app.py

import gradio as gr
import numpy as np
import os
os.environ['CUDA_VISIBLE_DEVICES'] = '-1'

from glob import glob
import tensorflow as tf
import matplotlib.pyplot as plt
from skimage.transform import resize
from skimage.io import imsave
from skimage.filters import threshold_otsu

from doodleverse_utils.prediction_imports import *
from doodleverse_utils.imports import *


#load model
filepath = './saved_model'
model = tf.keras.models.load_model(filepath, compile = True)
model.compile

#segmentation
def segment(input_img, use_tta, use_otsu, dims=(512, 512)):

    N = 2

    if use_otsu:
        print("Use Otsu threshold")
    else:
        print("No Otsu threshold")

    if use_tta:
        print("Use TTA")
    else:
        print("Do not use TTA")


    worig, horig, channels = input_img.shape

    w, h = dims[0], dims[1]

    print("Original dimensions {}x{}".format(worig,horig))
    print("New dimensions {}x{}".format(w,h))

    img = standardize(input_img)
    
    img = resize(img, dims, preserve_range=True, clip=True) 
    
    img = np.expand_dims(img,axis=0)
    
    est_label = model.predict(img)

    if use_tta:
        #Test Time Augmentation
        est_label2 = np.flipud(model.predict((np.flipud(img)), batch_size=1))
        est_label3 = np.fliplr(model.predict((np.fliplr(img)), batch_size=1))
        est_label4 = np.flipud(np.fliplr(model.predict((np.flipud(np.fliplr(img))))))

        #soft voting - sum the softmax scores to return the new TTA estimated softmax scores
        est_label = est_label + est_label2 + est_label3 + est_label4
        est_label /= 4
    
    pred = np.squeeze(est_label, axis=0)
    pred = resize(pred, (worig, horig), preserve_range=True, clip=True)
    
    mask = np.argmax(pred,-1)

    imsave("greyscale_download_me.png", mask.astype('uint8'))
    
    class_label_colormap = [
        "#3366CC",
        "#DC3912",
        "#FF9900",
        "#109618",
        "#990099",
        "#0099C6",
        "#DD4477",
        "#66AA00",
        "#B82E2E",
        "#316395",
    ]
    
    # add classes
    class_label_colormap = class_label_colormap[:N]

    color_label = label_to_colors(
        mask,
        input_img[:, :, 0] == 0,
        alpha=128,
        colormap=class_label_colormap,
        color_class_offset=0,
        do_alpha=False,
    )
    
    imsave("color_download_me.png", color_label)


    if use_otsu:
        thres = threshold_otsu(mask)
        print("Otsu threshold is {}".format(thres))
        water_nowater = (mask>thres).astype('uint8')
    else:
        water_nowater = (mask>=1).astype('uint8')


    #overlay plot
    plt.clf()
    plt.subplot(121)
    plt.imshow(input_img[:,:,-1],cmap='gray')
    plt.imshow(color_label, alpha=0.4)
    plt.axis("off")
    plt.margins(x=0, y=0)

    plt.subplot(122)
    plt.imshow(input_img[:,:,-1],cmap='gray')
    plt.contour(water_nowater, levels=[0], colors='r')
    plt.axis("off")
    plt.margins(x=0, y=0)

    plt.savefig("overlay_download_me.png", dpi=300, bbox_inches="tight")    

    return color_label, plt , "greyscale_download_me.png", "color_download_me.png", "overlay_download_me.png"



with open("article.html", "r", encoding='utf-8') as f:
    article= f.read()

title = "Segment Satellite imagery" 
description = "This simple model demonstration segments 15-m Landsat-7/8 or 10-m Sentinel-2 RGB (visible spectrum) imagery into the following classes: 1. water and 2. other. Please note that, ordinarily, ensemble models are used in predictive mode. Here, we are using just one model, i.e. without ensembling. Allows upload of 3-band imagery in jpg format and download of label imagery only one at a time. " 


examples= [[l] for l in glob('examples/*.jpg')]

inp = gr.Image()
out1 = gr.Image(type='numpy')
out2 = gr.Plot(type='matplotlib')
out3 = gr.File()
out4 = gr.File()
out5 = gr.File()

inp2 = gr.inputs.Checkbox(default=False, label="Use TTA")
inp3 = gr.inputs.Checkbox(default=False, label="Use Otsu")

Segapp = gr.Interface(segment, [inp, inp2, inp3], 
                    [out1, out2, out3, out4, out5], 
                    title = title, description = description, examples=examples,  article=article,
                    theme="grass")
                    
Segapp.launch(enable_queue=True)