utils/tools_gradio.py

import cv2
import matplotlib.pyplot as plt
import numpy as np
import torch
from PIL import Image


def fast_process(
    annotations,
    image,
    device,
    scale,
    better_quality=False,
    mask_random_color=True,
    bbox=None,
    points=None,
    use_retina=True,
    withContours=True,
):
    if isinstance(annotations[0], dict):
        annotations = [annotation["segmentation"] for annotation in annotations]

    original_h = image.height
    original_w = image.width
    if better_quality:
        if isinstance(annotations[0], torch.Tensor):
            annotations = np.array(annotations.cpu())
        for i, mask in enumerate(annotations):
            mask = cv2.morphologyEx(
                mask.astype(np.uint8), cv2.MORPH_CLOSE, np.ones((3, 3), np.uint8)
            )
            annotations[i] = cv2.morphologyEx(
                mask.astype(np.uint8), cv2.MORPH_OPEN, np.ones((8, 8), np.uint8)
            )
    if device == "cpu":
        annotations = np.array(annotations)
        inner_mask = fast_show_mask(
            annotations,
            plt.gca(),
            random_color=mask_random_color,
            bbox=bbox,
            retinamask=use_retina,
            target_height=original_h,
            target_width=original_w,
        )
    else:
        if isinstance(annotations[0], np.ndarray):
            annotations = np.array(annotations)
            annotations = torch.from_numpy(annotations)
        inner_mask = fast_show_mask_gpu(
            annotations,
            plt.gca(),
            random_color=mask_random_color,
            bbox=bbox,
            retinamask=use_retina,
            target_height=original_h,
            target_width=original_w,
        )
    if isinstance(annotations, torch.Tensor):
        annotations = annotations.cpu().numpy()

    if withContours:
        contour_all = []
        temp = np.zeros((original_h, original_w, 1))
        for i, mask in enumerate(annotations):
            if type(mask) == dict:
                mask = mask["segmentation"]
            annotation = mask.astype(np.uint8)
            if use_retina == False:
                annotation = cv2.resize(
                    annotation,
                    (original_w, original_h),
                    interpolation=cv2.INTER_NEAREST,
                )
            contours, _ = cv2.findContours(
                annotation, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE
            )
            for contour in contours:
                contour_all.append(contour)
        cv2.drawContours(temp, contour_all, -1, (255, 255, 255), 2 // scale)
        color = np.array([0 / 255, 0 / 255, 255 / 255, 0.9])
        contour_mask = temp / 255 * color.reshape(1, 1, -1)

    image = image.convert("RGBA")
    overlay_inner = Image.fromarray((inner_mask * 255).astype(np.uint8), "RGBA")
    image.paste(overlay_inner, (0, 0), overlay_inner)

    if withContours:
        overlay_contour = Image.fromarray((contour_mask * 255).astype(np.uint8), "RGBA")
        image.paste(overlay_contour, (0, 0), overlay_contour)

    return image


# CPU post process
def fast_show_mask(
    annotation,
    ax,
    random_color=False,
    bbox=None,
    retinamask=True,
    target_height=960,
    target_width=960,
):
    mask_sum = annotation.shape[0]
    height = annotation.shape[1]
    weight = annotation.shape[2]
    # annotation is sorted by area
    areas = np.sum(annotation, axis=(1, 2))
    sorted_indices = np.argsort(areas)[::1]
    annotation = annotation[sorted_indices]

    index = (annotation != 0).argmax(axis=0)
    if random_color == True:
        color = np.random.random((mask_sum, 1, 1, 3))
    else:
        color = np.ones((mask_sum, 1, 1, 3)) * np.array(
            [30 / 255, 144 / 255, 255 / 255]
        )
    transparency = np.ones((mask_sum, 1, 1, 1)) * 0.6
    visual = np.concatenate([color, transparency], axis=-1)
    mask_image = np.expand_dims(annotation, -1) * visual

    mask = np.zeros((height, weight, 4))

    h_indices, w_indices = np.meshgrid(
        np.arange(height), np.arange(weight), indexing="ij"
    )
    indices = (index[h_indices, w_indices], h_indices, w_indices, slice(None))

    mask[h_indices, w_indices, :] = mask_image[indices]
    if bbox is not None:
        x1, y1, x2, y2 = bbox
        ax.add_patch(
            plt.Rectangle(
                (x1, y1), x2 - x1, y2 - y1, fill=False, edgecolor="b", linewidth=1
            )
        )

    if retinamask == False:
        mask = cv2.resize(
            mask, (target_width, target_height), interpolation=cv2.INTER_NEAREST
        )

    return mask


def fast_show_mask_gpu(
    annotation,
    ax,
    random_color=False,
    bbox=None,
    retinamask=True,
    target_height=960,
    target_width=960,
):
    device = annotation.device
    mask_sum = annotation.shape[0]
    height = annotation.shape[1]
    weight = annotation.shape[2]
    areas = torch.sum(annotation, dim=(1, 2))
    sorted_indices = torch.argsort(areas, descending=False)
    annotation = annotation[sorted_indices]
    # find the first non-zero subscript for each position
    index = (annotation != 0).to(torch.long).argmax(dim=0)
    if random_color == True:
        color = torch.rand((mask_sum, 1, 1, 3)).to(device)
    else:
        color = torch.ones((mask_sum, 1, 1, 3)).to(device) * torch.tensor(
            [30 / 255, 144 / 255, 255 / 255]
        ).to(device)
    transparency = torch.ones((mask_sum, 1, 1, 1)).to(device) * 0.6
    visual = torch.cat([color, transparency], dim=-1)
    mask_image = torch.unsqueeze(annotation, -1) * visual
    # index
    mask = torch.zeros((height, weight, 4)).to(device)
    h_indices, w_indices = torch.meshgrid(torch.arange(height), torch.arange(weight))
    indices = (index[h_indices, w_indices], h_indices, w_indices, slice(None))
    # make updates based on indices
    mask[h_indices, w_indices, :] = mask_image[indices]
    mask_cpu = mask.cpu().numpy()
    if bbox is not None:
        x1, y1, x2, y2 = bbox
        ax.add_patch(
            plt.Rectangle(
                (x1, y1), x2 - x1, y2 - y1, fill=False, edgecolor="b", linewidth=1
            )
        )
    if retinamask == False:
        mask_cpu = cv2.resize(
            mask_cpu, (target_width, target_height), interpolation=cv2.INTER_NEAREST
        )
    return mask_cpu