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@@ -4,41 +4,37 @@ from .get_feature import main
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import os
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rate = 0.5
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-
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-def predict(path, model):
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+def predict(path, model) -> tuple:
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''' predict
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Args:
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- path - image path
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- Returns: image path, image info
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+ path - 图像路径
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+ Returns: 图像路径，图像信息
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'''
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global img_y
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- file_name = os.path.split(path)[1].split('.')[0]
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-
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- x = path.replace('\\', '/')
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- img_y = model.predict(x)['label_map']
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- img_y = img_y * 255
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- img_y = img_y.astype(np.int)
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+ file_name = os.path.split(path)[1].split('.')[0] # 获取文件名，不包括扩展名
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+
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+ x = path.replace('\\', '/') # 将路径中的反斜杠替换为正斜杠
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+ img_y = model.predict(x)['label_map'] # 使用模型预测图片得到标签映射
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+ img_y = img_y * 255 # 将标签映射乘以255，转为灰度值
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+ img_y = img_y.astype(np.int) # 转换为整数类型
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cv2.imwrite(f'./tmp/mask/{file_name}_mask.png', img_y,
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- (cv2.IMWRITE_PNG_COMPRESSION, 0))
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- last_process(file_name)
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- image_info = main(file_name)
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+ (cv2.IMWRITE_PNG_COMPRESSION, 0)) # 将图像保存为PNG格式
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+ last_process(file_name) # 调用最后处理函数
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+ image_info = main(file_name) # 调用主函数获取图像信息
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return file_name + '.png', image_info
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+
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def data_in_one(inputdata):
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- if not inputdata.any():
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+ if not inputdata.any(): # 如果输入数据为空则直接返回
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return inputdata
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- inputdata = (inputdata - inputdata.min()) / (inputdata.max() - inputdata.min())
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+ inputdata = (inputdata - inputdata.min()) / (inputdata.max() - inputdata.min()) # 数据归一化处理
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return inputdata
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-def pre_process(data_path):
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- file_name = os.path.split(data_path)[1].split('.')[0]
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- return data_path, file_name
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-
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def last_process(file_name):
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- ''' last process'''
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- image = cv2.imread(f'./tmp/ct/{file_name}.png')
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- mask = cv2.imread(f'./tmp/mask/{file_name}_mask.png', 0)
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- contours, hierarchy = cv2.findContours(mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
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- cv2.drawContours(image, contours, -1, (0, 255, 0), 2)
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+ '''最后的处理'''
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+ image = cv2.imread(f'./tmp/ct/{file_name}.png') # 读取目录下的图片
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+ mask = cv2.imread(f'./tmp/mask/{file_name}_mask.png', 0) # 以灰度图像方式读取目录下的掩膜图片
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+ contours, hierarchy = cv2.findContours(mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE) # 使用findContours函数找到掩膜中的轮廓
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+ cv2.drawContours(image, contours, -1, (0, 255, 0), 2) # 在原始图片上绘制轮廓
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cv2.imwrite('./tmp/draw/{}.png'.format(file_name), image)
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