Web app development

__helpers__.py

@@ -19,15 +19,16 @@
 import urllib
 from tqdm import tqdm
 
+
 if tf.__version__[0] == '1':
 	tf.enable_eager_execution()
 # print("Eager Execution Initialized:",tf.executing_eagerly())
 
 
 # Starting session to download Images from URL if fed.
-s = requests.Session()
-s.proxies = {"http": "http://61.233.25.166:80"}
-r = s.get("http://www.google.com")
+# s = requests.Session()
+# s.proxies = {"http": "http://61.233.25.166:80"}
+# r = s.get("http://www.google.com")
 
 
 # Defining the Feature Layers we need respectively
@@ -60,8 +61,7 @@ def getModel():
   return models.Model(model.input, modelOutput)
 
 
- # Defining GRAM MATRIX
-
+# Defining GRAM MATRIX
 def gram(x):
 
   # number of channels
@@ -73,9 +73,9 @@ def gram(x):
 
   # gram matrix
   gram = tf.matmul(a, a, transpose_a=True)
-
   return gram / tf.cast(n, tf.float32)
 
+
 # Defining CONTENT COST
 def contentCost(contentFeatures, generateFeatures):
   return tf.reduce_mean(tf.square(contentFeatures-generateFeatures))
@@ -94,13 +94,13 @@ def getFeatures(content, style, model):
 
   # Extracting out the different features from the model output
   contentFeatures = [contentFeature[0] for contentFeature in contentOutputs[numStyleLayers:]]
-  styleFeatures = [styleFeature[0] for styleFeature in styleOutputs[:numStyleLayers]]
-
+  styleFeatures   = [styleFeature[0] for styleFeature in styleOutputs[:numStyleLayers]]
   return contentFeatures, styleFeatures
 
 
 def loadImage(path_to_img):
   max_dim = 512
+  # import pdb; pdb.set_trace()
   if type(path_to_img) == str:
     img2show = Image.open(path_to_img)
   else:
@@ -109,17 +109,19 @@ def loadImage(path_to_img):
   # long = max(img2show.size)
   long = (img2show.size)
 
+  long = long[0]
   scale = max_dim/long
   img = img2show.resize((round(img2show.size[0]*scale), round(img2show.size[1]*scale)), Image.ANTIALIAS)
-  
+
   img = image.img_to_array(img)
 
   # We need to broadcast the image array such that it has a batch dimension 
-  img = np.expand_dims(img2show, axis=0)
+  img = np.expand_dims(img, axis=0)
   return img2show, img
 
 
 def urlToImage(url):
+  import pdb; pdb.set_trace()
   resp = urllib.request.urlopen(url)
   img = np.asarray(bytearray(resp.read()), dtype='uint8')
   img = cv2.imdecode(img, cv2.IMREAD_COLOR)
@@ -132,15 +134,14 @@ def urlToImage(url):
 def inputImageAndPreprocess(path):
   # Loading the image and reshaping it according to VGG19 requirements.
   if path[:4]=='http':
-    # print("Loading Image from Internet...")
+    print("Loading Image from Internet...")
     img2show, img = urlToImage(path)
   else:
-    # print("Loading Image from Local...")
+    print("Loading Image from Local...")
     img2show, img = loadImage(path)
 
   # Preprocessing the img according to VGG19 requirements
   img = tf.keras.applications.vgg19.preprocess_input(img)
-
   return img2show, img
 
 
@@ -190,15 +191,13 @@ def totalLoss(model, lossWeights, generateImage, contentFeatures, styleFeatures)
   # Computing Style Cost for every layer
   for generateStyle, combinationStyle in zip(styleFeatures, styleGenerateFeatures):
     styleCostValue += styleWeightPerLayer * styleCost(combinationStyle[0], generateStyle)
-
 
   # Assigning the weights
   contentCostValue *= contentWeight
   styleCostValue *= styleWeight
 
   # Computing the Total Loss
   totalLossValue = contentCostValue + styleCostValue
-
   return totalLossValue, contentCostValue, styleCostValue
 
 
@@ -207,7 +206,6 @@ def computeGrads(config):
 	  allLoss = totalLoss(**config)
 
 	loss = allLoss[0]
-
 	return tape.gradient(loss, config['generateImage']), allLoss
 
 
@@ -252,7 +250,8 @@ def runStyleTransfer(contentPath,
                      SAVE_EVERY     = 0,
                      contentWeight  = 1e3,
                      styleWeight    = 1e-2,
-                     output_dirName = None):
+                     output_dirName = None,
+                     save_stats     = False):
 
   # Importing the Model
   model = getModel()
@@ -294,23 +293,22 @@ def runStyleTransfer(contentPath,
 
 
   normMeans = np.array([103.939, 116.779, 123.68])
-  minVals = -normMeans
-  maxVals = 255 - normMeans  
+  minVals   = -normMeans
+  maxVals   = 255 - normMeans  
 
   # Creating Logs to use for Plotting Later
   # global contentCostLog, styleCostLog, totalCostLog 
   contentCostLog, styleCostLog, totalCostLog = [], [], []
 
-
   if output_dirName:
-    PATH = os.path.join(os.curdir, 'outputs', output_dirName)
+    PATH = os.path.join(os.curdir, output_dirName)
     if not os.path.isdir(PATH):
       os.mkdir(PATH)
     os.chdir(PATH)
 
-  for iter in tqdm(range(iterations), leave=False):
 
-    # Computing the Grads and Loss
+  for iter in tqdm(range(iterations), leave=False):
+  # for iter in range(iterations):
     grads, allLoss = computeGrads(config)
 
     # Extracting different kinds of Losses
@@ -339,13 +337,13 @@ def runStyleTransfer(contentPath,
         new = deprocessImage(generateImage.numpy())
         cv2.imwrite(f'generateImage_{iter+1}.jpg', cv2.cvtColor(new, cv2.COLOR_BGR2RGB))
 
-
-
   bestImage = deprocessImage(generateImage.numpy())
-  cv2.imwrite(f'FINAL_OUTPUT.jpg', cv2.cvtColor(bestImage, cv2.COLOR_BGR2RGB))
-  # Saving the numpy Arrays to plot later
-  np.save('contentLoss.npy', contentCostLog)
-  np.save('styleLoss.npy', styleCostLog)
-  np.save('totalCostLoss.npy', totalCostLog)
+  cv2.imwrite(f'output.jpg', cv2.cvtColor(bestImage, cv2.COLOR_BGR2RGB))
+
+  if save_stats:
+    # Saving the numpy Arrays to plot later
+    np.save('contentLoss.npy', contentCostLog)
+    np.save('styleLoss.npy', styleCostLog)
+    np.save('totalCostLoss.npy', totalCostLog)
 
   return bestImage, (contentCostLog, styleCostLog, totalCostLog)

application.py

@@ -0,0 +1,88 @@
+from flask import Flask, render_template, request, redirect
+import numpy as np
+import joblib
+import os, time, random
+from PIL import Image
+import numpy as np
+
+from __helpers__ import *
+
+
+app = Flask(__name__)
+
+# Creating a user_file folder if not present in the working dir
+if 'temp_user_files' not in os.listdir():
+	os.mkdir('temp_user_files')
+# temp_user_file : It'll contain the images uploaded by the user temporarily
+
+app.config["IMAGE_UPLOADS"] = os.path.join(os.getcwd(), 'temp_user_files')
+home_dir = os.getcwd()
+
+# To DISABLE Caching
+@app.after_request
+def add_header(r):
+    """
+    Add headers to both force latest IE rendering engine or Chrome Frame,
+    and also to cache the rendered page for 10 minutes.
+    """
+    r.headers["Cache-Control"] = "no-cache, no-store, must-revalidate"
+    r.headers["Pragma"] = "no-cache"
+    r.headers["Expires"] = "0"
+    r.headers['Cache-Control'] = 'public, max-age=0'
+    return r
+
+# Defining the Home page
+@app.route('/', methods=['GET', 'POST'])
+def home():
+	return render_template('index.html')
+
+
+# Defining the Prediction page
+@app.route('/output', methods=['GET', 'POST'])
+def output():
+
+	if request.method == 'POST':
+		if request.form['submit'] == 'Do the magic!':
+
+	# 		# Reading the POST request
+			content_img = request.files['img1name']
+			style_img   = request.files['img2name']
+			iterations  = int(request.form['iterations'])
+			print('Iterations :',iterations)
+
+	# 		# Saving the Image file in local env
+			content_path = os.path.join(app.config["IMAGE_UPLOADS"], 'content_img.jpg')
+			style_path   = os.path.join(app.config["IMAGE_UPLOADS"], 'style_img.jpg')
+			content_img.save(content_path)
+			style_img.save(style_path)
+
+	# 		# Running Style Transfer
+			print('[INFO] Style Transfer Intializing..')
+			generatedImage, losses = runStyleTransfer(content_path,
+													style_path,
+													iterations     = iterations,
+													SAVE_EVERY     = 0,
+													contentWeight  = 1,
+													styleWeight    = 0.8,
+													output_dirName = 'static',
+													save_stats     = False)
+
+
+			# Deleting the file from the database
+			os.remove(content_path)
+			os.remove(style_path)
+
+			# Switching back to the main dir because Current dir is changed internally
+			os.chdir(home_dir)
+			print('[INFO] Output generated.')
+			return render_template('output.html')
+
+		print('[INFO] Rendering Output')
+		return render_template('index.html')
+
+
+
+if __name__ == '__main__':
+	app.run(port=5555, debug=True)
+
+# int(random.random()*10000)