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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 on aerial imagery", | |
| description = "model finetuned on IGN flair-one dataset", | |
| examples=["IMG_011942.jpeg","IMG_005339.jpeg","IMG_004753.jpeg","IMG_011617.jpeg","IMG_003022.jpeg"] | |
| ) | |
| demo.launch() #debug=True |