147 lines
6.6 KiB
Python
147 lines
6.6 KiB
Python
import cv2
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import numpy as np
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class ImageProcessor:
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def __init__(self, image_path):
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# 讀取圖像
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self.original_image = cv2.imread(image_path)
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self.process_image()
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def process_image(self):
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# 複製原始圖像
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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
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# 將圖像轉換為灰度圖
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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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cv2.imshow('Canny Edges', edges) # 顯示 Canny 邊緣檢測結果
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cv2.imwrite('canny_edges.jpg', edges) # 保存 Canny 邊緣檢測結果
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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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# 初始化黑色圖像
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black_img = gray_image.copy()
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black_img[:, :] = 0
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hough_lines_image = black_img.copy() # 用於顯示HoughLinesP檢測結果的圖像
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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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cv2.line(hough_lines_image, (x1, y1), (x2, y2), (255, 255, 0), 2)
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cv2.imshow('Hough Lines', hough_lines_image) # 顯示 HoughLinesP 檢測結果
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cv2.imwrite('hough_lines.jpg', hough_lines_image) # 保存 HoughLinesP 檢測結果
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# 繪製紅色的兩條線
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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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# 找到黑色圖像中白色（255）的像素
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y_list, x_list = np.where(black_img == 255)
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# 像素大小（5.5um）
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pixel_size_um = 5.5
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# 計算直徑
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diameter1, point1_top, point1_bottom = self.calculate_diameter(300, x_list, y_list, pixel_size_um)
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diameter2, point2_top, point2_bottom = self.calculate_diameter(o_img_black.shape[1] - 300, x_list, y_list, pixel_size_um)
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# 計算角度並找到負角度的最小值和正角度的最大值
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angle_sum = self.calculate_angles(lines)
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# 標記相交點
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self.mark_intersection_points(o_img_black, x_list, y_list, 300, o_img_black.shape[1] - 300)
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# 繪製點和垂直線
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self.draw_points_and_lines(diameter1, diameter2, point1_top, point1_bottom, point2_top, point2_bottom)
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# 顯示結果
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self.display_image(o_img_black, diameter1, diameter2, angle_sum)
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def calculate_diameter(self, x_value, x_list, y_list, pixel_size_um):
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indices = np.where(x_list == x_value)[0]
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y_values = y_list[indices]
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max_y = np.max(y_values)
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min_y = np.min(y_values)
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diff = max_y - min_y
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diameter = diff * pixel_size_um / 1000
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print(f"直徑: Ø{diameter:.3f} mm")
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return diameter, (x_value, min_y), (x_value, max_y)
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def calculate_angles(self, lines):
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min_negative_angle = float('inf')
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max_positive_angle = float('-inf')
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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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angle = np.arctan2(y2 - y1, x2 - x1) * 180.0 / np.pi
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if angle < 0:
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min_negative_angle = min(min_negative_angle, angle)
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elif angle > 0:
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max_positive_angle = max(max_positive_angle, angle)
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if min_negative_angle != float('inf') and max_positive_angle != float('-inf'):
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min_negative_angle_abs = abs(min_negative_angle)
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angle_sum = min_negative_angle_abs + max_positive_angle
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print(f"角度為: {angle_sum:.2f} °")
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return angle_sum
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return None
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def mark_intersection_points(self, img, x_list, y_list, x_value1, x_value2):
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indices_1 = np.where(x_list == x_value1)[0]
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indices_2 = np.where(x_list == x_value2)[0]
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intersection_points_1 = [(x_value1, y_list[i]) for i in indices_1]
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intersection_points_2 = [(x_value2, y_list[i]) for i in indices_2]
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intersection_points = intersection_points_1 + intersection_points_2
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for point in intersection_points:
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cv2.circle(img, point, 10, (183, 143, 58), -1)
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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(img, point, 1, (183, 143, 58), -1)
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def draw_points_and_lines(self, diameter1, diameter2, point1_top, point1_bottom, point2_top, point2_bottom):
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# 在原圖上添加測量點和連接線
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cv2.line(self.original_image, point1_top, point1_bottom, (121, 180, 177), 2)
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cv2.circle(self.original_image, point1_top, 10, (183, 143, 58), -1)
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cv2.circle(self.original_image, point1_bottom, 10, (183, 143, 58), -1)
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cv2.line(self.original_image, point2_top, point2_bottom, (121, 180, 177), 2)
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cv2.circle(self.original_image, point2_top, 10, (183, 143, 58), -1)
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cv2.circle(self.original_image, point2_bottom, 10, (183, 143, 58), -1)
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def display_image(self, img, diameter1, diameter2, angle_sum):
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# Resize and display original image
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original_resized = cv2.resize(self.original_image, (1280, 1280))
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# Add status bar information on the original image
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cv2.putText(original_resized, f"Diameter1: {diameter1:.3f} mm", (25, 80), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 0), 2) #300
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cv2.putText(original_resized, f"Diameter2: {diameter2:.3f} mm", (25, 130), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 0), 2) #350
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# cv2.putText(original_resized, f"Diameter1: {diameter1:.3f} mm", (25, 100), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 0), 2)
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# cv2.putText(original_resized, f"Diameter2: {diameter2:.3f} mm", (25, 150), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 0), 2)
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if angle_sum is not None:
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cv2.putText(original_resized, f"Angle: {angle_sum:.2f} degrees", (25, 180), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 0), 2) #400
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# Resize and display processed image
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processed_resized = cv2.resize(img, (1280, 1280))
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# Show the images
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cv2.imshow('Original Image', original_resized)
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cv2.imwrite('Original_Image.jpg', original_resized) # 保存 original 邊緣檢測結果
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cv2.imshow('Processed Image', processed_resized)
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cv2.imwrite('Processed_Image.jpg', processed_resized) # 保存 Processed 邊緣檢測結果
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cv2.waitKey(0)
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cv2.destroyAllWindows()
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if __name__ == "__main__":
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image_path = r'D:\Code\Project\Medeologix\Python\Size\01_10fps_0.041ms_0db_1092\10.bmp'
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processor = ImageProcessor(image_path)
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