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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')
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