Delete utils_tools_gradio.py

utils_tools_gradio.py

@@ -1,175 +0,0 @@
-import numpy as np
-from PIL import Image
-import matplotlib.pyplot as plt
-import cv2
-import torch
-
-
-def fast_process(
-    annotations,
-    image,
-    device,
-    scale,
-    better_quality=False,
-    mask_random_color=True,
-    bbox=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 = 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]
-    # 将annotation 按照面积 排序
-    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:
-        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 not retinamask:
-        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]
-    # 找每个位置第一个非零值下标
-    index = (annotation != 0).to(torch.long).argmax(dim=0)
-    if random_color:
-        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
-    # 按index取数，index指每个位置选哪个batch的数，把mask_image转成一个batch的形式
-    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))
-    # 使用向量化索引更新show的值
-    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 not retinamask:
-        mask_cpu = cv2.resize(
-            mask_cpu, (target_width, target_height), interpolation=cv2.INTER_NEAREST
-        )
-    return mask_cpu