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nitinkaushik01 · Mar 22, 2020 · ee63295 · ee63295
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diff --git a/Build_Clothing_Prediction_Flask_Web_App/Clothing_Apparel_Prediction_Web_App/app.py b/Build_Clothing_Prediction_Flask_Web_App/Clothing_Apparel_Prediction_Web_App/app.py
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+#Import necessary libraries
+from flask import Flask, render_template, request
+import pandas as pd
+import numpy as np
+import tensorflow as tf
+from keras.preprocessing.image import load_img
+from keras.preprocessing.image import img_to_array
+from keras.models import load_model
+import os
+
+# Create flask instance
+app = Flask(__name__)
+
+#Set Max size of file as 10MB.
+app.config['MAX_CONTENT_LENGTH'] = 10 * 1024 * 1024
+
+#Allow files with extension png, jpg and jpeg
+ALLOWED_EXTENSIONS = ['png', 'jpg', 'jpeg']
+def allowed_file(filename):
+    return '.' in filename and \
+           filename.rsplit('.', 1)[1].lower() in ALLOWED_EXTENSIONS
+
+def init():
+    global graph
+    graph = tf.get_default_graph()
+
+# Function to load and prepare the image in right shape
+def read_image(filename):
+    # Load the image
+    img = load_img(filename, grayscale=True, target_size=(28, 28))
+    # Convert the image to array
+    img = img_to_array(img)
+    # Reshape the image into a sample of 1 channel
+    img = img.reshape(1, 28, 28, 1)
+    # Prepare it as pixel data
+    img = img.astype('float32')
+    img = img / 255.0
+    return img
+
+@app.route("/", methods=['GET', 'POST'])
+def home():
+
+    return render_template('home.html')
+
+@app.route("/predict", methods = ['GET','POST'])
+def predict():
+    if request.method == 'POST':
+        file = request.files['file']
+        try:
+            if file and allowed_file(file.filename):
+                filename = file.filename
+                file_path = os.path.join('static/images', filename)
+                file.save(file_path)
+                img = read_image(file_path)
+                # Predict the class of an image
+
+                with graph.as_default():
+                    model1 = load_model('clothing_classification_model.h5')
+                    class_prediction = model1.predict_classes(img)
+                    print(class_prediction)
+
+                #Map apparel category with the numerical class
+                if class_prediction[0] == 0:
+                  product = "T-shirt/top"
+                elif class_prediction[0] == 1:
+                  product = "Trouser"
+                elif class_prediction[0] == 2:
+                  product = "Pullover"
+                elif class_prediction[0] == 3:
+                  product = "Dress"
+                elif class_prediction[0] == 4:
+                  product = "Coat"
+                elif class_prediction[0] == 5:
+                  product = "Sandal"
+                elif class_prediction[0] == 6:
+                  product = "Shirt"
+                elif class_prediction[0] == 7:
+                  product = "Sneaker"
+                elif class_prediction[0] == 8:
+                  product = "Bag"
+                else:
+                  product = "Ankle boot"
+                return render_template('predict.html', product = product, user_image = file_path)
+        except Exception as e:
+            return "Unable to read the file. Please check if the file extension is correct."
+
+    return render_template('predict.html')
+
+if __name__ == "__main__":
+    init()
+    app.run()
diff --git a/...iction_Flask_Web_App/Clothing_Apparel_Prediction_Web_App/clothing_classification_model.h5 b/...iction_Flask_Web_App/Clothing_Apparel_Prediction_Web_App/clothing_classification_model.h5
diff --git a/...tion_Flask_Web_App/Clothing_Apparel_Prediction_Web_App/static/images/sandal.jpg b/...tion_Flask_Web_App/Clothing_Apparel_Prediction_Web_App/static/images/sandal.jpg
diff --git a/...Clothing_Prediction_Flask_Web_App/Clothing_Apparel_Prediction_Web_App/templates/home.html b/...Clothing_Prediction_Flask_Web_App/Clothing_Apparel_Prediction_Web_App/templates/home.html
@@ -0,0 +1,24 @@
+<html>
+<head>
+  <link rel="stylesheet" href="https://maxcdn.bootstrapcdn.com/bootstrap/4.0.0/css/bootstrap.min.css" integrity="sha384-Gn5384xqQ1aoWXA+058RXPxPg6fy4IWvTNh0E263XmFcJlSAwiGgFAW/dAiS6JXm" crossorigin="anonymous">
+    <title>Upload Apparel Image</title>
+</head>
+<body>
+
+
+
+    <div class="col-sm">
+      <h1><span class="badge badge-danger">Apparel/Clothing Prediction</span></h1>
+    </div>
+
+    <div class="col-sm">
+      <form action = "/predict" method = "post" enctype="multipart/form-data">
+          <input type="file" name="file" />
+          <input type = "submit" value="Upload">
+      </form>
+    </div>
+
+
+
+</body>
+</html>
diff --git a/...thing_Prediction_Flask_Web_App/Clothing_Apparel_Prediction_Web_App/templates/predict.html b/...thing_Prediction_Flask_Web_App/Clothing_Apparel_Prediction_Web_App/templates/predict.html
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+<!DOCTYPE html>
+<html>
+<head>
+  <!-- Required meta tags -->
+    <meta charset="utf-8">
+    <meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no">
+<link rel="stylesheet" href="https://maxcdn.bootstrapcdn.com/bootstrap/4.0.0/css/bootstrap.min.css" integrity="sha384-Gn5384xqQ1aoWXA+058RXPxPg6fy4IWvTNh0E263XmFcJlSAwiGgFAW/dAiS6JXm" crossorigin="anonymous">
+    <title>Apparel Prediction</title>
+</head>
+<body>
+
+  <div class="container">
+  <div class="row">
+
+    <div class="col-sm">
+      <span class="border border-primary">
+        <img src="{{ user_image }}" alt="User Image" class="img-thumbnail">
+      </span>
+    </div>
+
+    <div class="col-sm">
+      <h1>Fashion Tag <span class="badge badge-secondary">{{product}}</span></h1>
+    </div>
+
+  </div>
+</div>
+
+<script src="https://code.jquery.com/jquery-3.2.1.slim.min.js" integrity="sha384-KJ3o2DKtIkvYIK3UENzmM7KCkRr/rE9/Qpg6aAZGJwFDMVNA/GpGFF93hXpG5KkN" crossorigin="anonymous"></script>
+<script src="https://cdnjs.cloudflare.com/ajax/libs/popper.js/1.12.9/umd/popper.min.js" integrity="sha384-ApNbgh9B+Y1QKtv3Rn7W3mgPxhU9K/ScQsAP7hUibX39j7fakFPskvXusvfa0b4Q" crossorigin="anonymous"></script>
+<script src="https://maxcdn.bootstrapcdn.com/bootstrap/4.0.0/js/bootstrap.min.js" integrity="sha384-JZR6Spejh4U02d8jOt6vLEHfe/JQGiRRSQQxSfFWpi1MquVdAyjUar5+76PVCmYl" crossorigin="anonymous"></script>
+</body>
+</html>
diff --git a/Build_Clothing_Prediction_Flask_Web_App/Clothing_Classification_CNN.ipynb b/Build_Clothing_Prediction_Flask_Web_App/Clothing_Classification_CNN.ipynb
diff --git a/Build_Clothing_Prediction_Flask_Web_App/Clothing_Classification_CNN.py b/Build_Clothing_Prediction_Flask_Web_App/Clothing_Classification_CNN.py
@@ -0,0 +1,220 @@
+# -*- coding: utf-8 -*-
+"""Clothing_Classification_CNN.ipynb
+
+Automatically generated by Colaboratory.
+
+Original file is located at
+    https://colab.research.google.com/drive/18VR6CM3oaugAWZA2smOf0iKdvWrXXfU_
+
+### Clothing_Apparel_Classification
+"""
+
+#Import necessary Libraries
+
+import numpy as np
+from keras.datasets import fashion_mnist
+
+# Load fashion MNIST dataset
+(x_train, y_train), (x_test, y_test) = fashion_mnist.load_data()
+
+# Explore the dataset
+# Check the shape and size of x_train, x_test, y_train, y_test
+print ("Number of samples/observations in training data: " + str(len(x_train)))
+print ("Number of labels in training data: " + str(len(y_train)))
+print ("Dimensions of a single image in x_train:" + str(x_train[0].shape))
+print("-------------------------------------------------------------")
+print ("Number of samples/observations in test data: " + str(len(x_test)))
+print ("Number of labels in test data: " + str(len(y_test)))
+print ("Dimensions of single image in x_test:" + str(x_test[0].shape))
+
+"""### View sample images"""
+
+# Visualization library to visualize images 
+import matplotlib.pyplot as plt
+
+# Plotting 5 images, Subplot arugments represent nrows, ncols and index
+# Color map is set to grey since our image dataset is grayscale
+plt.subplot(231)
+random_num = np.random.randint(0,len(x_train))
+plt.imshow(x_train[random_num], cmap=plt.get_cmap('gray'))
+
+plt.subplot(232)
+random_num = np.random.randint(0,len(x_train))
+plt.imshow(x_train[random_num], cmap=plt.get_cmap('gray'))
+
+plt.subplot(233)
+random_num = np.random.randint(0,len(x_train))
+plt.imshow(x_train[random_num], cmap=plt.get_cmap('gray'))
+
+plt.subplot(234)
+random_num = np.random.randint(0,len(x_train))
+plt.imshow(x_train[random_num], cmap=plt.get_cmap('gray'))
+
+plt.subplot(235)
+random_num = np.random.randint(0,len(x_train))
+plt.imshow(x_train[random_num], cmap=plt.get_cmap('gray'))
+
+
+# Visualize the images
+plt.show()
+
+"""### Let's create our model"""
+
+#Import necessary keras specific libraries
+
+from keras.utils import np_utils
+import keras
+from keras.models import Sequential
+from keras.layers import Dense, Dropout, Flatten
+from keras.layers import Conv2D, MaxPooling2D, BatchNormalization
+from keras import backend as K
+
+# Setting Training Parameters like batch_size, epochs
+batch_size = 128
+epochs = 100
+
+# Storing the number of rows and columns
+img_rows = x_train[0].shape[0]
+img_cols = x_train[1].shape[0]
+
+''' Getting the data in the right 'shape' as required by Keras i.e. adding a 4th 
+dimension to our data thereby changing the original image shape of (60000,28,28) 
+to (60000,28,28,1)'''
+x_train = x_train.reshape(x_train.shape[0], img_rows, img_cols, 1)
+x_test = x_test.reshape(x_test.shape[0], img_rows, img_cols, 1)
+
+# Storing the shape of a single image 
+input_shape = (img_rows, img_cols, 1)
+
+# Changing image type to float32 data type
+x_train = x_train.astype('float32')
+x_test = x_test.astype('float32')
+
+# Normalizing the data by changing the image pixel range from (0 to 255) to (0 to 1)
+x_train /= 255
+x_test /= 255
+
+# Performing one hot encoding
+y_train = np_utils.to_categorical(y_train)
+y_test = np_utils.to_categorical(y_test)
+
+# Calculate the number of classes and number of pixels 
+num_classes = y_test.shape[1]
+num_pixels = x_train.shape[1] * x_train.shape[2]
+
+# Create CNN model
+model = Sequential()
+
+model.add(Conv2D(32, kernel_size=(3, 3),
+                 activation='relu',
+                 input_shape=input_shape))
+model.add(BatchNormalization())
+
+model.add(Conv2D(64, (3, 3), activation='relu'))
+model.add(BatchNormalization())
+
+model.add(MaxPooling2D(pool_size=(2, 2)))
+model.add(Dropout(0.25))
+
+model.add(Flatten())
+model.add(Dense(128, activation='relu'))
+model.add(BatchNormalization())
+
+model.add(Dropout(0.5))
+model.add(Dense(num_classes, activation='softmax'))
+
+model.compile(loss = 'categorical_crossentropy',
+              optimizer = keras.optimizers.Adadelta(),
+              metrics = ['accuracy'])
+
+print(model.summary())
+
+"""### Let's train our model"""
+
+model_fitting = model.fit(x_train, y_train,
+          batch_size=batch_size,
+          epochs=epochs,
+          verbose=1,
+          validation_data=(x_test, y_test))
+
+score = model.evaluate(x_test, y_test, verbose=0)
+print('Test loss:', score[0])
+print('Test accuracy:', score[1])
+
+# Configuration related preprocessing step before mounting the drive
+!apt-get install -y -qq software-properties-common python-software-properties module-init-tools
+!add-apt-repository -y ppa:alessandro-strada/ppa 2>&1 > /dev/null
+!apt-get update -qq 2>&1 > /dev/null
+!apt-get -y install -qq google-drive-ocamlfuse fuse
+from google.colab import auth
+auth.authenticate_user()
+from oauth2client.client import GoogleCredentials
+creds = GoogleCredentials.get_application_default()
+import getpass
+!google-drive-ocamlfuse -headless -id={creds.client_id} -secret={creds.client_secret} < /dev/null 2>&1 | grep URL
+vcode = getpass.getpass()
+!echo {vcode} | google-drive-ocamlfuse -headless -id={creds.client_id} -secret={creds.client_secret}
+
+#Mount the google drive
+from google.colab import drive
+drive.mount('/content/drive')
+
+# Change the directory to current working directory
+import os
+os.chdir("/content/drive/My Drive/Youtube_Projects")
+
+# Save the model with the name clothing_classification_model
+model.save('clothing_classification_model.h5')
+
+# Import few more necessary libraries.
+from keras.models import load_model
+from keras.preprocessing.image import load_img
+from keras.preprocessing.image import img_to_array
+
+# Function to load and prepare the image in right shape
+def load_image(filename):
+	# Load the image
+	img = load_img(filename, grayscale=True, target_size=(28, 28))
+	# Convert the image to array
+	img = img_to_array(img)
+	# Reshape the image into a sample of 1 channel
+	img = img.reshape(1, 28, 28, 1)
+	# Prepare it as pixel data
+	img = img.astype('float32')
+	img = img / 255.0
+	return img
+
+# Load an image and predict the apparel class
+img = load_image('sandal.jpg')
+# Load the saved model
+model = load_model('clothing_classification_model.h5')
+# Predict the apparel class
+class_prediction = model.predict_classes(img)
+print(class_prediction[0])
+
+#Map apparel category with the numerical class
+if class_prediction[0] == 0:
+  product = "T-shirt/top"
+elif class_prediction[0] == 1:
+  product = "Trouser"
+elif class_prediction[0] == 2:
+  product = "Pullover"
+elif class_prediction[0] == 3:
+  product = "Dress"
+elif class_prediction[0] == 4:
+  product = "Coat"
+elif class_prediction[0] == 5:
+  product = "Sandal"
+elif class_prediction[0] == 6:
+  product = "Shirt"
+elif class_prediction[0] == 7:
+  product = "Sneaker"
+elif class_prediction[0] == 8:
+  product = "Bag"
+else:
+  product = "Ankle boot"
+
+print(product)
+
+
+