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# Data Handling
from huggingface_hub import from_pretrained_keras
import numpy as np
import cv2
import imutils
import tensorflow as tf
from tensorflow import keras
import gradio as gr
from tensorflow.keras.models import load_model
model = load_model('./augmented_unet_pretrained.h5', compile=False)
def segmentation(inp):
#inp = cv2.cvtColor(inp, cv2.COLOR_BGR2RGB) # Input image
inp = cv2.resize(inp, (256, 256)) # Resize
inp = (inp.astype('float32')) / 255.
test_input = inp
# (Must Add cropping for real time images)
# Predictions
prediction_on_test = np.expand_dims(test_input, 0)
prediction_on_test = model.predict(prediction_on_test)
prediction_on_test = prediction_on_test > 0.5
predicted_img = prediction_on_test[0,:,:,0]
# EXTRACTING CONTOURS
predicted = predicted_img.astype(np.uint8)
cnts = cv2.findContours(image=predicted, mode=cv2.RETR_TREE, method=cv2.CHAIN_APPROX_NONE)
contours = imutils.grab_contours(cnts)
contoured = test_input.copy()
contoured = (contoured * 255).astype(np.uint8)
cv2.drawContours(image=contoured, contours=contours, contourIdx=-1, color=(255, 0, 0), thickness=1, lineType=cv2.LINE_AA)
# Circumference of detected Mask
if contours :
a = "Polynya Detected"
for i in range(len(contours)):
circum = cv2.arcLength(contours[i], True)
circum = round(circum,2)
b = str(circum) + '\t' + "px"
else:
a = "No Polynya Detected"
b = "0.0 px"
return(contoured, a, b)
image = gr.Image(label = 'Input Image')
out1 = gr.Image(label = 'Result')
out2 = gr.Textbox(label = 'Label')
out3 = gr.Textbox(label = 'Circumference in Pixel Unit')
interface = gr.Interface(fn = segmentation, inputs = image, outputs = [out1, out2, out3],
title= 'Polynya Detection',
description= 'Let the system detect if there is a Polynya in your image or not.',
share=True)
interface.launch() |