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# -*- coding: utf-8 -*-
import os
import numpy as np
from glob import glob
import matplotlib.pyplot as plt
import matplotlib
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import backend as K
import pandas as pd
import gc
import random
import math
import glob
import torch
import gradio as gr
from PIL import Image
import cv2
classes = ['None','building','pervious surface','impervious surface','bare soil','water','coniferous','deciduous','brushwood','vineyard','herbaceous vegetation','agricultural land','plowed land']
id2label = pd.DataFrame(classes)[0].to_dict()
print(id2label)
label2id = {v: k for k, v in id2label.items()}
num_labels = len(id2label)
from transformers import SegformerForSemanticSegmentation, SegformerFeatureExtractor
segformer_b0_rgb_model = SegformerForSemanticSegmentation.from_pretrained("alanoix/segformer_b0_flair_one",
num_labels=len(id2label),
id2label=id2label,
label2id=label2id)
segformer_rgb_feature_extractor = SegformerFeatureExtractor(ignore_index=0, reduce_labels=False, do_resize=False, do_rescale=False, do_normalize=False)
segformer_b0_rgb_model= torch.quantization.quantize_dynamic(segformer_b0_rgb_model, {torch.nn.Linear}, dtype=torch.qint8)
import albumentations as aug
MEAN = np.array([0.44050665, 0.45704361, 0.42254708])
STD = np.array([0.20264351, 0.1782405 , 0.17575739])
test_transform = aug.Compose([
aug.Normalize(mean=MEAN, std=STD),
])
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
segformer_b0_rgb_model = segformer_b0_rgb_model.to(device)
class_colors = [(random.randint(0, 255), random.randint(
0, 255), random.randint(0, 255)) for _ in range(5000)]
# Default IMAGE_ORDERING = channels_last
IMAGE_ORDERING = "channels_last"
def get_colored_segmentation_image(seg_arr, n_classes, colors=class_colors):
output_height = seg_arr.shape[0]
output_width = seg_arr.shape[1]
seg_img = np.zeros((output_height, output_width, 3))
for c in range(n_classes):
seg_arr_c = seg_arr[:, :] == c
seg_img[:, :, 0] += ((seg_arr_c)*(colors[c][0])).astype('uint8')
seg_img[:, :, 1] += ((seg_arr_c)*(colors[c][1])).astype('uint8')
seg_img[:, :, 2] += ((seg_arr_c)*(colors[c][2])).astype('uint8')
return seg_img
def get_legends(class_names, colors=class_colors):
n_classes = len(class_names)
legend = np.zeros(((len(class_names) * 25) + 25, 125, 3),
dtype="uint8") + 255
class_names_colors = enumerate(zip(class_names[:n_classes],
colors[:n_classes]))
for (i, (class_name, color)) in class_names_colors:
color = [int(c) for c in color]
cv2.putText(legend, class_name, (5, (i * 25) + 17),
cv2.FONT_HERSHEY_COMPLEX, 0.5, (0, 0, 0), 1)
cv2.rectangle(legend, (100, (i * 25)), (125, (i * 25) + 25),
tuple(color), -1)
return legend
def overlay_seg_image(inp_img, seg_img):
orininal_h = inp_img.shape[0]
orininal_w = inp_img.shape[1]
seg_img = cv2.resize(seg_img, (orininal_w, orininal_h), interpolation=cv2.INTER_NEAREST)
fused_img = (inp_img/2 + seg_img/2).astype('uint8')
return fused_img
def concat_lenends(seg_img, legend_img):
new_h = np.maximum(seg_img.shape[0], legend_img.shape[0])
new_w = seg_img.shape[1] + legend_img.shape[1]
out_img = np.zeros((new_h, new_w, 3)).astype('uint8') + legend_img[0, 0, 0]
out_img[:legend_img.shape[0], : legend_img.shape[1]] = np.copy(legend_img)
out_img[:seg_img.shape[0], legend_img.shape[1]:] = np.copy(seg_img)
return out_img
def visualize_segmentation(seg_arr, inp_img=None, n_classes=None,
colors=class_colors, class_names=None,
overlay_img=False, show_legends=False,
prediction_width=None, prediction_height=None):
if n_classes is None:
n_classes = np.max(seg_arr)
seg_img = get_colored_segmentation_image(seg_arr, n_classes, colors=colors)
if inp_img is not None:
original_h = inp_img.shape[0]
original_w = inp_img.shape[1]
seg_img = cv2.resize(seg_img, (original_w, original_h), interpolation=cv2.INTER_NEAREST)
if (prediction_height is not None) and (prediction_width is not None):
seg_img = cv2.resize(seg_img, (prediction_width, prediction_height), interpolation=cv2.INTER_NEAREST)
if inp_img is not None:
inp_img = cv2.resize(inp_img,
(prediction_width, prediction_height))
if overlay_img:
assert inp_img is not None
seg_img = overlay_seg_image(inp_img, seg_img)
if show_legends:
assert class_names is not None
legend_img = get_legends(class_names, colors=colors)
seg_img = concat_lenends(seg_img, legend_img)
return seg_img
def query_image(img):
image_to_pred = test_transform(image=img)['image']
pixel_values = segformer_rgb_feature_extractor(image_to_pred, return_tensors="pt").pixel_values.to(device)
outputs_segformer_b0_rgb = segformer_b0_rgb_model(pixel_values=pixel_values)
pred_segformer_b0_rgb = outputs_segformer_b0_rgb.logits.cpu().detach().numpy()
pred = np.mean(np.array([K.softmax(pred_segformer_b0_rgb, axis = 1)]), axis = 0)
pred = tf.image.resize(tf.transpose(pred, perm=[0,2,3,1]), size = [512,512], method="bilinear") # resize to 512*512
pred = np.argmax(pred, axis = -1)
pred =np.squeeze(pred)
result = pred.astype(np.uint8)
class_names = [ 'None', 'building', 'pervious surface', 'impervious surface', 'bare soil','water','coniferous','deciduous','brushwood','vineyard', 'herbaceous vegetation', 'agricultural land', 'plowed land']
seg_img = visualize_segmentation(result, img, n_classes=13,
colors=class_colors , overlay_img=True,
show_legends=True,
class_names=class_names,
prediction_width=512,
prediction_height=512)
return seg_img
demo = gr.Interface(
query_image,
inputs=[gr.Image()],
outputs="image",
title="Image Segmentation Demo",
description = "Please upload an image to see segmentation capabilities of this model",
examples=["examples/IMG_011942.jpeg","examples/IMG_005339.jpeg","examples/IMG_004753.jpeg","examples/IMG_011617.jpeg","examples/IMG_003022.jpeg"]
)
demo.launch() #debug=True |