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| #credit to https://github.com/NielsRogge/Transformers-Tutorials/blob/master/SegFormer/Segformer_inference_notebook.ipynb for segmentation code | |
| import streamlit as st | |
| st.title("Segmentation of Beauty Products") | |
| file_name = st.file_uploader("Upload an image of a beauty product") | |
| from transformers import SegformerImageProcessor, SegformerForSemanticSegmentation | |
| import torch | |
| device = torch.device("cuda" if torch.cuda.is_available() else "cpu") | |
| model_name = "nvidia/segformer-b5-finetuned-ade-640-640" | |
| feature_extractor = SegformerImageProcessor.from_pretrained(model_name) | |
| model = SegformerForSemanticSegmentation.from_pretrained(model_name) | |
| model.to(device) | |
| from datasets import load_dataset | |
| from PIL import Image | |
| if file_name is not None: | |
| image = Image.open(file_name) | |
| st.image(image, caption='Image without Segmentation') | |
| pixel_values = feature_extractor(image, return_tensors="pt").pixel_values.to(device) | |
| outputs = model(pixel_values) | |
| logits = outputs.logits | |
| def ade_palette(): | |
| 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]] | |
| from torch import nn | |
| import numpy as np | |
| import matplotlib.pyplot as plt | |
| logits = nn.functional.interpolate(outputs.logits.detach().cpu(), | |
| size=image.size[::-1], | |
| mode='bilinear', | |
| align_corners=False) | |
| seg = logits.argmax(dim=1)[0] | |
| color_seg = np.zeros((seg.shape[0], seg.shape[1], 3), dtype=np.uint8) | |
| palette = np.array(ade_palette()) | |
| for label, color in enumerate(palette): | |
| color_seg[seg == label, :] = color | |
| color_seg = color_seg[..., ::-1] | |
| img = np.array(image) * 0.5 + color_seg * 0.5 | |
| img = img.astype(np.uint8) | |
| plt.figure(figsize=(15, 10)) | |
| plt.title("Image with Segmentation") | |
| st.pyplot(plt.gcf()) | |