Recognizing Emotion using DeepFace
Use deepface to analyze the emotion in an image. Pass the image stored as an array to Deepface’s analyze function. It will return a Python dictionary with the percentage of all emotions.
Python3
# storing the dictionary of emotions in resultresult = DeepFace.analyze(img, actions = ['emotion'])# print resultprint(result) |
Output:
[{'emotion': {'angry': 2.9941825391265356e-05,
'disgust': 3.6047339119136895e-10,
'fear': 0.00011003920865101386,
'happy': 97.65191646241146,
'sad': 0.0015582609232700413,
'surprise': 0.0032574247843123716,
'neutral': 2.343132812456228},
'dominant_emotion': 'happy',
'region': {'x': 325, 'y': 64, 'w': 128, 'h': 128}}]
Extract the emotion with the highest percentage.
Python3
# extracting emotion with highest percentagequery = str(max(zip(result[0]['emotion'].values(), result[0]['emotion'].keys()))[1])print(query) |
Output:
happy
Emotion Based Music Player – Python Project
In this article, we will be discussing how can we recommend music based on expressions or say dominant expressions on someone’s face. This is a basic project in which we will be using OpenCV, Matplotlib, DeepFace, and Spotify API.