import cv2
import matplotlib.pyplot as plt
import numpy as np
color_lst = [(255, 0, 0), (0, 255, 0), (0, 0, 255), (72, 209, 204), (238, 130, 238), (221, 160, 221), (139, 69, 19),
(218, 112, 214), (210, 105, 30), (188, 143, 143), (119, 136, 153), (153, 50, 204), (106, 90, 205),
(135, 206, 250), (70, 130, 180), (138, 43, 226)]
[docs]def overlay_seg_w_img(image_rgb, image_seg, alpha=0.5):
"""
Combines image and its segmentation mask into a single image.
:param image_rgb: WxHx3, np.ndarray
:param image_seg: WxH, np.ndarray
:param alpha: Segmentation mask's transparency. float = 0.5,
:return:
"""
def overlay(image, mask, color, alpha, resize=None):
"""Combines image and its segmentation mask into a single image.
https://www.kaggle.com/code/purplejester/showing-samples-with-segmentation-mask-overlay
Params:
image: Training image. np.ndarray,
mask: Segmentation mask. np.ndarray,
color: Color for segmentation mask rendering. tuple[int, int, int] = (255, 0, 0)
alpha: Segmentation mask's transparency. float = 0.5,
resize: If provided, both image and its mask are resized before blending them together.
tuple[int, int] = (1024, 1024))
Returns:
image_combined: The combined image. np.ndarray
"""
color = color[::-1]
colored_mask = np.expand_dims(mask, 0).repeat(3, axis=0)
colored_mask = np.moveaxis(colored_mask, 0, -1)
masked = np.ma.MaskedArray(image, mask=colored_mask, fill_value=color)
image_overlay = masked.filled()
if resize is not None:
image = cv2.resize(image.transpose(1, 2, 0), resize)
image_overlay = cv2.resize(image_overlay.transpose(1, 2, 0), resize)
image_combined = cv2.addWeighted(image, 1 - alpha, image_overlay, alpha, 0)
return image_combined
image_with_masks = np.copy(image_rgb)
masks = np.unique(image_seg)
for mask_i in masks:
if mask_i == 0:
continue
mask = np.where(image_seg == mask_i, 1, 0)
image_with_masks = overlay(image_with_masks, mask, color=color_lst[mask_i], alpha=alpha)
return image_with_masks
[docs]def show_anns(anns):
if len(anns) == 0:
return
sorted_anns = sorted(anns, key=(lambda x: x['area']), reverse=True)
ax = plt.gca()
ax.set_autoscale_on(False)
img = np.ones((sorted_anns[0]['segmentation'].shape[0], sorted_anns[0]['segmentation'].shape[1], 4))
img[:, :, 3] = 0
for ann in sorted_anns:
m = ann['segmentation']
color_mask = np.concatenate([np.random.random(3), [0.35]])
img[m] = color_mask
ax.imshow(img)
[docs]def show_mask(mask, ax, random_color=False):
if random_color:
color = np.concatenate([np.random.random(3), np.array([0.6])], axis=0)
else:
color = np.array([30 / 255, 144 / 255, 255 / 255, 0.6])
h, w = mask.shape[-2:]
mask_image = mask.reshape(h, w, 1) * color.reshape(1, 1, -1)
ax.imshow(mask_image)
[docs]def show_star_points_w_labels(coords, labels, ax, marker_size=375):
pos_points = coords[labels == 1]
neg_points = coords[labels == 0]
ax.scatter(pos_points[:, 0], pos_points[:, 1], color='green', marker='*', s=marker_size, edgecolor='white',
linewidth=1.25)
ax.scatter(neg_points[:, 0], neg_points[:, 1], color='red', marker='*', s=marker_size, edgecolor='white',
linewidth=1.25)
[docs]def show_box(box, ax):
x0, y0 = box[0], box[1]
w, h = box[2] - box[0], box[3] - box[1]
ax.add_patch(plt.Rectangle((x0, y0), w, h, edgecolor='green', facecolor=(0, 0, 0, 0), lw=2))
