| import matplotlib |
| import matplotlib.cm as cm |
| import matplotlib.colors as mcolors |
| import numpy as np |
| import torch |
| import torchvision |
| from PIL import Image, ImageDraw, ImageFont |
| from einops import rearrange |
| from matplotlib import pyplot as plt |
|
|
|
|
| def get_similarity(image_encodings, label_encodings, target_shape, interpolation="bilinear", do_argmax=False): |
| """ |
| |
| Args: |
| image_encodings: |
| label_encodings: |
| target_shape: |
| interpolation: nearest, bilinear |
| do_argmax: |
| |
| Returns: |
| |
| """ |
|
|
| image_encodings = image_encodings.cpu() |
| label_encodings = label_encodings.cpu() |
|
|
| image_encodings = rearrange( |
| image_encodings, "b (h w) d -> d b h w", h=int(np.sqrt(image_encodings.shape[-2])) |
| ) |
| |
| scale_ratio = (target_shape[-2] / image_encodings.shape[-2], |
| target_shape[-1] / image_encodings.shape[-1],) |
| temp_list = [] |
| for i in image_encodings: |
| i = i.unsqueeze(1) |
| i = torch.nn.functional.interpolate( |
| i, scale_factor=scale_ratio, mode=interpolation |
| ) |
| temp_list.append(i) |
| image_encodings = torch.cat(temp_list, dim=1) |
|
|
| image_encodings = rearrange(image_encodings, "b d h w -> b h w d") |
| similarity = image_encodings @ label_encodings.T |
| similarity = rearrange(similarity, "b h w d-> b d h w") |
| if do_argmax: |
| similarity = torch.argmax(similarity, dim=1, keepdim=True).to(torch.float64) |
| return similarity |
|
|
|
|
| def get_cmap(ncolors): |
| if ncolors > 9: |
| cmap = plt.cm.tab20 |
| else: |
| cmap = plt.cm.tab10 |
| cmaplist = [cmap(i) for i in range(ncolors)] |
| cmap = matplotlib.colors.LinearSegmentedColormap.from_list("custom", cmaplist, ncolors) |
|
|
| mappable = cm.ScalarMappable(cmap=cmap) |
| mappable.set_array([]) |
| mappable.set_clim(-0.5, ncolors + 0.5) |
|
|
| return cmap, mappable |
|
|
|
|
| def vis_prediction(sample_text, img_arr, similarity): |
| N = len(sample_text) |
| cmap, mappable = get_cmap(N) |
|
|
| fig, axs = plt.subplots(1, 2) |
|
|
| _ = axs[0].imshow(img_arr) |
| _ = axs[1].imshow(img_arr) |
| _ = axs[1].imshow(similarity, cmap=cmap, interpolation="nearest", vmin=0, vmax=N, alpha=0.5) |
| axs[0].axis("off") |
| axs[1].axis("off") |
|
|
| fig.subplots_adjust(bottom=0.2) |
| cbar_ax = fig.add_axes([0.0, 0.85, 1.0, 0.05]) |
| colorbar = plt.colorbar(mappable, cax=cbar_ax, cmap=cmap, orientation="horizontal") |
| colorbar.set_ticks(np.linspace(0, N, N)) |
| colorbar.set_ticklabels(sample_text) |
|
|
| return fig |
|
|
|
|
| class DummyArgs: |
| def __init__(self, **kwargs): |
| self.__dict__.update(kwargs) |
|
|
|
|
| def get_transform(size=(224, 224)): |
| transform = torchvision.transforms.Compose([ |
| torchvision.transforms.Resize(size), |
| torchvision.transforms.ToTensor(), |
| torchvision.transforms.Normalize(mean=(0.48145466, 0.4578275, 0.40821073), |
| std=(0.26862954, 0.26130258, 0.27577711)) |
| ]) |
| return transform |
|
|
|
|
| def ade_palette(): |
| """ADE20K palette that maps each class to RGB values.""" |
| return [[120, 120, 120], [180, 120, 120], [6, 230, 230], [80, 50, 50], |
| [4, 200, 3], [120, 120, 80], [140, 140, 140], [204, 5, 255], |
| [230, 230, 230], [4, 250, 7], [224, 5, 255], [235, 255, 7], |
| [150, 5, 61], [120, 120, 70], [8, 255, 51], [255, 6, 82], |
| [143, 255, 140], [204, 255, 4], [255, 51, 7], [204, 70, 3], |
| [0, 102, 200], [61, 230, 250], [255, 6, 51], [11, 102, 255], |
| [255, 7, 71], [255, 9, 224], [9, 7, 230], [220, 220, 220], |
| [255, 9, 92], [112, 9, 255], [8, 255, 214], [7, 255, 224], |
| [255, 184, 6], [10, 255, 71], [255, 41, 10], [7, 255, 255], |
| [224, 255, 8], [102, 8, 255], [255, 61, 6], [255, 194, 7], |
| [255, 122, 8], [0, 255, 20], [255, 8, 41], [255, 5, 153], |
| [6, 51, 255], [235, 12, 255], [160, 150, 20], [0, 163, 255], |
| [140, 140, 140], [250, 10, 15], [20, 255, 0], [31, 255, 0], |
| [255, 31, 0], [255, 224, 0], [153, 255, 0], [0, 0, 255], |
| [255, 71, 0], [0, 235, 255], [0, 173, 255], [31, 0, 255], |
| [11, 200, 200], [255, 82, 0], [0, 255, 245], [0, 61, 255], |
| [0, 255, 112], [0, 255, 133], [255, 0, 0], [255, 163, 0], |
| [255, 102, 0], [194, 255, 0], [0, 143, 255], [51, 255, 0], |
| [0, 82, 255], [0, 255, 41], [0, 255, 173], [10, 0, 255], |
| [173, 255, 0], [0, 255, 153], [255, 92, 0], [255, 0, 255], |
| [255, 0, 245], [255, 0, 102], [255, 173, 0], [255, 0, 20], |
| [255, 184, 184], [0, 31, 255], [0, 255, 61], [0, 71, 255], |
| [255, 0, 204], [0, 255, 194], [0, 255, 82], [0, 10, 255], |
| [0, 112, 255], [51, 0, 255], [0, 194, 255], [0, 122, 255], |
| [0, 255, 163], [255, 153, 0], [0, 255, 10], [255, 112, 0], |
| [143, 255, 0], [82, 0, 255], [163, 255, 0], [255, 235, 0], |
| [8, 184, 170], [133, 0, 255], [0, 255, 92], [184, 0, 255], |
| [255, 0, 31], [0, 184, 255], [0, 214, 255], [255, 0, 112], |
| [92, 255, 0], [0, 224, 255], [112, 224, 255], [70, 184, 160], |
| [163, 0, 255], [153, 0, 255], [71, 255, 0], [255, 0, 163], |
| [255, 204, 0], [255, 0, 143], [0, 255, 235], [133, 255, 0], |
| [255, 0, 235], [245, 0, 255], [255, 0, 122], [255, 245, 0], |
| [10, 190, 212], [214, 255, 0], [0, 204, 255], [20, 0, 255], |
| [255, 255, 0], [0, 153, 255], [0, 41, 255], [0, 255, 204], |
| [41, 0, 255], [41, 255, 0], [173, 0, 255], [0, 245, 255], |
| [71, 0, 255], [122, 0, 255], [0, 255, 184], [0, 92, 255], |
| [184, 255, 0], [0, 133, 255], [255, 214, 0], [25, 194, 194], |
| [102, 255, 0], [92, 0, 255]] |
|
|
|
|
| def get_cmap_image(legend): |
| |
| width = 200 |
| height = len(legend) * 20 |
|
|
| |
| img = Image.new('RGB', (width, height), (255, 255, 255)) |
|
|
| |
| draw = ImageDraw.Draw(img) |
|
|
| |
| font = ImageFont.truetype('arial.ttf', 16) |
|
|
| |
| y = 0 |
| for label, color in legend.items(): |
| draw.rectangle((0, y, 20, y + 20), fill=color) |
| draw.text((30, y), label, font=font, fill=(0, 0, 0)) |
| y += 20 |
|
|
| return img |
|
|
