.3f} mm".format(o_img_black.shape[1] - 300, diameter_other), (10, 170), cv2.FONT_HERSHEY_SIMPLEX, 2, (0, 0, 0), 2)
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# 顯示結果
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black_img_resized = cv2.resize(o_img_black, (640, 640))
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cv2.imshow('Black Image with Lines', black_img_resized)
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original_img_resized = cv2.resize(img, (1280, 1280))
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cv2.imshow('Original Image with Diameter', original_img_resized)
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cv2.waitKey(0)
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cv2.destroyAllWindows()
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if __name__ == "__main__":
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test_main()
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