Updated ML

ML/API_tester.py

@@ -0,0 +1,21 @@
+import cv2
+from pred_lib import Predictor
+
+predict = Predictor()
+
+vid = cv2.VideoCapture(0)
+
+while True:
+    ret,frame = vid.read()
+    if(not(ret)):
+        break
+    new_frame = predict.get_pred(frame)
+    if(type(new_frame)==int):
+        continue
+    cv2.imshow("Original",frame)
+    cv2.imshow("New",new_frame)
+    if(cv2.waitKey(1)==27):
+        break
+cv2.destroyAllWindows()
+vid.release()
+

ML/Flask_API.py

@@ -0,0 +1,60 @@
+from flask import Flask, jsonify, request
+from flask_restful import reqparse, Api, Resource
+import os
+import subprocess
+import cv2
+import numpy as np
+import time
+
+app = Flask(__name__)
+api = Api(app)
+
+parser = reqparse.RequestParser()
+parser.add_argument("image",type=str)
+parser.add_argument("count",type=int)
+
+output_dir = "outputs/"
+
+class Main_Page(Resource):
+    def __init__(self):
+        pass
+
+    def get(self):
+        return {"Hello":"World"}
+
+    def post(self):
+        json_data = request.get_json(force = True)
+        username = json_data["user_name"]
+        os.mkdir(username)
+        os.mkdir(output_dir+username)
+        p = subprocess.run(["python","Test_1.py",username])
+        return {"Happy":"Noises"}
+
+class User_Page(Resource):
+    def __init__(self):
+        pass
+
+    def get(self,name):
+        items = os.listdir(output_dir+name)
+        while(len(items)==0):
+           time.sleep(0.5)
+           items = os.listdir(output_dir+name)
+        img_name = items[-1]
+        img = cv2.imread(output_dir+name+"/"+img_name)
+        _, img_encoded = cv2.imencode('.jpg', img)
+        return jsonify(image = img_encoded.tolist())
+
+    def post(self,name):
+        args = request.get_json(force = True)
+        image = args["image"]
+        count = args["count"]
+        np_img = np.array(image,dtype=np.uint8)
+        img_decoded = cv2.imdecode(np_img,cv2.IMREAD_COLOR)
+        cv2.imwrite(name+"/"+str(count)+".jpg",img_decoded)
+        return {"Happy":"Noises"}
+
+api.add_resource(Main_Page,"/")
+api.add_resource(User_Page,"/name/<string:name>")
+
+if __name__=="__main__":
+    app.run(debug=True)

ML/ReadMe

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+Prototype completed, only pred_lib needed at client, rest are server applications.
+Also, email/facebook me, phone screen broke cannot communicate through that.