可视化接口

图像分类

image_with_heatmap

medcv.visualize.cls.image_with_heatmap(image, heatmap, alpha=0.8, beta=0.3, colormap=cv2.COLORMAP_JET, width=None, level=None)

将关注热图在原图上进行可视化，例如：深度学习分类网络的Grad-CAM热图叠加在医学图像上进行可视化。

本函数内部采用 cv2.addWdighted 函数实现，是一种图像融合方法，对图像赋予不同的权重，以使其具有融合或透明的效果。根据以下等式进行融合：

\[dst = \alpha \cdot image + \beta \cdot heatmap + \gamma\]

参数

  • image (np.ndarray)：二维样式的医学图像原图

  • heatmap (np.ndarray)：image 对应的热图矩阵

  • alpha (float)： image 对应的权重，取值范围为 0 ~ 1 ，默认为 0.8

  • beta (float)： heatmap 对应的权重，取值范围为 0 ~ 1 ，默认为 0.3

  • colormap：颜色图类型，默认为 cv2.COLORMAP_JET ，详情参考 此文档

  • width (int)：显示的窗宽，默认为```None```，则```width=max(image)-min(image)```

  • level (int)：显示的窗位，默认为```None```，则```level=((image)+min(image))/2```

使用示例

import numpy as np
import matplotlib.pyplot as plt
from medcv.tools.transform import med2rgb
from medcv.data import chest_dcm, chest_heatmap
from medcv.visualize.cls import image_with_heatmap

# load chest dcm image and its heatmap
chest_dcm_image = chest_dcm()
chest_heatmap_image = chest_heatmap()

# dcm to rgb image with image window width and level
chest_rgb_image = med2rgb(chest_dcm_image, width=22135, level=12209)

# dcm with heatmap
chest_image_with_heatmap = image_with_heatmap(chest_dcm_image, chest_heatmap_image, width=22135, level=12209)

# plot visualize
plt.figure(figsize=(15, 5))
plt.subplot(1, 4, 1)
plt.title('origin image')
plt.imshow(np.squeeze(chest_dcm_image))
plt.subplot(1, 4, 2)
plt.title('rgb image')
plt.imshow(chest_rgb_image)
plt.subplot(1, 4, 3)
plt.title('heatmap image')
plt.imshow(chest_heatmap_image)
plt.subplot(1, 4, 4)
plt.title('chest with heatmap image')
plt.imshow(chest_image_with_heatmap)
plt.show()

可视化

_images/chest_with_heatmap.png

目标检测

image_with_bbox

medcv.visualize.det.imag_with_bbox(image, bbox, bbox_ids=None, colors=None, thickness=1, width=None, level=None)

将目标检测框在原图上进行可视化，通过指定 bbox_ids 参数支持多个不同对象框的可视化

参数

  • image (np.ndarray)：二维样式的医学图像原图

  • bbox (list)：检测框列表，形式为 [(x, y, w, h), (x, y, w, h), ...]

  • bbox_ids (list)：检测框的id列表，每个检测框对应一个id，以区分不同的对象，默认为 None ，即每个检测框对应不同的对象

  • colors (list)：不同id对应rgb颜色列表，形式为 [(r, g, b), (r, g, b), ...] ，如果为 None ，颜色将会随机

  • thickness (int)：边框的像素大小，默认为1

  • width (int)：显示的窗宽，默认为```None```，则```width=max(image)-min(image)```

  • level (int)：显示的窗位，默认为```None```，则```level=((image)+min(image))/2```

使用示例

import matplotlib.pyplot as plt
from medcv.tools.transform import med2rgb
from medcv.data import chest_dcm, chest_bbox
from medcv.visualize.det import image_with_bbox

# load chest dcm image and its heatmap
chest_dcm_image = chest_dcm()
chest_bbox_dict = chest_bbox()

# bbox list, because chest_bbox_dict has left-lung and right-lung bbox
bbox_list = chest_bbox_dict.values()

# dcm to rgb image with image window width and level
chest_rgb_image = med2rgb(chest_dcm_image, width=22135, level=12209)

# dcm with bbox, default is different bbox id.
chest_image_with_bbox = image_with_bbox(chest_dcm_image, bbox_list, width=22135, level=12209, thickness=10)

# dcm with bbox with thickness=20
chest_image_with_bold_bbox = image_with_bbox(chest_dcm_image, bbox_list, width=22135, level=12209, thickness=20)

# dcm with same bbox id
chest_image_with_same_bbox = image_with_bbox(chest_dcm_image, bbox_list, bbox_ids=[0, 0], width=22135, level=12209, thickness=10)

# plot visualize
plt.figure(figsize=(15, 5))
plt.subplot(1, 4, 1)
plt.title('chest rgb image')
plt.imshow(chest_rgb_image)
plt.subplot(1, 4, 2)
plt.title('bbox image')
plt.imshow(chest_image_with_bbox)
plt.subplot(1, 4, 3)
plt.title('bold thickness')
plt.imshow(chest_image_with_bold_bbox)
plt.subplot(1, 4, 4)
plt.title('same bbox id')
plt.imshow(chest_image_with_same_bbox)
plt.show()

可视化

_images/chest_with_bbox.png

实例分割

imag_with_mask

medcv.visualize.seg.imag_with_mask(image, mask, alpha=0.5, colors=None, width=None, level=None)

将标注的Mask在原图上进行可视化，例如：对比分割结果与金标准的重叠程度的可视化。

参数

  • image (np.ndarray)：二维样式的医学图像原图

  • mask (np.ndarray)：image 对应的mask，默认0为背景，非零值为ROI

  • alpha (float)： mask 对应的加权值，取值范围为 0 ~ 1 ，默认为 0.5

  • colors (list)：不同ROI对应rgb颜色列表，按照ROI的标注值进行升序索引，形式为 [(r, g, b), (r, g, b), ...]

  • width (int)：显示的窗宽，默认为 None，则```width=max(image)-min(image)```

  • level (int)：显示的窗位，默认为 None，则```level=((image)+min(image))/2```

使用示例

import numpy as np
import matplotlib.pyplot as plt
from medcv.tools.transform import med2rgb
from medcv.data import chest_dcm, chest_mask
from medcv.visualize.seg import image_with_mask

# load chest dcm image and its mask
chest_dcm_image = chest_dcm()
chest_mask_image = chest_mask()

# dcm to rgb image with image window width and level
chest_rgb_image = med2rgb(chest_dcm_image, width=22135, level=12209)

# visualize colors
colors = [(0, 255, 0), (255, 0, 0), (0, 0, 255)]

# alpha=0.2
chest_image_with_mask1 = image_with_mask(chest_dcm_image, chest_mask_image, colors=colors, alpha=0.2, width=22135, level=12209)
# alpha=0.5 (default alpha=0.5)
chest_image_with_mask2 = image_with_mask(chest_dcm_image, chest_mask_image, colors=colors, alpha=0.5, width=22135, level=12209)
# alpha=0.8
chest_image_with_mask3 = image_with_mask(chest_dcm_image, chest_mask_image, colors=colors, alpha=0.8, width=22135, level=12209)

# plot visualize
plt.figure(figsize=(15, 5))
plt.subplot(1, 4, 1)
plt.title('chest rgb image')
plt.imshow(chest_rgb_image)
plt.subplot(1, 4, 2)
plt.title('alpha=0.2')
plt.imshow(chest_image_with_mask1)
plt.subplot(1, 4, 3)
plt.title('alpha=0.5')
plt.imshow(chest_image_with_mask2)
plt.subplot(1, 4, 4)
plt.title('alpha=0.8')
plt.imshow(chest_image_with_mask3)
plt.show()

可视化

_images/chest_with_mask.png

备注说明：绿色为金标准与分割结果的重叠部分，红色+绿色=金标准区域，蓝色+绿色=分割结果区域

image_with_contours

medcv.visualize.seg.image_with_contours(image, mask, colors=None, thickness=1, width=None, level=None)

本函数内部采用 cv2.drawContours 函数实现，将标注的Mask的边缘轮廓在原图上进行可视化，例如：对比分割结果与金标准的边缘信息的可视化。

参数

  • image (np.ndarray)：二维样式的医学图像原图

  • mask (np.ndarray)：image 对应的mask，默认0为背景，非零值为ROI

  • colors (list)：不同ROI对应rgb颜色列表，按照ROI的标注值进行升序索引，形式为 [(r, g, b), (r, g, b), ...]

  • thickness (int)：边缘轮廓线的像素大小

  • width (int)：显示的窗宽，默认为```None```，则```width=max(image)-min(image)```

  • level (int)：显示的窗位，默认为```None```，则```level=((image)+min(image))/2```

使用示例

import numpy as np
import matplotlib.pyplot as plt
from medcv.tools.transform import med2rgb
from medcv.data import chest_dcm, chest_mask
from medcv.visualize.seg import image_with_contours

# load chest dcm image and its mask
chest_dcm_image = chest_dcm()
chest_mask_image = chest_mask()

# dcm to rgb image with image window width and level
chest_rgb_image = med2rgb(chest_dcm_image, width=22135, level=12209)

# ground truth
gt_mask = np.zeros_like(chest_mask_image)
gt_mask[np.logical_or(chest_mask_image == 1, chest_mask_image == 2)] = 1
chest_image_with_gt = image_with_contours(chest_dcm_image, gt_mask, thickness=10, width=22135, level=12209)
# model segmentation
seg_mask = np.zeros_like(chest_mask_image)
seg_mask[np.logical_or(chest_mask_image == 1, chest_mask_image == 3)] = 1
chest_image_with_seg = image_with_contours(chest_dcm_image, seg_mask, thickness=10, width=22135, level=12209)
# difference between ground truth and model segmentation
diff_mask = np.zeros_like(chest_mask_image)
diff_mask[np.logical_or(chest_mask_image == 2, chest_mask_image == 3)] = 1
chest_image_with_diff = image_with_contours(chest_dcm_image, diff_mask, thickness=10, width=22135, level=12209)

# plot visualize
plt.figure(figsize=(15, 5))
plt.subplot(1, 4, 1)
plt.title('chest rgb image')
plt.imshow(chest_rgb_image)
plt.subplot(1, 4, 2)
plt.title('ground truth')
plt.imshow(chest_image_with_gt)
plt.subplot(1, 4, 3)
plt.title('model segmentation')
plt.imshow(chest_image_with_seg)
plt.subplot(1, 4, 4)
plt.title('difference')
plt.imshow(chest_image_with_diff)
plt.show()

可视化

_images/chest_with_contours.png