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#!/usr/bin/env python
# coding: utf-8
# In[ ]:
import streamlit as st
import joblib
import json
import numpy as np
import base64
import cv2
import pywt
from PIL import Image
import io
__class_name_to_number = {}
__class_number_to_name = {}
__model = None
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 classify_image(image_base64_data, file_path=None):
imgs = get_cropped_image_if_2_eyes(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 class_number_to_name(class_num):
return __class_number_to_name[class_num]
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:
with open('./saved_model.pkl', 'rb') as f:
__model = joblib.load(f)
print("loading saved artifacts...done")
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 get_cropped_image_if_2_eyes(image_path, image_base64_data):
face_cascade = cv2.CascadeClassifier('./opencv/haarcascades/haarcascade_frontalface_default.xml')
eye_cascade = cv2.CascadeClassifier('./opencv/haarcascades/haarcascade_eye.xml')
if image_path:
img = cv2.imread(image_path)
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 get_b64_test_image_for_virat():
with open("b64.txt") as f:
return f.read()
# Load the saved artifacts at the start of the app
load_saved_artifacts()
st.title("Celebrity Image Classification")
uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "jpeg", "png"])
if uploaded_file is not None:
image = Image.open(uploaded_file)
st.image(image, caption='Uploaded Image', use_column_width=True)
# Convert PIL Image to base64
buffered = io.BytesIO()
image.save(buffered, format="PNG")
img_str = base64.b64encode(buffered.getvalue()).decode()
# Auto-classify the image
result = classify_image(f"data:image/png;base64,{img_str}", None)
if result:
st.subheader("Classification Results:")
for r in result:
# Create a dictionary of celebrity names and their probabilities
probabilities = dict(zip(r['class_dictionary'].keys(), r['class_probability']))
# Sort the probabilities in descending order
sorted_probabilities = sorted(probabilities.items(), key=lambda x: x[1], reverse=True)
# Create a DataFrame for the tabulator
import pandas as pd
df = pd.DataFrame(sorted_probabilities, columns=['Celebrity', 'Probability'])
# Display the tabulator
st.table(df)
# Display the top prediction
st.write(f"Top prediction: {r['class']} with {r['class_probability'][r['class_dictionary'][r['class']]]}% probability")
else:
st.write("No faces detected in the image.")