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import numpy as np
import cv2
import torch
import numpy as np
from PIL import Image
def instantiate_from_config(config):
if not "target" in config:
raise KeyError("Expected key `target` to instantiate.")
return get_obj_from_str(config["target"])(**config.get("params", dict()))
def get_obj_from_str(string, reload=False):
import importlib
module, cls = string.rsplit(".", 1)
if reload:
module_imp = importlib.import_module(module)
importlib.reload(module_imp)
return getattr(importlib.import_module(module, package=None), cls)
def tensor_detail(t):
assert type(t) == torch.Tensor
print(f"shape: {t.shape} mean: {t.mean():.2f}, std: {t.std():.2f}, min: {t.min():.2f}, max: {t.max():.2f}")
def drawRoundRec(draw, color, x, y, w, h, r):
drawObject = draw
'''Rounds'''
drawObject.ellipse((x, y, x + r, y + r), fill=color)
drawObject.ellipse((x + w - r, y, x + w, y + r), fill=color)
drawObject.ellipse((x, y + h - r, x + r, y + h), fill=color)
drawObject.ellipse((x + w - r, y + h - r, x + w, y + h), fill=color)
'''rec.s'''
drawObject.rectangle((x + r / 2, y, x + w - (r / 2), y + h), fill=color)
drawObject.rectangle((x, y + r / 2, x + w, y + h - (r / 2)), fill=color)
def do_resize_content(original_image: Image, scale_rate):
# resize image content wile retain the original image size
if scale_rate != 1:
# Calculate the new size after rescaling
new_size = tuple(int(dim * scale_rate) for dim in original_image.size)
# Resize the image while maintaining the aspect ratio
resized_image = original_image.resize(new_size)
# Create a new image with the original size and black background
padded_image = Image.new("RGBA", original_image.size, (0, 0, 0, 0))
paste_position = ((original_image.width - resized_image.width) // 2, (original_image.height - resized_image.height) // 2)
padded_image.paste(resized_image, paste_position)
return padded_image
else:
return original_image
def add_stroke(img, color=(255, 255, 255), stroke_radius=3):
# color in R, G, B format
if isinstance(img, Image.Image):
assert img.mode == "RGBA"
img = cv2.cvtColor(np.array(img), cv2.COLOR_RGBA2BGRA)
else:
assert img.shape[2] == 4
gray = img[:,:, 3]
ret, binary = cv2.threshold(gray,127,255,cv2.THRESH_BINARY)
contours, hierarchy = cv2.findContours(binary,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)
res = cv2.drawContours(img, contours,-1, tuple(color)[::-1] + (255,), stroke_radius)
return Image.fromarray(cv2.cvtColor(res,cv2.COLOR_BGRA2RGBA))
def make_blob(image_size=(512, 512), sigma=0.2):
"""
make 2D blob image with:
I(x, y)=1-\exp \left(-\frac{(x-H / 2)^2+(y-W / 2)^2}{2 \sigma^2 HS}\right)
"""
import numpy as np
H, W = image_size
x = np.arange(0, W, 1, float)
y = np.arange(0, H, 1, float)
x, y = np.meshgrid(x, y)
x0 = W // 2
y0 = H // 2
img = 1 - np.exp(-((x - x0) ** 2 + (y - y0) ** 2) / (2 * sigma ** 2 * H * W))
return (img * 255).astype(np.uint8) |