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on
Zero
Running
on
Zero
import random | |
import numpy as np | |
import cv2 | |
import os | |
import PIL | |
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, short = False, interpolation=None): | |
if isinstance(input_image,PIL.Image.Image): | |
mode = 'pil' | |
W,H = input_image.size | |
elif isinstance(input_image,np.ndarray): | |
mode = 'cv2' | |
H, W, _ = input_image.shape | |
H = float(H) | |
W = float(W) | |
if short: | |
k = float(resolution) / min(H, W) # k>1 放大, k<1 缩小 | |
else: | |
k = float(resolution) / max(H, W) # k>1 放大, k<1 缩小 | |
H *= k | |
W *= k | |
H = int(np.round(H / 64.0)) * 64 | |
W = int(np.round(W / 64.0)) * 64 | |
if mode == 'cv2': | |
if interpolation is None: | |
interpolation = cv2.INTER_LANCZOS4 if k > 1 else cv2.INTER_AREA | |
img = cv2.resize(input_image, (W, H), interpolation=interpolation) | |
elif mode == 'pil': | |
if interpolation is None: | |
interpolation = PIL.Image.LANCZOS if k > 1 else PIL.Image.BILINEAR | |
img = input_image.resize((W, H), resample=interpolation) | |
return img | |
# 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)) | |