Update app.py

app.py

-from flask import Flask, render_template, Response
+import os
+import glob
 import cv2
 import numpy as np
-from tensorflow.keras.models import model_from_json  
-from tensorflow.keras.preprocessing import image  
-  
+from cv2 import CAP_ANY, CAP_DSHOW
+from flask import Flask, Response, redirect, render_template, request, url_for
+from tensorflow.keras.models import model_from_json
+from tensorflow.keras.preprocessing import image
+from werkzeug.utils import secure_filename
 
 #load model  
 model = model_from_json(open("model.json", "r").read())  
@@ -14,12 +17,45 @@ model.load_weights('model.h5')
 
 face_haar_cascade = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')  
 
+UPLOAD_FOLDER = 'video'
+ALLOWED_EXTENSIONS = {'mp4'}
+
 
 app = Flask(__name__)
 
-camera = cv2.VideoCapture(0)
+app.config['UPLOAD_FOLDER'] = UPLOAD_FOLDER
+def allowed_file(filename):
+    return '.' in filename and \
+           filename.rsplit('.', 1)[1].lower() in ALLOWED_EXTENSIONS
+
+@app.route('/gen_frames',methods=["POST"])
+def gen_frames(): 
+
+    file = request.files['file'] # Gives Content type text/html etc
+    if file and allowed_file(file.filename):
+            filename = secure_filename(file.filename)
+            file.save(os.path.join(app.config['UPLOAD_FOLDER'], filename))
+            # return 'success'
+
+    list_of_files = glob.glob('video/*') # * means all if need specific format then *.csv
+    latest_file = max(list_of_files, key=os.path.getctime)
+    print(latest_file)
+
+    camera = cv2.VideoCapture(latest_file)
+
+
+    emo = []
+
+
+# def gen_frames():  # generate frame by frame from camera
+    Angry=0
+    Disgust=0
+    Fear=0
+    Happy=0
+    Neutral=0
+    Sad=0
+    Surprise=0
 
-def gen_frames():  # generate frame by frame from camera
     while True:
         # Capture frame by frame
         success, frame = camera.read()
@@ -37,7 +73,8 @@ def gen_frames():  # generate frame by frame from camera
                 roi_gray=gray_img[y:y+w,x:x+h]          #cropping region of interest i.e. face area from  image  
                 roi_gray=cv2.resize(roi_gray,(48,48))  
                 img_pixels = image.img_to_array(roi_gray)  
-                img_pixels = np.expand_dims(img_pixels, axis = 0)  
+                img_pixels = np.concatenate((img_pixels,)*3, axis = -1)
+                img_pixels = np.expand_dims(img_pixels, axis = 0) 
                 img_pixels /= 255  
         
                 print(img_pixels.shape)
@@ -46,32 +83,76 @@ def gen_frames():  # generate frame by frame from camera
         
                 #find max indexed array  
                 
-                max_index = np.argmax(predictions[0])  
+                max_index = np.argmax(predictions[0])
+                mx=max(max(predictions))
+  
         
                 emotions = ['angry', 'disgust', 'fear', 'happy', 'sad', 'surprise', 'neutral']  
-                predicted_emotion = emotions[max_index]  
+                predicted_emotion = emotions[max_index] 
+                
                 print(predicted_emotion)
+
+                        
+                if(predicted_emotion == 'neutral' ):
+                    Neutral = Neutral+mx
+                    emo.append((predicted_emotion,Neutral))
+                elif(predicted_emotion == 'angry' ):
+                    Angry = Angry+mx
+                    emo.append((predicted_emotion,Angry))
+                elif(predicted_emotion == 'disgusted' ):
+                    Disgust = Disgust+mx
+                    emo.append((predicted_emotion,Disgust))
+                elif(predicted_emotion == 'fearful' ):
+                    Fear = Fear+mx
+                    emo.append((predicted_emotion,Fear))
+                elif(predicted_emotion == 'happy' ):
+                    Happy = Happy+mx
+                    emo.append((predicted_emotion,Happy))
+                elif(predicted_emotion == 'sad' ):
+                    Sad = Sad+mx
+                    emo.append((predicted_emotion,Sad))
+
+                elif(predicted_emotion == 'surprised' ):
+                    Surprise = Surprise+mx
+                    emo.append((predicted_emotion,Surprise))
+
+                        
+
                 cv2.putText(frame, predicted_emotion, (int(x), int(y)), cv2.FONT_HERSHEY_SIMPLEX, 1, (0,0,255), 2)  
         
-            resized_img = cv2.resize(frame, (1000, 700))  
+            # resized_img = cv2.resize(frame, (1000, 700))  
             
-            ret, buffer = cv2.imencode('.jpg', frame)
+            # ret, buffer = cv2.imencode('.jpg', frame)
             
-            frame = buffer.tobytes()
-            yield (b'--frame\r\n'
-                   b'Content-Type: image/jpeg\r\n\r\n' + frame + b'\r\n')  # concat frame one by one and show result
+            # frame = buffer.tobytes()
+            # yield (b'--frame\r\n'
+            #        b'Content-Type: image/jpeg\r\n\r\n' + frame + b'\r\n')  # concat frame one by one and show result
+
+    d = dict(emo)
+    return d
+
+
+    
+
+
+# @app.route('/video_feed')
+# def video_feed():
+#     #Video streaming route. Put this in the src attribute of an img tag
+#     return Response(gen_frames(), mimetype='multipart/x-mixed-replace; boundary=frame')
 
 
-@app.route('/video_feed')
-def video_feed():
-    #Video streaming route. Put this in the src attribute of an img tag
-    return Response(gen_frames(), mimetype='multipart/x-mixed-replace; boundary=frame')
+# @app.route('/')
+# def index():
+#     return render_template('index.html')
 
+# # @app.route('/gen_frames',methods=["POST"])
 
-@app.route('/')
-def index():
-    return render_template('index.html')
 
+# @app.route('/emo')
+# def emotions():
+#     d = dict(emo)
+#     print(d)
+#     return d
 
 if __name__ == '__main__':
-    app.run(debug=True)
+    app.run(host="0.0.0.0", port=5000, debug=True)