Migrating code to work on tensorflow 2.0 and adding running on directory

classifiers.py

@@ -1,8 +1,9 @@
 # -*- coding:utf-8 -*-
 
-from keras.models import Model as KerasModel
-from keras.layers import Input, Dense, Flatten, Conv2D, MaxPooling2D, BatchNormalization, Dropout, Reshape, Concatenate, LeakyReLU
-from keras.optimizers import Adam
+
+from tensorflow.keras import Model as KerasModel
+from tensorflow.keras.layers import Input, Dense, Flatten, Conv2D, MaxPooling2D, BatchNormalization, Dropout, Reshape, Concatenate, LeakyReLU
+from tensorflow.keras.optimizers import Adam
 
 IMGWIDTH = 256
 

example.py

@@ -2,7 +2,9 @@
 from classifiers import *
 from pipeline import *
 
-from keras.preprocessing.image import ImageDataGenerator
+import os
+
+from tensorflow.keras.preprocessing.image import ImageDataGenerator
 
 # 1 - Load the model and its pretrained weights
 classifier = Meso4()
@@ -17,15 +19,19 @@
         'test_images',
         target_size=(256, 256),
         batch_size=1,
+        shuffle=False,
         class_mode='binary',
         subset='training')
 
 # 3 - Predict
-X, y = generator.next()
-print('Predicted :', classifier.predict(X), '\nReal class :', y)
+num_iterations = 0
+for X, y in generator:
+        print('Predicted :', classifier.predict(X), '\nReal class :', y)
+        num_iterations += 1
+        if num_iterations >= 4:
+                break
 
 # 4 - Prediction for a video dataset
-
 classifier.load('weights/Meso4_F2F')
 
 predictions = compute_accuracy(classifier, 'test_videos')

predict_on_directory.py

@@ -0,0 +1,35 @@
+########################################################################################################################
+# Model
+########################################################################################################################
+
+import numpy as np
+from classifiers import *
+from pipeline import *
+
+import os
+import glob
+import sys
+from tensorflow.keras.preprocessing.image import load_img, img_to_array
+
+REQUIRED_SIZE = (256, 256)
+
+if __name__ == "__main__":
+    images_dir = sys.argv[1]
+
+    if not os.path.isdir(images_dir):
+        print("## Directory provided {} doesn't exist.".format(images_dir))
+        exit()
+
+    # 1 - Load the model and its pretrained weights
+    classifier = Meso4()
+    classifier.load('weights/Meso4_DF')
+
+    # Getting files
+    files = glob.glob(os.path.join(images_dir, "*.jpg"))
+    for f in files:
+        im = load_img(f, target_size=REQUIRED_SIZE)
+        im_arr = np.expand_dims(img_to_array(im), axis=0)
+        im_arr /= 255.0
+        print(im_arr.shape)
+        pred = classifier.predict(im_arr)
+        print("## Image {} is classified as {}".format(f, pred))