Initial commit

metric_depth/zoedepth/data/preprocess.py

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+# MIT License
+
+# Copyright (c) 2022 Intelligent Systems Lab Org
+
+# Permission is hereby granted, free of charge, to any person obtaining a copy
+# of this software and associated documentation files (the "Software"), to deal
+# in the Software without restriction, including without limitation the rights
+# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+# copies of the Software, and to permit persons to whom the Software is
+# furnished to do so, subject to the following conditions:
+
+# The above copyright notice and this permission notice shall be included in all
+# copies or substantial portions of the Software.
+
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+
+# File author: Shariq Farooq Bhat
+
+import numpy as np
+from dataclasses import dataclass
+from typing import Tuple, List
+
+# dataclass to store the crop parameters
+@dataclass
+class CropParams:
+    top: int
+    bottom: int
+    left: int
+    right: int
+
+
+
+def get_border_params(rgb_image, tolerance=0.1, cut_off=20, value=0, level_diff_threshold=5, channel_axis=-1, min_border=5) -> CropParams:
+    gray_image = np.mean(rgb_image, axis=channel_axis)
+    h, w = gray_image.shape
+
+
+    def num_value_pixels(arr):
+        return np.sum(np.abs(arr - value) < level_diff_threshold)
+
+    def is_above_tolerance(arr, total_pixels):
+        return (num_value_pixels(arr) / total_pixels) > tolerance
+
+    # Crop top border until number of value pixels become below tolerance
+    top = min_border
+    while is_above_tolerance(gray_image[top, :], w) and top < h-1:
+        top += 1
+        if top > cut_off:
+            break
+
+    # Crop bottom border until number of value pixels become below tolerance
+    bottom = h - min_border
+    while is_above_tolerance(gray_image[bottom, :], w) and bottom > 0:
+        bottom -= 1
+        if h - bottom > cut_off:
+            break
+
+    # Crop left border until number of value pixels become below tolerance
+    left = min_border
+    while is_above_tolerance(gray_image[:, left], h) and left < w-1:
+        left += 1
+        if left > cut_off:
+            break
+
+    # Crop right border until number of value pixels become below tolerance
+    right = w - min_border
+    while is_above_tolerance(gray_image[:, right], h) and right > 0:
+        right -= 1
+        if w - right > cut_off:
+            break
+        
+
+    return CropParams(top, bottom, left, right)
+
+
+def get_white_border(rgb_image, value=255, **kwargs) -> CropParams:
+    """Crops the white border of the RGB.
+
+    Args:
+        rgb: RGB image, shape (H, W, 3).
+    Returns:
+        Crop parameters.
+    """
+    if value == 255:
+        # assert range of values in rgb image is [0, 255]
+        assert np.max(rgb_image) <= 255 and np.min(rgb_image) >= 0, "RGB image values are not in range [0, 255]."
+        assert rgb_image.max() > 1, "RGB image values are not in range [0, 255]."
+    elif value == 1:
+        # assert range of values in rgb image is [0, 1]
+        assert np.max(rgb_image) <= 1 and np.min(rgb_image) >= 0, "RGB image values are not in range [0, 1]."
+
+    return get_border_params(rgb_image, value=value, **kwargs)
+
+def get_black_border(rgb_image, **kwargs) -> CropParams:
+    """Crops the black border of the RGB.
+
+    Args:
+        rgb: RGB image, shape (H, W, 3).
+
+    Returns:
+        Crop parameters.
+    """
+
+    return get_border_params(rgb_image, value=0, **kwargs)
+
+def crop_image(image: np.ndarray, crop_params: CropParams) -> np.ndarray:
+    """Crops the image according to the crop parameters.
+
+    Args:
+        image: RGB or depth image, shape (H, W, 3) or (H, W).
+        crop_params: Crop parameters.
+
+    Returns:
+        Cropped image.
+    """
+    return image[crop_params.top:crop_params.bottom, crop_params.left:crop_params.right]
+
+def crop_images(*images: np.ndarray, crop_params: CropParams) -> Tuple[np.ndarray]:
+    """Crops the images according to the crop parameters.
+
+    Args:
+        images: RGB or depth images, shape (H, W, 3) or (H, W).
+        crop_params: Crop parameters.
+
+    Returns:
+        Cropped images.
+    """
+    return tuple(crop_image(image, crop_params) for image in images)
+
+def crop_black_or_white_border(rgb_image, *other_images: np.ndarray, tolerance=0.1, cut_off=20, level_diff_threshold=5) -> Tuple[np.ndarray]:
+    """Crops the white and black border of the RGB and depth images.
+
+    Args:
+        rgb: RGB image, shape (H, W, 3). This image is used to determine the border.
+        other_images: The other images to crop according to the border of the RGB image.
+    Returns:
+        Cropped RGB and other images.
+    """
+    # crop black border
+    crop_params = get_black_border(rgb_image, tolerance=tolerance, cut_off=cut_off, level_diff_threshold=level_diff_threshold)
+    cropped_images = crop_images(rgb_image, *other_images, crop_params=crop_params)
+
+    # crop white border
+    crop_params = get_white_border(cropped_images[0], tolerance=tolerance, cut_off=cut_off, level_diff_threshold=level_diff_threshold)
+    cropped_images = crop_images(*cropped_images, crop_params=crop_params)
+
+    return cropped_images
+