Emotion detection python project

E/EmotionML/Dockerfile

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+FROM python:3.6
+WORKDIR /app
+COPY . /app
+RUN pip wheel opencv-python
+RUN pip install opencv-python
+RUN pip install libgl1
+RUN pip install -r requirements.txt
+EXPOSE 8080
+CMD python ./app.py

E/EmotionML/README.md

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+Emotion detection app

E/EmotionML/app.py

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+from flask import Flask, render_template,Response
+from camera import Video
+
+app = Flask(__name__)
+
+@app.route('/')
+def index():
+    return render_template('index.html')
+
+def gen(camera):
+    while True:
+        frame=camera.get_frame()
+        yield(b'--frame\r\n'
+            b'Content-Type: image/jpeg\r\n\r\n' + frame +
+                b'\r\n\r\n')
+
+@app.route('/video')
+def video():
+    return Response(gen(Video()),
+    mimetype='multipart/x-mixed-replace; boundary=frame')
+
+if __name__=="__main__":
+    from waitress import serve
+    serve(app, host="0.0.0.0", port=8080)
+
+
+# import os
+# os.environ["CUDA_VISIBLE_DEVICES"]= "-1"
+# import cv2
+# from deepface import DeepFace
+
+# face_classifier = cv2.CascadeClassifier()
+# face_classifier.load(cv2.samples.findFile("haarcascade_frontalface_default.xml"))
+
+# cap = cv2.VideoCapture(0)
+
+# while True:
+
+#     ret, frame = cap.read()
+#     frame_gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
+#     faces = face_classifier.detectMultiScale(frame_gray)
+
+#     response = DeepFace.analyze(frame, actions=("emotion",), enforce_detection=False)
+#     for face in faces:
+#         x,y,w,h = face
+
+#         cv2.rectangle(frame, (x+10,y+10), (x+w,y+h), color= (238,130,238), thickness=4)
+#         cv2.putText(frame, response["dominant_emotion"].upper(), org=(x,y), fontFace=cv2.FONT_HERSHEY_SIMPLEX, 
+#             fontScale=1.2, color=(255,204,153),thickness=4)
+#         cv2.imshow("", frame)
+#         if(cv2.waitKey(30)==27):
+#             break
+# cap.release()
+# cv2.destroyAllWindows()

E/EmotionML/camera.py

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+import os
+os.environ["CUDA_VISIBLE_DEVICES"]= "-1"
+import cv2
+from deepface import DeepFace
+
+face_classifier = cv2.CascadeClassifier()
+face_classifier.load(cv2.samples.findFile("haarcascade_frontalface_default.xml"))
+
+class Video(object):
+    def __init__(self):
+        self.video=cv2.VideoCapture(0)
+    def __del__(self):
+        self.video.release()
+    def get_frame(self):
+        ret,frame = self.video.read()
+        frame_gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
+        faces = face_classifier.detectMultiScale(frame_gray)
+
+        response = DeepFace.analyze(frame, actions=("emotion",), enforce_detection=False)
+        for face in faces:
+            x,y,w,h = face
+
+            cv2.rectangle(frame, (x+10,y+10), (x+w,y+h), color= (238,130,238), thickness=4)
+            cv2.putText(frame, response["dominant_emotion"].upper(), org=(x,y), fontFace=cv2.FONT_HERSHEY_SIMPLEX, 
+                fontScale=1.2, color=(255,204,153),thickness=4)
+            cv2.imshow("", frame)
+            if(cv2.waitKey(30)==27):
+                break
+        ret,jpg=cv2.imencode('.jpg',frame)
+        return jpg.tobytes()