Merge branch 'common/image-perturbators' into 'master'

Common/image perturbators

See merge request polystar/robomaster/computer-vision/code!51

common/README.md

@@ -68,13 +68,13 @@ You have 2 options to download the videos:
 1. First, go on the [google sheet](https://docs.google.com/spreadsheets/d/1kIrMOjcKJ8hslZoVMx1D0H7QYj9nQLFvzUAQ1U4Le-I/edit#gid=0), and choose a video that nobody already did, and put your name in the 2nd column
 2. Download the video, in **720p**
 3. Rename it using the video id on twitch, and place it in [../dataset/twitch/videos](../dataset/twitch/videos)
-5. Launch the python script [./research_common/scripts/extract_robots_views_from_video.py](research_common/scripts/extract_robots_views_from_video.py), with the video id as parameter (In Pycharm, `Run` > `Edit Configurations...`, then in parameters enter the id). You can put multiple video ids by separating them with spaces.
+5. Launch the python script [./research/common/scripts/extract_robots_views_from_video.py](research/common/scripts/extract_robots_views_from_video.py), with the video id as parameter (In Pycharm, `Run` > `Edit Configurations...`, then in parameters enter the id). You can put multiple video ids by separating them with spaces.
 
 The frames will appear in the [../dataset/twitch/robots-views](../dataset/twitch/robots-views) folder.
 
 #### Aerial dataset
 
-Once you have the robots views in the [../dataset/twitch/robots-views](../dataset/twitch/robots-views) folder, run the python script [./research_common/scripts/move_aerial_views.py](research_common/scripts/move_aerial_views.py). It will put the aerial views in the [../dataset/twitch/aerial-views](../dataset/twitch/aerial-views) directory.
+Once you have the robots views in the [../dataset/twitch/robots-views](../dataset/twitch/robots-views) folder, run the python script [./research/common/scripts/move_aerial_views.py](research/common/scripts/move_aerial_views.py). It will put the aerial views in the [../dataset/twitch/aerial-views](../dataset/twitch/aerial-views) directory.
 
 
 

common/research_common/constants.py → common/research/common/constants.py

@@ -6,7 +6,7 @@ DSET_DIR: Path = PROJECT_DIR / "dataset"
 
 TWITCH_DSET_DIR: Path = DSET_DIR / "twitch"
 DJI_ROCO_DSET_DIR: Path = DSET_DIR / "dji_roco"
-DJI_ROCO_ZOOMED_DSET_DIR: Path = DSET_DIR / "dji_roco_zoomed_v1"
+DJI_ROCO_ZOOMED_DSET_DIR: Path = DSET_DIR / "dji_roco_zoomed_v2"
 TENSORFLOW_RECORDS_DIR: Path = DSET_DIR / "tf_records"
 TWITCH_ROBOTS_VIEWS_DIR: Path = TWITCH_DSET_DIR / "robots-views"
 TWITCH_DSET_ROBOTS_VIEWS_DIR: Path = TWITCH_DSET_DIR / "final-robots-views"

common/research_common/dataset/directory_roco_dataset.py → common/research/common/dataset/directory_roco_dataset.py

 from pathlib import Path
 from typing import Iterable
 
-from research_common.dataset.roco_dataset import ROCODataset
+from research.common.dataset.roco_dataset import ROCODataset
 
 
 class DirectoryROCODataset(ROCODataset):

common/research_common/dataset/dji/dji_roco_datasets.py → common/research/common/dataset/dji/dji_roco_datasets.py

 from enum import Enum
 
-from research_common.constants import DJI_ROCO_DSET_DIR
-from research_common.dataset.directory_roco_dataset import DirectoryROCODataset
+from research.common.constants import DJI_ROCO_DSET_DIR
+from research.common.dataset.directory_roco_dataset import DirectoryROCODataset
 
 
 class DJIROCODataset(DirectoryROCODataset, Enum):

common/research_common/dataset/dji/dji_roco_zoomed_datasets.py → common/research/common/dataset/dji/dji_roco_zoomed_datasets.py

 from enum import Enum, auto
 
 from polystar.common.utils.str_utils import camel2snake
-from research_common.constants import DJI_ROCO_ZOOMED_DSET_DIR
-from research_common.dataset.directory_roco_dataset import DirectoryROCODataset
+from research.common.constants import DJI_ROCO_ZOOMED_DSET_DIR
+from research.common.dataset.directory_roco_dataset import DirectoryROCODataset
 
 
 class DJIROCOZoomedDataset(DirectoryROCODataset, Enum):
     def __init__(self, _):
-        super().__init__(DJI_ROCO_ZOOMED_DSET_DIR / camel2snake(self.name), f"{self.name}ZoomedV1")
+        super().__init__(DJI_ROCO_ZOOMED_DSET_DIR / camel2snake(self.name), f"{self.name}ZoomedV2")
 
     CentralChina = auto()
     NorthChina = auto()

common/research_common/dataset/improvement/zoom.py → common/research/common/dataset/improvement/zoom.py

@@ -7,7 +7,7 @@ from polystar.common.models.box import Box
 from polystar.common.models.image_annotation import ImageAnnotation
 from polystar.common.target_pipeline.objects_validators.in_box_validator import InBoxValidator
 from polystar.common.view.plt_results_viewer import PltResultViewer
-from research_common.dataset.dji.dji_roco_datasets import DJIROCODataset
+from research.common.dataset.dji.dji_roco_datasets import DJIROCODataset
 
 
 def crop_image_annotation(image_annotation: ImageAnnotation, box: Box, min_coverage: float) -> ImageAnnotation:

common/research/common/dataset/perturbations/image_modifiers/brightness.py

+from dataclasses import dataclass
+
+import numpy as np
+
+from polystar.common.models.image import Image
+from research.common.dataset.perturbations.image_modifiers.image_modifier_abc import ImageModifierABC
+
+
+@dataclass
+class BrightnessModifier(ImageModifierABC):
+    max_offset: float = 10.0
+
+    def modify(self, image: Image, intensity: float) -> Image:
+        offset = self.max_offset * intensity
+        perturbed_image = np.clip((image.astype(np.uint16) + offset), 0, 255).astype(np.uint8)
+        return perturbed_image
+
+
+if __name__ == "__main__":
+    from research.common.dataset.perturbations.utils import simple_modifier_demo
+
+    simple_modifier_demo(BrightnessModifier())

common/research/common/dataset/perturbations/image_modifiers/contrast.py

+from dataclasses import dataclass
+
+import numpy as np
+
+from polystar.common.models.image import Image
+from research.common.dataset.perturbations.image_modifiers.image_modifier_abc import ImageModifierABC
+
+
+@dataclass
+class ContrastModifier(ImageModifierABC):
+    min_coef: float = 0.8
+    max_coef: float = 1.5
+
+    def modify(self, image: Image, intensity: float) -> Image:
+        coef = self.min_coef + (self.max_coef - self.min_coef) * intensity
+        perturbed_image = np.clip((image.astype(np.uint16) * coef), 0, 255).astype(np.uint8)
+        return perturbed_image
+
+
+if __name__ == "__main__":
+    from research.common.dataset.perturbations.utils import simple_modifier_demo
+
+    simple_modifier_demo(ContrastModifier())

common/research/common/dataset/perturbations/image_modifiers/gaussian_blur.py

+from dataclasses import dataclass
+
+import cv2
+
+from polystar.common.models.image import Image
+from research.common.dataset.perturbations.image_modifiers.image_modifier_abc import ImageModifierABC
+
+
+@dataclass
+class GaussianBlurrer(ImageModifierABC):
+    max_factor: float = 0.015
+
+    def modify(self, image: Image, intensity: float) -> Image:
+        blur_factor = intensity * self.max_factor
+        width, height, *_ = image.shape
+
+        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
+
+
+def _to_odd_number(number):
+    return int(number // 2 * 2) - 1
+
+
+if __name__ == "__main__":
+    from research.common.dataset.perturbations.utils import simple_modifier_demo
+
+    simple_modifier_demo(GaussianBlurrer())

common/research/common/dataset/perturbations/image_modifiers/gaussian_noise.py

+from dataclasses import dataclass
+
+import numpy as np
+
+from polystar.common.models.image import Image
+from research.common.dataset.perturbations.image_modifiers.image_modifier_abc import ImageModifierABC
+
+
+@dataclass
+class GaussianNoiser(ImageModifierABC):
+    max_variance: float = 300.0
+
+    def modify(self, image: Image, intensity: float) -> Image:
+        variance = self.max_variance * intensity
+        sigma = variance ** 0.5
+        row, column, ch = image.shape
+        gaussian = np.random.normal(0, 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.common.dataset.perturbations.utils import simple_modifier_demo
+
+    simple_modifier_demo(GaussianNoiser())

common/research/common/dataset/perturbations/image_modifiers/horizontal_blur.py

+from dataclasses import dataclass
+
+import cv2
+import numpy as np
+
+from polystar.common.models.image import Image
+from research.common.dataset.perturbations.image_modifiers.image_modifier_abc import ImageModifierABC
+
+
+@dataclass
+class HorizontalBlurrer(ImageModifierABC):
+    max_kernel_size: int = 11
+
+    def modify(self, image: Image, intensity: float) -> Image:
+        kernel = self._make_zero_kernel(intensity)
+        self._fill_middle_row_with_ones(kernel)
+        self._normalize_kernel(kernel)
+        return self._apply_kernel(image, kernel)
+
+    def _make_zero_kernel(self, intensity: float) -> np.ndarray:
+        kernel_size = int(self.max_kernel_size * intensity) + 1
+        return np.zeros((kernel_size, kernel_size))
+
+    @staticmethod
+    def _fill_middle_row_with_ones(kernel: np.ndarray):
+        kernel[len(kernel) // 2, :] = 1
+
+    @staticmethod
+    def _normalize_kernel(kernel: np.ndarray):
+        kernel /= len(kernel)
+
+    @staticmethod
+    def _apply_kernel(image: Image, kernel: np.ndarray) -> Image:
+        return cv2.filter2D(image, -1, kernel)
+
+
+if __name__ == "__main__":
+    from research.common.dataset.perturbations.utils import simple_modifier_demo
+
+    simple_modifier_demo(HorizontalBlurrer())

common/research/common/dataset/perturbations/image_modifiers/image_modifier_abc.py

+from abc import ABC, abstractmethod
+
+from polystar.common.models.image import Image
+
+
+class ImageModifierABC(ABC):
+    @abstractmethod
+    def modify(self, image: Image, intensity: float) -> Image:
+        pass