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")