mirror of
https://github.com/serengil/deepface.git
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74 lines
2.3 KiB
Python
74 lines
2.3 KiB
Python
from flask import request
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import base64
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from PIL import Image
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import io
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import datetime
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from extension.utilServices import send_json_response
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from flask_restplus import Namespace, Resource
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api = Namespace('predict', path='/predict', description='prediction related operations')
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@api.route('')
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class Prediction(Resource):
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@api.doc('make prediction')
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def post(self):
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"""
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return prediction results and save it to the database
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:return: prediction results
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"""
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from models.prediction import Prediction
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from config import deepface
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message = request.get_json(force=True)
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encoded = message['image']
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decoded = base64.b64decode(encoded)
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image = Image.open(io.BytesIO(decoded)).convert('RGB')
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img, detections = deepface.analyze(image)
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# TODO: handle outputs
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# encode image and jsonify detections
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buffered = io.BytesIO()
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img.save(buffered, format="JPEG")
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img_str = base64.b64encode(buffered.getvalue())
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base64_string = img_str.decode('utf-8')
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result = {
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'img_str': base64_string,
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'results': detections,
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'message': '',
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'status': 'success'
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}
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if len(detections) == 0:
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result['message'] = "We’re not very sure of what this may be, could you try with another image",
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result['status'] = 'failure'
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elif len(detections) == 1:
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result['isShowId'] = 'false'
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if len(detections) > 0:
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formatted_prediction_results = []
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for each in detections:
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age = each['age']
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gender = each['gender']
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emotion = each['emotion']['dominant']
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emotion_score = each['emotion']['dominant_score']
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formatted_prediction_results.append({
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'age': age,
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'gender': gender,
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'emotion': emotion,
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'emotionScore': emotion_score
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})
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# store to db?
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# new_prediction = Prediction(**{
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# 'predictionResults': formatted_prediction_results,
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# 'rawPredictionResults': detections,
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# 'date': datetime.datetime.now(),
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# })
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# new_prediction.save()
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return send_json_response(result, 200)
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