Merge branch 'robots-at-runes/image-perturbations-2' into 'master'

WIP[robots-at-runes](perturbations) fonction de perturbations

See merge request polystar/robomaster/computer-vision/code!8

robots-at-runes/research/dataset/perturbations/brightness.py

+import numpy as np
+
+from polystar.common.models.image import Image
+
+
+def brightness(image: Image, intensity: float) -> Image:
+    MAX_BETA = 10
+    beta = MAX_BETA * intensity
+    perturbed_image = np.clip((image.astype(np.uint16) + beta), 0, 255).astype(np.uint8)
+    return perturbed_image
+
+
+if __name__ == "__main__":
+    from research.dataset.perturbations.utils import simple_perturbator_test
+
+    simple_perturbator_test(brightness)

robots-at-runes/research/dataset/perturbations/contrast.py

+import numpy as np
+
+from polystar.common.models.image import Image
+
+
+def contrast(image: Image, intensity: float) -> Image:
+    ALPHA_FACTOR = 0.7
+    MIN_ALPHA = 0.8
+    alpha = MIN_ALPHA + ALPHA_FACTOR * intensity
+    perturbed_image = np.clip((image.astype(np.uint16) * alpha), 0, 255).astype(np.uint8)
+    return perturbed_image
+
+
+if __name__ == "__main__":
+    from research.dataset.perturbations.utils import simple_perturbator_test
+
+    simple_perturbator_test(contrast)

robots-at-runes/research/dataset/perturbations/examples/.gitignore

+/res_*.png
\ No newline at end of file

robots-at-runes/research/dataset/perturbations/gaussian_blur.py

+import cv2
+
+from polystar.common.models.image import Image
+
+
+def to_odd_number(number):
+    return int(number // 2 * 2) - 1
+
+
+def gaussian_blur(image: Image, intensity: float) -> Image:
+    MAX_FACTOR = 0.015
+    blur_factor = intensity * MAX_FACTOR
+    width = image.shape[0]
+    height = image.shape[1]
+
+    x, y = to_odd_number(width * blur_factor), to_odd_number(height * blur_factor)
+    image = cv2.GaussianBlur(image, (x, y), cv2.BORDER_DEFAULT)
+    return image
+
+
+if __name__ == "__main__":
+    from research.dataset.perturbations.utils import simple_perturbator_test
+
+    simple_perturbator_test(gaussian_blur)

robots-at-runes/research/dataset/perturbations/gaussian_noise.py

+import numpy as np
+
+from polystar.common.models.image import Image
+
+
+def gaussian_noise(image: Image, intensity: float) -> Image:
+    MAX_VARIANCE = 300
+    mean = 0
+    variance = MAX_VARIANCE * intensity
+    sigma = variance ** 0.5
+    row, column, ch = image.shape
+    gaussian = np.random.normal(mean, sigma, (row, column, ch))
+    perturbed_image = np.clip((image.astype(np.uint16) + gaussian), 0, 255).astype(np.uint8)
+    return perturbed_image
+
+
+if __name__ == "__main__":
+    from research.dataset.perturbations.utils import simple_perturbator_test
+
+    simple_perturbator_test(gaussian_noise)

robots-at-runes/research/dataset/perturbations/horizontal_blur.py

+import cv2
+import numpy as np
+
+from polystar.common.models.image import Image
+
+
+def horizontal_blur(image: Image, intensity: float) -> Image:
+    MAX_KERNEL_SIZE = 11
+    kernel_size = int(MAX_KERNEL_SIZE * intensity) + 1
+    # Fill kernel with zeros
+    kernel_horizontal = np.zeros((kernel_size, kernel_size))
+    # Fill middle row with ones
+    kernel_horizontal[int(abs(kernel_size - 1) / 2), :] = np.ones(kernel_size)
+
+    # Normalize
+    kernel_horizontal /= kernel_size
+
+    return cv2.filter2D(image, -1, kernel_horizontal)
+
+
+if __name__ == "__main__":
+    from research.dataset.perturbations.utils import simple_perturbator_test
+
+    simple_perturbator_test(horizontal_blur)

robots-at-runes/research/dataset/perturbations/perturbator.py

+from dataclasses import dataclass
+from pathlib import Path
+from random import shuffle
+from typing import List, Callable
+
+import cv2
+import matplotlib.pyplot as plt
+import numpy as np
+
+from polystar.common.models.image import Image
+from research.dataset.perturbations.contrast import contrast
+from research.dataset.perturbations.gaussian_blur import gaussian_blur
+from research.dataset.perturbations.gaussian_noise import gaussian_noise
+from research.dataset.perturbations.horizontal_blur import horizontal_blur
+
+
+@dataclass
+class ImagePerturbator:
+    perturbator_functions: List[Callable[[Image, float], Image]]
+
+    def perturbate(self, image: Image) -> Image:
+        shuffle(self.perturbator_functions)
+        intensities = self._generate_intensities()
+        for perturbator_function, intensity in zip(self.perturbator_functions, intensities):
+            image = perturbator_function(image, intensity)
+        return image
+
+    def _generate_intensities(self) -> List[float]:
+        intensities = np.random.random(len(self.perturbator_functions))
+        return intensities / intensities.sum()
+
+
+if __name__ == "__main__":
+    EXAMPLE_DIR = Path(__file__).parent / "examples"
+    rune_img = Image.from_path(EXAMPLE_DIR / "test.png")
+    perturbator = ImagePerturbator([contrast, gaussian_blur, gaussian_noise, horizontal_blur])
+    rune_perturbed = perturbator.perturbate(rune_img)
+    cv2.imwrite(str(EXAMPLE_DIR / "res_full_pipeline.png"), cv2.cvtColor(rune_perturbed, cv2.COLOR_RGB2BGR))
+    plt.axis("off")
+    plt.tight_layout()
+    plt.imshow(rune_perturbed)
+    plt.show()

robots-at-runes/research/dataset/perturbations/saturation.py

+import numpy as np
+import cv2
+
+from polystar.common.models.image import Image
+
+
+def saturation(image: Image, intensity: float) -> Image:
+    MAX_SATURATION = 0.6
+    saturation_factor = 1 + MAX_SATURATION * intensity
+    image_hsv = cv2.cvtColor(image, cv2.COLOR_RGB2HSV)
+    (h, s, v) = cv2.split(image_hsv)
+    new_s = np.clip((s.astype(np.uint16) * saturation_factor), 0, 255).astype(np.uint8)
+    new_image_hsv = cv2.merge([h, new_s, v])
+    image_rgb = cv2.cvtColor(new_image_hsv, cv2.COLOR_HSV2RGB).astype(np.uint8)
+    return image_rgb
+
+
+if __name__ == "__main__":
+    from research.dataset.perturbations.utils import simple_perturbator_test
+
+    simple_perturbator_test(saturation)

robots-at-runes/research/dataset/perturbations/utils.py

+from collections import Callable
+from pathlib import Path
+
+import cv2
+import matplotlib.pyplot as plt
+import numpy as np
+
+from polystar.common.models.image import Image
+
+EXAMPLE_DIR = Path(__file__).parent / "examples"
+
+
+def simple_perturbator_test(perturbator_function: Callable):
+    rune_img = Image.from_path(EXAMPLE_DIR / "test.png")
+    rune_perturbed = perturbator_function(rune_img, 1)
+    cv2.imwrite(
+        str(EXAMPLE_DIR / f"res_{perturbator_function.__name__}.png"), cv2.cvtColor(rune_perturbed, cv2.COLOR_RGB2BGR)
+    )
+    side_by_side_display = np.hstack((rune_img, rune_perturbed))
+    h, w, _ = rune_img.shape
+    plt.figure(figsize=(12, 6 * h / w))
+    plt.axis("off")
+    plt.tight_layout()
+    plt.imshow(side_by_side_display)
+    plt.show()