File size: 2,172 Bytes
8f9088a |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 |
from sklearn.metrics import accuracy_score
import os
import pandas as pd
# import cv2
import numpy as np
import torch
from transformers import SegformerForSemanticSegmentation, SegformerFeatureExtractor
from torch import nn
import streamlit as st
raw_image = st.file_uploader('Raw Input Image')
if raw_image is not None:
df = pd.read_csv('class_dict_seg.csv')
classes = df['name']
palette = df[[' r', ' g', ' b']].values
id2label = classes.to_dict()
label2id = {v: k for k, v in id2label.items()}
image = np.asarray(raw_image)
feature_extractor = SegformerFeatureExtractor(align=False, reduce_zero_label=False)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = SegformerForSemanticSegmentation.from_pretrained("deep-learning-analytics/segformer_semantic_segmentation",
ignore_mismatched_sizes=True,
num_labels=len(id2label), id2label=id2label, label2id=label2id,
reshape_last_stage=True)
model = model.to(device)
# prepare the image for the model (aligned resize)
feature_extractor_inference = SegformerFeatureExtractor(do_random_crop=False, do_pad=False)
pixel_values = feature_extractor_inference(image, return_tensors="pt").pixel_values.to(device)
model.eval()
outputs = model(pixel_values=pixel_values)# logits are of shape (batch_size, num_labels, height/4, width/4)
logits = outputs.logits.cpu()
# First, rescale logits to original image size
upsampled_logits = nn.functional.interpolate(logits,
size=image.shape[:-1], # (height, width)
mode='bilinear',
align_corners=False)
# Second, apply argmax on the class dimension
seg = upsampled_logits.argmax(dim=1)[0]
color_seg = np.zeros((seg.shape[0], seg.shape[1], 3), dtype=np.uint8) # height, width, 3\
for label, color in enumerate(palette):
color_seg[seg == label, :] = color
# Convert to BGR
color_seg = color_seg[..., ::-1]
# Show image + mask
img = np.array(image) * 0.5 + color_seg * 0.5
img = img.astype(np.uint8)
st.image(img, caption="Segmented Image") |