def sample_params()

in src/data_augmentations/__init__.py [0:0]

• 33 lines of code
• 10 McCabe index (conditional complexity)

    def sample_params(self, x, seed: int=None):
        if type(x) is torch.Tensor:
            assert len(x.size()) == 4
            width, height = x.size(3), x.size(2)
        elif type(x) is Image.Image:
            width, height = x.size

        if seed is not None:
            random.seed(seed)

        flip = random.randint(0, 1) if self.flip else 0
        area = width * height

        for attempt in range(10):
            target_area = random.uniform(*self.scale) * area
            log_ratio = (math.log(self.ratio[0]), math.log(self.ratio[1]))
            aspect_ratio = math.exp(random.uniform(*log_ratio))

            w = int(round(math.sqrt(target_area * aspect_ratio)))
            h = int(round(math.sqrt(target_area / aspect_ratio)))

            if w <= width and h <= height:
                i = random.randint(0, height - h)
                j = random.randint(0, width - w)
                return i, j, h, w, flip

        # Fallback to central crop
        in_ratio = width / height
        if (in_ratio < min(self.ratio)):
            w = width
            h = int(round(w / min(self.ratio)))
        elif (in_ratio > max(self.ratio)):
            h = height
            w = int(round(h * max(self.ratio)))
        else:  # whole image
            w = width
            h = height
        i = (height - h) // 2
        j = (width - w) // 2

        return i, j, h, w, flip