Emotion reco

emotion/README.md

@@ -1 +1,2 @@
-#Facial Emotion Sensing
+# Facial Expression Recognition
+

emotion/lite.py

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+
+
+
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+import numpy as np
+import tensorflow as tf
+
+import argparse
+
+import numpy as np
+
+
+import cv2
+
+import tensorflow as tf # TF2
+
+
+def load_labels(filename):
+  with open(filename, 'r') as f:
+    return [line.strip() for line in f.readlines()]
+
+
+if __name__ == '__main__':
+
+  interpreter = tf.lite.Interpreter(model_path="model.tflite")
+  interpreter.allocate_tensors()
+
+  input_details = interpreter.get_input_details()
+  output_details = interpreter.get_output_details()
+
+  # check the type of the input tensor
+  floating_model = input_details[0]['dtype'] == np.float32
+
+
+  height = 48
+  width = 48
+  img = cv2.imread('1happy.jpg')
+  img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+  print("image shape ", img.shape)
+  img = np.asarray(img)
+  img=cv2.resize(img,(48,48))
+  img = np.expand_dims(img, axis=-1)
+  print("image shape after resize", img.shape)
+  img= img.astype('float32')
+  img /= 255.0
+
+
+  input_data = np.expand_dims(img, axis=0)
+  print("input data shape", input_data.shape)
+
+  if floating_model:
+    input_data = (np.float32(input_data) - 127.5) / 127.5
+
+  interpreter.set_tensor(input_details[0]['index'], input_data)
+
+  interpreter.invoke()
+
+  output_data = interpreter.get_tensor(output_details[0]['index'])
+  results = np.squeeze(output_data)
+  #print("result",results)
+  top_k = results.argsort()[-2:][::-1]
+  labels = load_labels("labels.txt")
+  print("top_k",top_k)
+
+
+  for i in top_k:
+    if floating_model:
+      print('{:08.6f}: {}'.format(float(results[i]), labels[i]))
+    else:
+      print('{:08.6f}: {}'.format(float(results[i] / 255.0), labels[i]))