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| 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 Sports Person Classifier!") | |
| col1,col2,col3,col4,col5 = st.columns(5) | |
| with col1: | |
| messi = cv2.imread("messi.jpeg") | |
| #st.header("Lionel Messi") | |
| st.image(messi,width=150, caption='Lionel Messi') | |
| with col2: | |
| maria = cv2.imread("sharapova.jpeg") | |
| #st.header("Maria Sharapova") | |
| st.image(maria,width=150, caption='Maria Sharapova') | |
| with col3: | |
| roger = cv2.imread("federer.jpeg") | |
| #st.header("Roger Federer") | |
| st.image(roger,width=150, caption='Roger Federer') | |
| with col4: | |
| serena = cv2.imread("serena.jpeg") | |
| #st.header("Serena Williams") | |
| st.image(serena,width=150, caption='Serena Williams') | |
| with col5: | |
| virat = cv2.imread("virat.jpeg") | |
| #st.header("Virat Kohli") | |
| st.image(virat,width=150, caption='Virat Kohli') | |
| 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_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('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]) | |
| col6,col7 = st.columns(2) | |
| with col6: | |
| st.header("Uploded Image: ") | |
| st.image(img,width=130, caption='Uploaded Image') | |
| with col7: | |
| celeb = result[0]['class'] | |
| st.header("Predicted Image: ") | |
| if celeb == "lionel_messi": | |
| messi = cv2.imread("messi.jpeg") | |
| st.image(messi,width=150, caption='Lionel Messi') | |
| elif celeb == "maria_sharapova": | |
| maria = cv2.imread("sharapova.jpeg") | |
| st.image(maria,width=150, caption='Maria Sharapova') | |
| elif celeb == "roger_federer": | |
| roger = cv2.imread("federer.jpeg") | |
| st.image(roger,width=150, caption='Roger Federer') | |
| elif celeb == "serena_williams": | |
| serena = cv2.imread("serena.jpeg") | |
| st.image(serena,width=150, caption='Serena Williams') | |
| elif celeb == "virat_kohli": | |
| virat = cv2.imread("virat.jpeg") | |
| st.image(virat,width=150, caption='Virat Kohli') | |