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import random
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 nms(x, t, s):
x = cv2.GaussianBlur(x.astype(np.float32), (0, 0), s)
f1 = np.array([[0, 0, 0], [1, 1, 1], [0, 0, 0]], dtype=np.uint8)
f2 = np.array([[0, 1, 0], [0, 1, 0], [0, 1, 0]], dtype=np.uint8)
f3 = np.array([[1, 0, 0], [0, 1, 0], [0, 0, 1]], dtype=np.uint8)
f4 = np.array([[0, 0, 1], [0, 1, 0], [1, 0, 0]], dtype=np.uint8)
y = np.zeros_like(x)
for f in [f1, f2, f3, f4]:
np.putmask(y, cv2.dilate(x, kernel=f) == x, x)
z = np.zeros_like(y, dtype=np.uint8)
z[y > t] = 255
return z
def make_noise_disk(H, W, C, F):
noise = np.random.uniform(low=0, high=1, size=((H // F) + 2, (W // F) + 2, C))
noise = cv2.resize(noise, (W + 2 * F, H + 2 * F), interpolation=cv2.INTER_CUBIC)
noise = noise[F: F + H, F: F + W]
noise -= np.min(noise)
noise /= np.max(noise)
if C == 1:
noise = noise[:, :, None]
return noise
def min_max_norm(x):
x -= np.min(x)
x /= np.maximum(np.max(x), 1e-5)
return x
def safe_step(x, step=2):
y = x.astype(np.float32) * float(step + 1)
y = y.astype(np.int32).astype(np.float32) / float(step)
return y
def img2mask(img, H, W, low=10, high=90):
assert img.ndim == 3 or img.ndim == 2
assert img.dtype == np.uint8
if img.ndim == 3:
y = img[:, :, random.randrange(0, img.shape[2])]
else:
y = img
y = cv2.resize(y, (W, H), interpolation=cv2.INTER_CUBIC)
if random.uniform(0, 1) < 0.5:
y = 255 - y
return y < np.percentile(y, random.randrange(low, high))
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