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 = st.columns(4) with col1: #dhoni = cv2.imread("dhoni.jpg") dhoni = Image.open("dhoni.jpg") st.image(dhoni,width=150, caption='MS Dhoni') #ganguly = cv2.imread("ganguly.jpg") ganguly = Image.open("ganguly.jpg") st.image(ganguly,width=150, caption='Saurav Ganguly') with col2: #rahul = cv2.imread("rahul.jpg") rahul = Image.open("rahul.jpg") st.image(rahul,width=150, caption='Rahul Dravid') #virat = cv2.imread("virat.jpg") virat = Image.open("virat.jpg") st.image(virat,width=150, caption='Virat Kohli') with col3: #sachin = cv2.imread("sachin.jpg") sachin = Image.open("sachin.jpg") st.image(sachin,width=150, caption='Sachin Tendulkar') #sehwag = cv2.imread("sehwag.jpg") sehwag = Image.open("sehwag.jpg") st.image(sehwag,width=150, caption='Virendra Sehwag') with col4: sunil_gavaskar = Image.open("sunil_gavaskar.jpg") st.image(sunil_gavaskar,width=150, caption='Sunil Gavaskar') #sehwag = cv2.imread("sehwag.jpg") kapil_dev = Image.open("kapil_dev.jpg") st.image(kapil_dev,width=150, caption='Kapil Dev') 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_dictionary1.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_model1.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) try: col6,col7 = st.columns(2) with col6: st.header("Uploded Image: ") dis_img = Image.open(display_image) st.image(dis_img,width=130, caption='Uploaded Image') with col7: celeb = result[0]['class'] st.header("Predicted Image: ") if celeb == "ms_dhoni": #dhoni = cv2.imread("dhoni.jpg") dhoni = Image.open("dhoni.jpg") st.image(dhoni,width=150, caption='MS Dhoni') elif celeb == "rahul_dravid": #dravid = cv2.imread("rahul.jpg") dravid = Image.open("rahul.jpg") st.image(dravid,width=150, caption='Rahul Dravid') elif celeb == "sachin_tendulkar": #sachin = cv2.imread("sachin.jpg") sachin = Image.open("sachin.jpg") st.image(sachin,width=150, caption='Sachin Tendulkar') elif celeb == "Saurav Ganguly": #ganguly = cv2.imread("ganguly.jpg") ganguly = Image.open("ganguly.jpg") st.image(ganguly,width=150, caption='Saurav Ganguly') elif celeb == "virat_kohli": #virat = cv2.imread("virat.jpg") virat = Image.open("virat.jpg") st.image(virat,width=150, caption='Virat Kohli') elif celeb == "Virendra Sehwag": #sehwag = cv2.imread("sehwag.jpg") sehwag = Image.open("sehwag.jpg") st.image(sehwag,width=150, caption='Virendra Sehwag') elif celeb == "sunil_gavaskar": sunil_gavaskar = Image.open("sunil_gavaskar.jpg") st.image(sunil_gavaskar,width=150, caption='Sunil Gavaskar') elif celeb == "kapil_dev": kapil_dev = Image.open("kapil_dev.jpg") st.image(kapil_dev,width=150, caption='Kapil Dev') except: st.header("Image Cannot be Classified!Please Try Again")