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app.py

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+import os
+import streamlit as st
+import cv2
+import numpy as np
+from skimage.io import imread
+from skimage.transform import resize
+from skimage.feature import hog
+import torch
+import torch.nn as nn
+import torchvision.models as models
+import torchvision.transforms as transforms
+from PIL import Image
+import pickle
+
+# Load the pretrained resnet-50 model
+resnet = models.resnet50(pretrained=True)
+resnet = nn.Sequential(*list(resnet.children())[:-1])
+resnet.eval()
+
+# Load the demo model
+with open('demo.pkl', 'rb') as f:
+    demo = pickle.load(f)
+
+# defining some of the important functions that are needed 
+
+# defining feature extraction for resnet-50
+def extract_features(image, model):
+    image_np = np.array(image)
+    preprocess = transforms.Compose([
+        transforms.ToPILImage(),
+        transforms.Resize(256),
+        transforms.CenterCrop(224),
+        transforms.ToTensor(),
+        transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
+    ])
+    image = preprocess(image_np)
+    image = image.unsqueeze(0)
+    with torch.no_grad():
+        features = model(image)
+    features = features.squeeze(0)
+    return features
+
+
+# hog calculation
+def compute_hog(img):
+    resized_img = resize(img, (128*4, 64*4))
+    fd, hog_image = hog(resized_img, orientations=9, pixels_per_cell=(8, 8),
+                    cells_per_block=(2, 2), visualize=True,channel_axis=-1)
+    return fd
+
+def get_pixel(img, center, x, y):
+    new_value = 0
+    try:
+        if img[x][y] >= center:
+            new_value = 1
+    except:
+        pass
+    return new_value
+
+# calculate lbp
+
+def lbp_calculated_pixel(img, x, y):
+    center = img[x][y]
+    val_ar = []
+    val_ar.append(get_pixel(img, center, x-1, y+1))
+    val_ar.append(get_pixel(img, center, x, y+1))
+    val_ar.append(get_pixel(img, center, x+1, y+1))
+    val_ar.append(get_pixel(img, center, x+1, y))
+    val_ar.append(get_pixel(img, center, x+1, y-1))
+    val_ar.append(get_pixel(img, center, x, y-1))
+    val_ar.append(get_pixel(img, center, x-1, y-1))
+    val_ar.append(get_pixel(img, center, x-1, y))
+
+    power_val = [1, 2, 4, 8, 16, 32, 64, 128]
+    val = 0
+    for i in range(len(val_ar)):
+        val += val_ar[i] * power_val[i]
+    return val
+
+def calcLBP(img):
+    height, width, channel = img.shape
+    img_gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+    img_lbp = np.zeros((height, width,3), np.uint8)
+    for i in range(0, height):
+        for j in range(0, width):
+             img_lbp[i, j] = lbp_calculated_pixel(img_gray, i, j)
+    hist_lbp = cv2.calcHist([img_lbp], [0], None, [256], [0, 256])
+    return hist_lbp.flatten()
+
+# Function to infer the class of an image
+
+def infer(image_path):
+    image = imread(image_path) # reading the image
+    hog_feature = compute_hog(image) # hog features
+    lbp_feature = calcLBP(image)     # lbp features
+    cnn_feature = extract_features(image, resnet).numpy() #cnn features
+    hog_feature = hog_feature.reshape(-1) # reshaping
+    lbp_feature = lbp_feature.reshape(-1) #reshaping 
+    cnn_feature = cnn_feature.flatten() # cnn features
+    combined_feature = np.concatenate((hog_feature, lbp_feature, cnn_feature)) # combining all the features 
+    prediction = demo.predict([combined_feature])
+    return prediction[0]
+# Streamlit code
+
+st.title("Face Identification")
+uploaded_file = st.file_uploader("Choose an image...", type=["png"])
+
+if uploaded_file is not None:
+    with open("temp_image.png", "wb") as f:
+        f.write(uploaded_file.getvalue())
+    image = Image.open("temp_image.png")
+    st.image(image, caption='Uploaded Image', use_column_width=True)
+    st.write("")
+    st.write("Classifying...")
+    prediction = infer("temp_image.png")
+    st.write("Prediction:", prediction)

demo.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:332ac3f12fcbf253869c688b99862faec19190e447465b080eaf5a5e049ecd57
+size 4067527

requirements.txt

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+opencv-python 
+numpy 
+scikit-image 
+torch 
+torchvision
+streamlit
+Pillow
+pickle-mixin
+scikit-learn
+