Upload libs/base_utils.py with huggingface_hub

libs/base_utils.py

@@ -0,0 +1,84 @@
+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)