PhyzmoCV/backend_testing.py at master · MDB-Inertia/PhyzmoCV · GitHub

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import json
import matplotlib.pyplot as plt
class DataUtils:

    def  __init__(self, jsonString, ref_list): #ref_list contains info for SCALING (coordinate1, coordinate2, physical distance betweent the two corrdinates)
        self.time = []

        self.ref_constant = self.getScaleConstant(ref_list[0], ref_list[1], ref_list[2])

        self.coordinates = []
        self.total_distance = []
        self.distance_per_frame = []
        self.displacement = []
        self.velocity = []
        self.acceleration = []

        self.jsonData = self.getJSON(jsonString) #jsonData shoild be an array of dictionarys

        self.normalized_velocity = []
        self.normalized_acce = []

        self.findDataPoints()
        self.findDistance()
        self.findVelocity()
        self.findAcceleration()
        self.findMovingAverageVelocity()
        #self.findMovingAverageAcce()


    def getJSON(self, jsonString):
        dict = json.loads(json.dumps(jsonString))
        return dict

    def findDataPoints(self):
        for frame in self.jsonData.get("frames"):
            self.time.append(frame.get("time"))
            x = (frame.get("right") + frame.get("left"))/2
            y = (frame.get("bottom") + frame.get("top"))/2
            self.coordinates.append([x, y])

    def getScaleConstant(self, ref_one, ref_two, ref_dist):
        return ref_dist / self.getCoordinateScale(ref_one, ref_two)

    def getCoordinateScale(self, coordinate_one,coordinate_two):
        return pow(pow((coordinate_one[0] - coordinate_two[0]), 2) + pow((coordinate_one[1] - coordinate_two[1]), 2), 0.5)

    def findDistance(self): #WARNING: run findDistance 1 time only
        for i in range(len(self.coordinates)):
            if i == 0:
                self.distance_per_frame.append(0)
                self.total_distance.append(0)
            else:
                #EDITED HERE
                frame_distance = self.ref_constant * self.getCoordinateScale(self.coordinates[i - 1], self.coordinates[i])
                #distance_per_frame.append(frame_distance)
                # if self.coordinates[i][1] < self.coordinates[i-1][1]:
                #     print("true")
                #     frame_distance = -1 * self.ref_constant * self.getCoordinateScale(self.coordinates[i - 1], self.coordinates[i])
                # else:
                #frame_distance = self.ref_constant * self.getCoordinateScale(self.coordinates[i - 1], self.coordinates[i])


                self.distance_per_frame.append(frame_distance)
                self.total_distance.append(frame_distance + self.total_distance[i - 1])

    def findVelocity(self):
        for i in range(len(self.distance_per_frame)):
            if i == 0:
                self.velocity.append(0) #Velocity might not be zero at the start
            else:
                self.velocity.append(self.distance_per_frame[i] / (self.time[i] - self.time[i - 1]))

    def findAcceleration(self):
        for i in range(len(self.distance_per_frame)):
            if i == 0:
                self.acceleration.append(0) #Velocity might not be zero at the start
            else:
                self.acceleration.append((self.velocity[i] - self.velocity[i - 1]) / pow((self.time[i] - self.time[i - 1]), 2))

    def findMovingAverageVelocity(self):
        x = 100
        # for i in range(x + 1):
        #     athena = sum(self.vel)
        #     self.normalized_velocity.append(self.velocity[i])
        # for i in range(x + 1, len(self.velocity) - x):
        #     anjali = sum([self.velocity[val] for val in range(i-x, i+x+1)]) / (2 * x + 1)
        #     print(anjali)
        #     self.normalized_velocity.append(anjali)
        # for i in range(len(self.velocity) - x, len(self.velocity)):
        #     self.normalized_velocity.append(self.velocity[i])
        self.normalized_velocity.append(self.velocity[0])
        for i in range(1 , len(self.velocity)):
            front = min(x, i)
            back = min(x, len(self.velocity) - (i + 1))
            anjali = sum([self.velocity[val] for val in range(i - front, i + back + 1)]) / (front + back + 1)
            self.normalized_velocity.append(anjali)

        def findMovingAverageAcce(self):
            x = 10
            # for i in range(x + 1):
            #     athena = sum(self.vel)
            #     self.normalized_velocity.append(self.velocity[i])
            # for i in range(x + 1, len(self.velocity) - x):
            #     anjali = sum([self.velocity[val] for val in range(i-x, i+x+1)]) / (2 * x + 1)
            #     print(anjali)
            #     self.normalized_velocity.append(anjali)
            # for i in range(len(self.velocity) - x, len(self.velocity)):
            #     self.normalized_velocity.append(self.velocity[i])
            self.normalized_acce.append(self.acceleration[0])
            for i in range(1 , len(self.acceleration)):
                front = min(x, i)
                back = min(x, len(self.acceleration) - (i + 1))
                anjali = sum([self.acceleration[val] for val in range(i - front, i + back + 1)]) / (front + back + 1)
                self.normalized_acce.append(anjali)


json_string = {"frames": [{"time": 0.10016, "left": 0.07517604529857635, "top": 0.24851511418819427, "right": 0.16755200922489166, "bottom": 0.3813386559486389}, {"time": 0.20032, "left": 0.07500027120113373, "top": 0.2486109733581543, "right": 0.16718807816505432, "bottom": 0.3819435238838196}, {"time": 0.30048, "left": 0.0749996080994606, "top": 0.2512838542461395, "right": 0.16718712449073792, "bottom": 0.3846171498298645}, {"time": 0.400641, "left": 0.07564438134431839, "top": 0.24838101863861084, "right": 0.1678318828344345, "bottom": 0.3817143440246582}, {"time": 0.500801, "left": 0.07957633584737778, "top": 0.24879993498325348, "right": 0.16594302654266357, "bottom": 0.38020312786102295}, {"time": 0.600961, "left": 0.07934197038412094, "top": 0.24718686938285828, "right": 0.16689977049827576, "bottom": 0.38113927841186523}, {"time": 0.701121, "left": 0.0825645923614502, "top": 0.2530876696109772, "right": 0.16693183779716492, "bottom": 0.37887251377105713}, {"time": 0.801282, "left": 0.08310423791408539, "top": 0.25295761227607727, "right": 0.16747860610485077, "bottom": 0.3792969584465027}, {"time": 0.901442, "left": 0.0831555426120758, "top": 0.2503523826599121, "right": 0.16822518408298492, "bottom": 0.38157278299331665}, {"time": 2.203525, "left": 0.08544894307851791, "top": 0.2520214915275574, "right": 0.1682526171207428, "bottom": 0.38059985637664795}, {"time": 2.3036849999999998, "left": 0.08593674749135971, "top": 0.2500031292438507, "right": 0.16874924302101135, "bottom": 0.37916886806488037}, {"time": 2.403846, "left": 0.08395379781723022, "top": 0.24867364764213562, "right": 0.17220768332481384, "bottom": 0.38131821155548096}, {"time": 2.504006, "left": 0.08620204031467438, "top": 0.251728892326355, "right": 0.16894489526748657, "bottom": 0.3795938789844513}, {"time": 2.604166, "left": 0.08702925592660904, "top": 0.25088971853256226, "right": 0.1702955812215805, "bottom": 0.37938934564590454}, {"time": 2.704326, "left": 0.08729182928800583, "top": 0.25288698077201843, "right": 0.17080959677696228, "bottom": 0.3794786334037781}, {"time": 2.804487, "left": 0.08598368614912033, "top": 0.2515745162963867, "right": 0.17044925689697266, "bottom": 0.38042500615119934}, {"time": 2.9046469999999998, "left": 0.08625893294811249, "top": 0.2531938850879669, "right": 0.16991037130355835, "bottom": 0.3795040249824524}, {"time": 3.004807, "left": 0.0863458439707756, "top": 0.25270000100135803, "right": 0.17051082849502563, "bottom": 0.38125666975975037}, {"time": 3.104967, "left": 0.08684378862380981, "top": 0.2526077330112457, "right": 0.171042799949646, "bottom": 0.38157975673675537}, {"time": 3.205128, "left": 0.08690735697746277, "top": 0.2515999972820282, "right": 0.17160633206367493, "bottom": 0.38145554065704346}, {"time": 3.305288, "left": 0.08741581439971924, "top": 0.2515943646430969, "right": 0.17181801795959473, "bottom": 0.3808189034461975}, {"time": 3.405448, "left": 0.08717922866344452, "top": 0.2521844804286957, "right": 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0.5060602426528931}, {"time": 34.154647, "left": 0.8675608038902283, "top": 0.3654189705848694, "right": 0.9593148231506348, "bottom": 0.5093873143196106}, {"time": 34.254807, "left": 0.8733093738555908, "top": 0.36696991324424744, "right": 0.9629847407341003, "bottom": 0.508144736289978}, {"time": 34.354967, "left": 0.8752660751342773, "top": 0.3668353259563446, "right": 0.9692518711090088, "bottom": 0.5083928108215332}, {"time": 34.455128, "left": 0.8807825446128845, "top": 0.368768572807312, "right": 0.9736337065696716, "bottom": 0.5077642798423767}, {"time": 34.555288, "left": 0.8859118819236755, "top": 0.37048429250717163, "right": 0.9789795875549316, "bottom": 0.5072551965713501}, {"time": 34.655448, "left": 0.8898364901542664, "top": 0.3702886700630188, "right": 0.9828134775161743, "bottom": 0.5064548254013062}, {"time": 34.755608, "left": 0.896341860294342, "top": 0.3695167899131775, "right": 0.9866579174995422, "bottom": 0.5108875632286072}, {"time": 34.855769, "left": 0.8983348608016968, "top": 0.3685346841812134, "right": 0.9930412769317627, "bottom": 0.5135278701782227}, {"time": 34.955929, "left": 0.9035754799842834, "top": 0.37026163935661316, "right": 0.9954034686088562, "bottom": 0.5145523548126221}, {"time": 35.056089, "left": 0.9060627222061157, "top": 0.3707977831363678, "right": 0.9988971948623657, "bottom": 0.5159861445426941}, {"time": 35.15625, "left": 0.9138146042823792, "top": 0.3729322850704193, "right": 0.998863160610199, "bottom": 0.5138548612594604}, {"time": 35.25641, "left": 0.9162196516990662, "top": 0.37130263447761536, "right": 1.0005296468734741, "bottom": 0.5151534080505371}, {"time": 35.35657, "left": 0.9187964797019958, "top": 0.37022924423217773, "right": 1.0013010501861572, "bottom": 0.516648530960083}, {"time": 35.45673, "left": 0.9251407384872437, "top": 0.3680594265460968, "right": 1.0001697540283203, "bottom": 0.5203942656517029}, {"time": 35.556891, "left": 0.9285692572593689, "top": 0.36935460567474365, "right": 1.000684142112732, "bottom": 0.522756814956665}]}

ref_list = [[0.121, 0.215], [0.9645, 0.446], 0.60]

test = DataUtils(json_string, ref_list)

#print(len(test.time))
print('time', len(test.time), "velo", len(test.normalized_velocity))
plt.plot(test.time, test.normalized_velocity)

#print(test.velocity)
plt.show()