112 lines
4.0 KiB
Python
112 lines
4.0 KiB
Python
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import cv2
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import matplotlib.pyplot as plt
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import os
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def plot_histogram_and_draw_lines(image_path, column_indices, y_range, results, image_index):
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# 讀取圖像
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image = cv2.imread(image_path, cv2.IMREAD_GRAYSCALE)
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# 檢查圖像是否成功讀取
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if image is None:
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print(f"Error: Unable to read the image '{image_path}'.")
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return
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# 創建彩色圖像以便畫線
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image_color = cv2.cvtColor(image, cv2.COLOR_GRAY2BGR)
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for column_index in column_indices:
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# 檢查 column_index 是否在圖像範圍內
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if column_index >= image.shape[1]:
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continue
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# 檢查 y_range 是否在圖像範圍內
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if max(y_range) >= image.shape[0]:
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continue
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# 選擇特定的列和 y 坐標範圍
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column_data = image[y_range, column_index]
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# 反轉 y 坐標範圍
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y_coordinates_reversed = list(y_range)[::-1]
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# 查找從灰度值 250 開始並遵循規則的最高點
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start_index = next((i for i, value in enumerate(column_data) if value <= 250), None)
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if start_index is None:
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continue
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start_y_coord = y_coordinates_reversed[start_index]
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decreasing = True
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highest_point = None
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potential_highest = None
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for i in range(start_index, len(column_data) - 40):
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current_value = column_data[i]
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next_values = column_data[i + 1:i + 5]
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if decreasing:
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if all(next_value > current_value for next_value in next_values):
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decreasing = False
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potential_highest = (y_coordinates_reversed[i + 3], column_data[i + 3])
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else:
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if all(next_value > potential_highest[1] for next_value in next_values):
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potential_highest = (y_coordinates_reversed[i + 2], column_data[i + 2])
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elif all(next_value < current_value for next_value in next_values):
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highest_point = potential_highest
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break
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if highest_point:
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highest_y_coord, highest_grayscale_value = highest_point
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# 計算Y坐標的差值
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y_difference = start_y_coord - highest_y_coord
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# 計算Y坐標差值乘以2.4/1000的結果
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result = y_difference * 2.4 / 1000
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# 在圖像上畫線，從起點畫到最高點
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cv2.line(image_color, (column_index, image.shape[0] - start_y_coord),
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(column_index, image.shape[0] - highest_y_coord), (0, 0, 255), 1)
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# 保存結果值和圖號到列表
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results.append((image_index, result))
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# 顯示並保存帶有畫線的圖像
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plt.imshow(cv2.cvtColor(image_color, cv2.COLOR_BGR2RGB))
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plt.title('Image with lines')
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plt.show()
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# 獲取輸出圖像的文件名和路徑
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base_filename = os.path.splitext(os.path.basename(image_path))[0]
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output_filename = f'{base_filename}.png'
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output_path = os.path.join(os.path.dirname(image_path), output_filename)
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cv2.imwrite(output_path, image_color)
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def process_folder(folder_path, column_indices, y_range):
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all_results = []
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image_index = 0
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for filename in os.listdir(folder_path):
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if filename.endswith(('.bmp', '.png', '.jpg', '.jpeg')):
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image_path = os.path.join(folder_path, filename)
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plot_histogram_and_draw_lines(image_path, column_indices, y_range, all_results, image_index)
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image_index += 1
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# 打印所有結果值，每10個空一行
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for i, (image_index, result) in enumerate(all_results, start=1):
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print(f'Image {image_index + 1}: Result: {result:.4f}')
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if i % 10 == 0:
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print() # 插入空行
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if __name__ == '__main__':
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folder_path = r"D:\Code\Project\Medeologix\Python\Size\TIS_test\joe\1092"
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x_range = 5472
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num_segments = 10
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column_indices = [x * x_range // num_segments for x in range(num_segments)]
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y_range_partial = range(0, 3648) # 嘗試不同的範圍
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process_folder(folder_path, column_indices, y_range_partial)
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