Emotion reco

emotion/README.md

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-#Facial Emotion Sensing
+# Facial Expression Recognition
+

emotion/deepface2.py

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+
+from deepface import DeepFace
+#model = load_model('model.h5')
+#demography = DeepFace.analyze("h.jpg") #passing nothing as 2nd argument will find everything
+demography = DeepFace.analyze("h.jpg", ['age', 'gender',  'emotion']) #identical to the line above
+#demographies = DeepFace.analyze(["img1.jpg", "img2.jpg", "img3.jpg"]) #analyzing multiple faces same time
+print('\n', "-------------------")
+print("Age: ", demography["age"])
+print("Gender: ", demography["gender"])
+print("Emotion: ", demography["dominant_emotion"])
+#print("Race: ", demography["dominant_race"])

emotion/lite.py

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+import cv2
+import numpy as np
+import tensorflow as tf
+
+
+#from translator import translate_eg_ar
+
+
+def init():
+    global labels, Arabic_labels
+    global interpreter
+
+    #labels = ['Angry', 'Disgusted', 'Fear', 'Happy', 'Neutral', 'Sad', 'Surprised']
+    #Arabic_labels = translate_eg_ar(labels)
+    Arabic_labels = ['غاضب', 'مشمئز', 'خائف', 'سعيد', 'طبيعى', 'حزين', 'متفاجىء']
+    interpreter = tf.lite.Interpreter(model_path="model2.tflite")
+    interpreter.allocate_tensors()
+
+
+
+
+def detect(image):
+
+    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 = image
+    img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+    # print(img.shape)
+    img = np.asarray(img)
+    img = cv2.resize(img, (width, height))
+    img = np.expand_dims(img, axis=-1)
+    # print(img.shape)
+    img = img.astype('float')
+    img /= 255.0
+
+    input_data = np.expand_dims(img, axis=0)
+    # print(input_data.shape)
+
+    if floating_model:
+        #input_data = (np.float32(input_data) - 127.5) / 127.5
+        input_data = (np.float32(input_data))
+
+
+    interpreter.set_tensor(input_details[0]['index'], input_data)
+
+    interpreter.invoke()
+
+    output_data = interpreter.get_tensor(output_details[0]['index'])
+    # print(output_data)
+    results = np.squeeze(output_data)
+    # print(results)
+
+    top_k = results.argsort()[-2:][::-1]
+    # print(Arabic_labels[top_k[0]])
+
+    for i in top_k:
+
+
+        if floating_model:
+            return float(results[i]), Arabic_labels[i]
+
+        else:
+            return float(results[i] / 255.0), Arabic_labels[i]
+        # exp = Arabic_labels[i]
+        #return exp
+
+
+
+
+if __name__ == '__main__':
+    init()
+    img = cv2.imread('1happy.jpg')
+    emotion = detect(img)
+    print(emotion)