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import os |
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import sys |
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from urllib.parse import urlparse |
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import cv2 |
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import numpy as np |
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import torch |
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from torch.hub import download_url_to_file, get_dir |
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LAMA_MODEL_URL = os.environ.get( |
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"LAMA_MODEL_URL", |
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"https://github.com/Sanster/models/releases/download/add_big_lama/big-lama.pt", |
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) |
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def download_model(url=LAMA_MODEL_URL): |
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parts = urlparse(url) |
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hub_dir = get_dir() |
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model_dir = os.path.join(hub_dir, "checkpoints") |
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if not os.path.isdir(model_dir): |
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os.makedirs(os.path.join(model_dir, "hub", "checkpoints")) |
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filename = os.path.basename(parts.path) |
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cached_file = os.path.join(model_dir, filename) |
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if not os.path.exists(cached_file): |
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sys.stderr.write('Downloading: "{}" to {}\n'.format(url, cached_file)) |
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hash_prefix = None |
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download_url_to_file(url, cached_file, hash_prefix, progress=True) |
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return cached_file |
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def ceil_modulo(x, mod): |
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if x % mod == 0: |
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return x |
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return (x // mod + 1) * mod |
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def numpy_to_bytes(image_numpy: np.ndarray) -> bytes: |
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data = cv2.imencode(".jpg", image_numpy)[1] |
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image_bytes = data.tobytes() |
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return image_bytes |
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def load_img(img_bytes, gray: bool = False): |
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nparr = np.frombuffer(img_bytes, np.uint8) |
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if gray: |
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np_img = cv2.imdecode(nparr, cv2.IMREAD_GRAYSCALE) |
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else: |
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np_img = cv2.imdecode(nparr, cv2.IMREAD_UNCHANGED) |
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if len(np_img.shape) == 3 and np_img.shape[2] == 4: |
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np_img = cv2.cvtColor(np_img, cv2.COLOR_BGRA2RGB) |
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else: |
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np_img = cv2.cvtColor(np_img, cv2.COLOR_BGR2RGB) |
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return np_img |
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def norm_img(np_img): |
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if len(np_img.shape) == 2: |
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np_img = np_img[:, :, np.newaxis] |
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np_img = np.transpose(np_img, (2, 0, 1)) |
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np_img = np_img.astype("float32") / 255 |
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return np_img |
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def resize_max_size( |
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np_img, size_limit: int, interpolation=cv2.INTER_CUBIC |
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) -> np.ndarray: |
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h, w = np_img.shape[:2] |
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if max(h, w) > size_limit: |
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ratio = size_limit / max(h, w) |
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new_w = int(w * ratio + 0.5) |
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new_h = int(h * ratio + 0.5) |
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return cv2.resize(np_img, dsize=(new_w, new_h), interpolation=interpolation) |
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else: |
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return np_img |
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def pad_img_to_modulo(img, mod): |
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channels, height, width = img.shape |
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out_height = ceil_modulo(height, mod) |
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out_width = ceil_modulo(width, mod) |
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return np.pad( |
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img, |
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((0, 0), (0, out_height - height), (0, out_width - width)), |
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mode="symmetric", |
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) |
