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import streamlit as st | |
import pandas as pd | |
import matplotlib.pyplot as plt | |
import numpy as np | |
from PIL import Image,ImageOps | |
import numpy as np | |
import random | |
import cv2 | |
import tensorflow as tf | |
from tensorflow import keras | |
from keras.models import load_model | |
import time | |
# import mysql.connector | |
import process_input_pipeline as pp | |
import img_extractor | |
from process_input_pipeline import ImageResizer, ContrastEnhancer | |
# added for inception concat | |
from sklearn.pipeline import make_pipeline | |
from sklearn.base import BaseEstimator, TransformerMixin | |
import combined_model | |
cm = combined_model.build_regression_model() | |
cm.load_weights(r"F:\python\web_streamlit\ALL_IN_ONE\model_weights\model_weights\combined_weights_7_epoch_third_acc.h5") | |
roi=img_extractor.RoiExtractor() | |
def process_image(img, img_size=(224, 224)): | |
try: | |
new_size=(299,299) | |
resized_img = img.resize(new_size, Image.BICUBIC) | |
img_array=np.array(resized_img) | |
clahe = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(64, 64)) | |
cl1 = clahe.apply(img_array) | |
cl2=cv2.cvtColor(cl1,cv2.COLOR_GRAY2BGR) | |
return cl2 | |
except Exception as e: | |
st.error(f"Error processing image: {e}") | |
return None | |
def predict(img_data_c,img_data_m, gender): | |
try: | |
if gender == 'Female': | |
# gender_input = np.array([0]) | |
gender_input=0 | |
elif gender == 'Male': | |
gender_input = 1 | |
pred =cm.predict([np.array([img_data_c]),np.array([img_data_m]), np.array([gender_input])]) | |
# pred = model.predict(img_data) | |
return pred | |
except Exception as e: | |
st.error(f"Error predicting bone age: {e}") | |
return None | |