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import numpy as np
import cv2
import os
annotator_ckpts_path = os.path.join(os.path.dirname(__file__), 'ckpts')
def HWC3(x):
assert x.dtype == np.uint8
if x.ndim == 2:
x = x[:, :, None]
assert x.ndim == 3
H, W, C = x.shape
assert C == 1 or C == 3 or C == 4
if C == 3:
return x
if C == 1:
return np.concatenate([x, x, x], axis=2)
if C == 4:
color = x[:, :, 0:3].astype(np.float32)
alpha = x[:, :, 3:4].astype(np.float32) / 255.0
y = color * alpha + 255.0 * (1.0 - alpha)
y = y.clip(0, 255).astype(np.uint8)
return y
def resize_image(input_image, resolution):
H, W, C = input_image.shape
H = float(H)
W = float(W)
k = float(resolution) / min(H, W)
H *= k
W *= k
H = int(np.round(H / 64.0)) * 64
W = int(np.round(W / 64.0)) * 64
img = cv2.resize(input_image, (W, H), interpolation=cv2.INTER_LANCZOS4 if k > 1 else cv2.INTER_AREA)
return img
def resize_points(clicked_points, original_shape, resolution):
original_height, original_width, _ = original_shape
original_height = float(original_height)
original_width = float(original_width)
scale_factor = float(resolution) / min(original_height, original_width)
resized_points = []
for point in clicked_points:
x, y, lab = point
resized_x = int(round(x * scale_factor))
resized_y = int(round(y * scale_factor))
resized_point = (resized_x, resized_y, lab)
resized_points.append(resized_point)
return resized_points
def get_bounding_box(mask):
# Convert PIL Image to numpy array
mask = np.array(mask).astype(np.uint8)
# Take the first channel (R) of the mask
mask = mask[:,:,0]
# Get the indices of elements that are not zero
rows = np.any(mask, axis=0)
cols = np.any(mask, axis=1)
# Get the minimum and maximum indices where the elements are not zero
rmin, rmax = np.where(rows)[0][[0, -1]]
cmin, cmax = np.where(cols)[0][[0, -1]]
# Return as [xmin, ymin, xmax, ymax]
return [rmin, cmin, rmax, cmax]
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