glou/image_segmentation

1#credit to https://github.com/NielsRogge/Transformers-Tutorials/blob/master/SegFormer/Segformer_inference_notebook.ipynb for segmentation code2import streamlit as st3 4st.title("Segmentation of Beauty Products")5 6file_name = st.file_uploader("Upload an image of a beauty product")7 8 9from transformers import SegformerImageProcessor, SegformerForSemanticSegmentation10import torch11 12device = torch.device("cuda" if torch.cuda.is_available() else "cpu")13 14model_name = "nvidia/segformer-b5-finetuned-ade-640-640"15feature_extractor = SegformerImageProcessor.from_pretrained(model_name)16model = SegformerForSemanticSegmentation.from_pretrained(model_name)17model.to(device)18 19 20from datasets import load_dataset21from PIL import Image22 23if file_name is not None:24    image = Image.open(file_name)25    st.image(image, caption='Image without Segmentation')26 27    pixel_values = feature_extractor(image, return_tensors="pt").pixel_values.to(device)28 29    outputs = model(pixel_values)30    logits = outputs.logits31 32    def ade_palette():33        return [[120, 120, 120], [180, 120, 120], [6, 230, 230], [80, 50, 50],34                [4, 200, 3], [120, 120, 80], [140, 140, 140], [204, 5, 255],35                [230, 230, 230], [4, 250, 7], [224, 5, 255], [235, 255, 7],36                [150, 5, 61], [120, 120, 70], [8, 255, 51], [255, 6, 82],37                [143, 255, 140], [204, 255, 4], [255, 51, 7], [204, 70, 3],38                [0, 102, 200], [61, 230, 250], [255, 6, 51], [11, 102, 255],39                [255, 7, 71], [255, 9, 224], [9, 7, 230], [220, 220, 220],40                [255, 9, 92], [112, 9, 255], [8, 255, 214], [7, 255, 224],41                [255, 184, 6], [10, 255, 71], [255, 41, 10], [7, 255, 255],42                [224, 255, 8], [102, 8, 255], [255, 61, 6], [255, 194, 7],43                [255, 122, 8], [0, 255, 20], [255, 8, 41], [255, 5, 153],44                [6, 51, 255], [235, 12, 255], [160, 150, 20], [0, 163, 255],45                [140, 140, 140], [250, 10, 15], [20, 255, 0], [31, 255, 0],46                [255, 31, 0], [255, 224, 0], [153, 255, 0], [0, 0, 255],47                [255, 71, 0], [0, 235, 255], [0, 173, 255], [31, 0, 255],48                [11, 200, 200], [255, 82, 0], [0, 255, 245], [0, 61, 255],49                [0, 255, 112], [0, 255, 133], [255, 0, 0], [255, 163, 0],50                [255, 102, 0], [194, 255, 0], [0, 143, 255], [51, 255, 0],51                [0, 82, 255], [0, 255, 41], [0, 255, 173], [10, 0, 255],52                [173, 255, 0], [0, 255, 153], [255, 92, 0], [255, 0, 255],53                [255, 0, 245], [255, 0, 102], [255, 173, 0], [255, 0, 20],54                [255, 184, 184], [0, 31, 255], [0, 255, 61], [0, 71, 255],55                [255, 0, 204], [0, 255, 194], [0, 255, 82], [0, 10, 255],56                [0, 112, 255], [51, 0, 255], [0, 194, 255], [0, 122, 255],57                [0, 255, 163], [255, 153, 0], [0, 255, 10], [255, 112, 0],58                [143, 255, 0], [82, 0, 255], [163, 255, 0], [255, 235, 0],59                [8, 184, 170], [133, 0, 255], [0, 255, 92], [184, 0, 255],60                [255, 0, 31], [0, 184, 255], [0, 214, 255], [255, 0, 112],61                [92, 255, 0], [0, 224, 255], [112, 224, 255], [70, 184, 160],62                [163, 0, 255], [153, 0, 255], [71, 255, 0], [255, 0, 163],63                [255, 204, 0], [255, 0, 143], [0, 255, 235], [133, 255, 0],64                [255, 0, 235], [245, 0, 255], [255, 0, 122], [255, 245, 0],65                [10, 190, 212], [214, 255, 0], [0, 204, 255], [20, 0, 255],66                [255, 255, 0], [0, 153, 255], [0, 41, 255], [0, 255, 204],67                [41, 0, 255], [41, 255, 0], [173, 0, 255], [0, 245, 255],68                [71, 0, 255], [122, 0, 255], [0, 255, 184], [0, 92, 255],69                [184, 255, 0], [0, 133, 255], [255, 214, 0], [25, 194, 194],70                [102, 255, 0], [92, 0, 255]]71 72    from torch import nn73    import numpy as np74    import matplotlib.pyplot as plt75 76    logits = nn.functional.interpolate(outputs.logits.detach().cpu(), 77                                       size=image.size[::-1],78                                       mode='bilinear',79                                       align_corners=False)80    seg = logits.argmax(dim=1)[0]81    color_seg = np.zeros((seg.shape[0], seg.shape[1], 3), dtype=np.uint8)82    palette = np.array(ade_palette())83    for label, color in enumerate(palette):84        color_seg[seg == label, :] = color85        86    color_seg = color_seg[..., ::-1]87        88    img = np.array(image) * 0.5 + color_seg * 0.589    img = img.astype(np.uint8)90    plt.figure(figsize=(15, 10))91    plt.title("Image with Segmentation")92    st.pyplot(plt.gcf())93