app.py

import streamlit as st
import pickle
import base64
import json
import numpy as np
import cv2
import pywt
import joblib 
from PIL import Image



__class_name_to_number = {}
__class_number_to_name = {}
__model = None

st.header("Welcome to Indian Cricketers Classifier!")
col1,col2,col3,col4,col5,col6 = st.columns(6)
with col1: 
    dhoni = cv2.imread("dhoni.jpg")
    st.image(dhoni,width=100, caption='MS Dhoni')
with col2:
    dravid = cv2.imread("rahul.jpg")
    st.image(dravid,width=100, caption='Rahul Dravid')
with col3:
    sachin = cv2.imread("sachin.jpg")
    st.image(sachin,width=100, caption='Sachin Tendulkar')
with col4:
    ganguly = cv2.imread("ganguly.jpg")
    st.image(ganguly,width=100, caption='Saurav Ganguly')
with col5:
    virat = cv2.imread("virat.jpg")
    st.image(virat,width=100, caption='Virat Kohli')
with col5:
    sehwag = cv2.imread("sehwag.jpg")
    st.image(sehwag,width=100, caption='Virendra Sehwag')



def classify_image(image_base64_data, file_path=None):

    imgs = get_cropped_image_if_2_eyes_new(file_path, image_base64_data)

    result = []
    for img in imgs:
        scalled_raw_img = cv2.resize(img, (32, 32))
        img_har = w2d(img, 'db1', 5)
        scalled_img_har = cv2.resize(img_har, (32, 32))
        combined_img = np.vstack((scalled_raw_img.reshape(32 * 32 * 3, 1), scalled_img_har.reshape(32 * 32, 1)))

        len_image_array = 32*32*3 + 32*32

        final = combined_img.reshape(1,len_image_array).astype(float)
        result.append({
            'class': class_number_to_name(__model.predict(final)[0]),
            'class_probability': np.around(__model.predict_proba(final)*100,2).tolist()[0],
            'class_dictionary': __class_name_to_number
        })

    return result


def get_cropped_image_if_2_eyes_new(file_path, image_base64_data):
    face_cascade = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')
    eye_cascade = cv2.CascadeClassifier('haarcascade_eye.xml')

    if file_path:
        img = cv2.imread(file_path)
        #st.image(img,width=150, caption='Uploaded Image')
    else:
        img = get_cv2_image_from_base64_string(image_base64_data)

    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
    faces = face_cascade.detectMultiScale(gray, 1.3, 5)

    cropped_faces = []
    for (x,y,w,h) in faces:
            roi_gray = gray[y:y+h, x:x+w]
            roi_color = img[y:y+h, x:x+w]
            eyes = eye_cascade.detectMultiScale(roi_gray)
            if len(eyes) >= 2:
                cropped_faces.append(roi_color)
    return cropped_faces


def w2d(img, mode='haar', level=1):
    imArray = img
    #Datatype conversions
    #convert to grayscale
    imArray = cv2.cvtColor( imArray,cv2.COLOR_RGB2GRAY )
    #convert to float
    imArray =  np.float32(imArray)   
    imArray /= 255;
    # compute coefficients 
    coeffs=pywt.wavedec2(imArray, mode, level=level)

    #Process Coefficients
    coeffs_H=list(coeffs)  
    coeffs_H[0] *= 0;  

    # reconstruction
    imArray_H=pywt.waverec2(coeffs_H, mode);
    imArray_H *= 255;
    imArray_H =  np.uint8(imArray_H)

    return imArray_H

def get_cv2_image_from_base64_string(b64str):
    '''
    credit: https://stackoverflow.com/questions/33754935/read-a-base-64-encoded-image-from-memory-using-opencv-python-library
    :param uri:
    :return:
    '''
    encoded_data = b64str.split(',')[1]
    nparr = np.frombuffer(base64.b64decode(encoded_data), np.uint8)
    img = cv2.imdecode(nparr, cv2.IMREAD_COLOR)
    return img

def load_saved_artifacts():
    #print("loading saved artifacts...start")
    global __class_name_to_number
    global __class_number_to_name

    with open("class_cri_dictionary.json", "r") as f:
        __class_name_to_number = json.load(f)
        __class_number_to_name = {v:k for k,v in __class_name_to_number.items()}

    global __model
    if __model is None:
        __model = joblib.load('cri_saved_model.pkl') 
    #st.text("loading saved artifacts...done")
    return True

def class_number_to_name(class_num):
    return __class_number_to_name[class_num]

def get_b64_test_image_for_virat():
    with open("b64.txt") as f:
        return f.read()

def save_uploaded_image(uploaded_image):
    try:
        with open(uploaded_image.name, 'wb') as f:
            f.write(uploaded_image.getbuffer())
        return {"complete":True, "filename":uploaded_image.name}
    except:
        return {"complete":False, "filename":""}


uploaded_image = st.file_uploader('Choose an image')

if uploaded_image is not None:
    # save the image in a directory
    image_dict = save_uploaded_image(uploaded_image)
    
    if image_dict["complete"]:
        display_image = image_dict["filename"]
        st.header("Image Uploded!, Processing...")
        if load_saved_artifacts():
            img = cv2.imread(display_image)
            img = cv2.resize(img, (130, 130))
            
            result = classify_image(get_b64_test_image_for_virat(), display_image)
            st.text(result[0])