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app.py

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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')
+
+
+

requirements.txt

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+PyWavelets
+opencv-python
+matplotlib
+pybase64
+scikit-learn
+joblib
+PyWavelets