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from functools import lru_cache
import cv2
import numpy as np
def visualize_instances(imask, bg_color=255,
boundaries_color=None, boundaries_width=1, boundaries_alpha=0.8):
num_objects = imask.max() + 1
palette = get_palette(num_objects)
if bg_color is not None:
palette[0] = bg_color
result = palette[imask].astype(np.uint8)
if boundaries_color is not None:
boundaries_mask = get_boundaries(imask, boundaries_width=boundaries_width)
tresult = result.astype(np.float32)
tresult[boundaries_mask] = boundaries_color
tresult = tresult * boundaries_alpha + (1 - boundaries_alpha) * result
result = tresult.astype(np.uint8)
return result
@lru_cache(maxsize=16)
def get_palette(num_cls):
palette = np.zeros(3 * num_cls, dtype=np.int32)
for j in range(0, num_cls):
lab = j
i = 0
while lab > 0:
palette[j*3 + 0] |= (((lab >> 0) & 1) << (7-i))
palette[j*3 + 1] |= (((lab >> 1) & 1) << (7-i))
palette[j*3 + 2] |= (((lab >> 2) & 1) << (7-i))
i = i + 1
lab >>= 3
return palette.reshape((-1, 3))
def visualize_mask(mask, num_cls):
palette = get_palette(num_cls)
mask[mask == -1] = 0
return palette[mask].astype(np.uint8)
def visualize_proposals(proposals_info, point_color=(255, 0, 0), point_radius=1):
proposal_map, colors, candidates = proposals_info
proposal_map = draw_probmap(proposal_map)
for x, y in candidates:
proposal_map = cv2.circle(proposal_map, (y, x), point_radius, point_color, -1)
return proposal_map
def draw_probmap(x):
return cv2.applyColorMap((x * 255).astype(np.uint8), cv2.COLORMAP_HOT)
def draw_points(image, points, color, radius=3):
image = image.copy()
for p in points:
if p[0] < 0:
continue
if len(p) == 3:
pradius = {0: 8, 1: 6, 2: 4}[p[2]] if p[2] < 3 else 2
else:
pradius = radius
image = cv2.circle(image, (int(p[1]), int(p[0])), pradius, color, -1)
return image
def draw_instance_map(x, palette=None):
num_colors = x.max() + 1
if palette is None:
palette = get_palette(num_colors)
return palette[x].astype(np.uint8)
def blend_mask(image, mask, alpha=0.6):
if mask.min() == -1:
mask = mask.copy() + 1
imap = draw_instance_map(mask)
result = (image * (1 - alpha) + alpha * imap).astype(np.uint8)
return result
def get_boundaries(instances_masks, boundaries_width=1):
boundaries = np.zeros((instances_masks.shape[0], instances_masks.shape[1]), dtype=bool)
for obj_id in np.unique(instances_masks.flatten()):
if obj_id == 0:
continue
obj_mask = instances_masks == obj_id
kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (3, 3))
inner_mask = cv2.erode(obj_mask.astype(np.uint8), kernel, iterations=boundaries_width).astype(bool)
obj_boundary = np.logical_xor(obj_mask, np.logical_and(inner_mask, obj_mask))
boundaries = np.logical_or(boundaries, obj_boundary)
return boundaries
def draw_with_blend_and_clicks(img, mask=None, alpha=0.6, clicks_list=None, pos_color=(0, 255, 0),
neg_color=(255, 0, 0), radius=4):
result = img.copy()
if mask is not None:
palette = get_palette(np.max(mask) + 1)
rgb_mask = palette[mask.astype(np.uint8)]
mask_region = (mask > 0).astype(np.uint8)
result = result * (1 - mask_region[:, :, np.newaxis]) + \
(1 - alpha) * mask_region[:, :, np.newaxis] * result + \
alpha * rgb_mask
result = result.astype(np.uint8)
# result = (result * (1 - alpha) + alpha * rgb_mask).astype(np.uint8)
if clicks_list is not None and len(clicks_list) > 0:
pos_points = [click.coords for click in clicks_list if click.is_positive]
neg_points = [click.coords for click in clicks_list if not click.is_positive]
result = draw_points(result, pos_points, pos_color, radius=radius)
result = draw_points(result, neg_points, neg_color, radius=radius)
return result
def draw_contour(img, mask, color=(253, 211, 106), thickness=2):
contours, _ = cv2.findContours(mask.astype(np.uint8), cv2.RETR_LIST, cv2.CHAIN_APPROX_NONE)
img = cv2.drawContours(img, contours, -1, color=color, thickness=thickness)
return img
def draw_mask(img, mask, opacity=0.6):
mask = (mask > 0)[..., None]
img = img * mask + img * ~mask * (1 - opacity)
return img.astype(np.uint8)
def draw_click(img, clicks, radius=5):
for click in clicks:
color = (146, 208, 80) if click.is_positive else (192, 0, 0)
coords = (click.coords[1], click.coords[0])
img = cv2.circle(img.copy(), coords, int(radius * 1.5), (0, 0, 0), -1)
img = cv2.circle(img, coords, radius, color, -1)
return img |