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utils.py
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utils.py
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import json
from typing import Union
import numpy as np
import torch
from PIL.JpegImagePlugin import JpegImageFile
from datasets.arrow_dataset import Dataset
from huggingface_hub import hf_hub_download
from matplotlib import pyplot as plt
# A colormap for visualizing segmentation results.
# https://github.com/tensorflow/models/blob/3f1ca33afe3c1631b733ea7e40c294273b9e406d/research/deeplab/utils/get_dataset_colormap.py#L51
# (date accessed: Nov 9th, 2022)
ade_palette = np.array(
[
[0, 0, 0],
[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_image_indices(dataset: Dataset, n: int):
image_indices = np.random.choice(dataset.num_rows, size=n, replace=False)
return [int(i) for i in image_indices]
# https://huggingface.co/datasets/huggingface/label-files/blob/main/ade20k-id2label.json
def get_labels():
repo_id = "huggingface/label-files"
filename = "ade20k-id2label.json"
id2label = json.load(
open(
hf_hub_download(repo_id=repo_id, filename=filename, repo_type="dataset"),
"r",
)
)
id2label = {int(k): v for k, v in id2label.items()}
label2id = {v: k for k, v in id2label.items()}
return id2label, label2id
def prepare_pixels_with_segmentation(
image: JpegImageFile, segmentation_maps: Union[torch.Tensor, np.array]
):
segmentation_maps = np.array(segmentation_maps)
color_segments = np.zeros(
(segmentation_maps.shape[0], segmentation_maps.shape[1], 3), dtype=np.uint8
)
for label, color in enumerate(ade_palette):
color_segments[segmentation_maps == label, :] = color
color_segments = color_segments[..., ::-1] # convert to BGR
pixels_with_segmentation = np.array(image) * 0.5 + color_segments * 0.5
return pixels_with_segmentation.astype(np.uint8)
def visualize_predictions(image: JpegImageFile, segmentation_maps: torch.Tensor):
pxs = prepare_pixels_with_segmentation(
image=image, segmentation_maps=segmentation_maps
)
plt.imshow(pxs)
plt.axis("off")
def display_example_images(dataset: Dataset, n: int = 2):
fig, axes = plt.subplots(nrows=n, ncols=n, figsize=(10, 10))
fig.set_tight_layout(True)
for i, j in enumerate(
np.random.choice(dataset.num_rows, size=(n * n), replace=False)
):
image_with_pixels = prepare_pixels_with_segmentation(
image=dataset[int(j)]["image"],
segmentation_maps=np.array(dataset[int(j)]["annotation"]),
)
axes[int(i / n), i % n].imshow(image_with_pixels)
axes[int(i / n), i % n].axis("off")
def convert_image_to_rgb(data_item):
if data_item["image"].mode != "RGB":
data_item["image"] = data_item["image"].convert(mode="RGB")
return data_item