preprocess/humanparsing/mhp_extension/detectron2/projects/PointRend/point_rend/color_augmentation.py

# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
import numpy as np
import random
import cv2
from fvcore.transforms.transform import Transform


class ColorAugSSDTransform(Transform):
    """
    A color related data augmentation used in Single Shot Multibox Detector (SSD).

    Wei Liu, Dragomir Anguelov, Dumitru Erhan, Christian Szegedy,
       Scott Reed, Cheng-Yang Fu, Alexander C. Berg.
       SSD: Single Shot MultiBox Detector. ECCV 2016.

    Implementation based on:

     https://github.com/weiliu89/caffe/blob
       /4817bf8b4200b35ada8ed0dc378dceaf38c539e4
       /src/caffe/util/im_transforms.cpp

     https://github.com/chainer/chainercv/blob
       /7159616642e0be7c5b3ef380b848e16b7e99355b/chainercv
       /links/model/ssd/transforms.py
    """

    def __init__(
        self,
        img_format,
        brightness_delta=32,
        contrast_low=0.5,
        contrast_high=1.5,
        saturation_low=0.5,
        saturation_high=1.5,
        hue_delta=18,
    ):
        super().__init__()
        assert img_format in ["BGR", "RGB"]
        self.is_rgb = img_format == "RGB"
        del img_format
        self._set_attributes(locals())

    def apply_coords(self, coords):
        return coords

    def apply_segmentation(self, segmentation):
        return segmentation

    def apply_image(self, img, interp=None):
        if self.is_rgb:
            img = img[:, :, [2, 1, 0]]
        img = self.brightness(img)
        if random.randrange(2):
            img = self.contrast(img)
            img = self.saturation(img)
            img = self.hue(img)
        else:
            img = self.saturation(img)
            img = self.hue(img)
            img = self.contrast(img)
        if self.is_rgb:
            img = img[:, :, [2, 1, 0]]
        return img

    def convert(self, img, alpha=1, beta=0):
        img = img.astype(np.float32) * alpha + beta
        img = np.clip(img, 0, 255)
        return img.astype(np.uint8)

    def brightness(self, img):
        if random.randrange(2):
            return self.convert(
                img, beta=random.uniform(-self.brightness_delta, self.brightness_delta)
            )
        return img

    def contrast(self, img):
        if random.randrange(2):
            return self.convert(img, alpha=random.uniform(self.contrast_low, self.contrast_high))
        return img

    def saturation(self, img):
        if random.randrange(2):
            img = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
            img[:, :, 1] = self.convert(
                img[:, :, 1], alpha=random.uniform(self.saturation_low, self.saturation_high)
            )
            return cv2.cvtColor(img, cv2.COLOR_HSV2BGR)
        return img

    def hue(self, img):
        if random.randrange(2):
            img = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
            img[:, :, 0] = (
                img[:, :, 0].astype(int) + random.randint(-self.hue_delta, self.hue_delta)
            ) % 180
            return cv2.cvtColor(img, cv2.COLOR_HSV2BGR)
        return img