add mark period reset

app.py

@@ -3,14 +3,12 @@ import numpy as np
 from PIL import Image
 import os
 import cv2
-import uuid
-import time
+import math
 import spaces
-import subprocess
 import matplotlib
 matplotlib.use('Agg')
 import matplotlib.pyplot as plt
-from scipy.signal import medfilt
+from scipy.signal import medfilt, find_peaks
 from functools import partial
 from passlib.hash import pbkdf2_sha256
 from tqdm import tqdm
@@ -195,13 +193,21 @@ def inference(x, count_only_api, api_key,
         periodLength = medfilt(periodLength, 5)
     periodicity = sigmoid(periodicity)
     full_marks = sigmoid(full_marks)
-    full_marks_mask = np.int32(full_marks > marks_threshold)
+    #full_marks_mask = np.int32(full_marks > marks_threshold)
+    pred_marks_peaks, _ = find_peaks(full_marks, distance=3, height=marks_threshold)
+    full_marks_mask = np.zeros(len(full_marks))
+    full_marks_mask[pred_marks_peaks] = 1
     periodicity_mask = np.int32(periodicity > miss_threshold)
     numofReps = 0
     count = []
     for i in range(len(periodLength)):
         if periodLength[i] < 2 or periodicity_mask[i] == 0:
             numofReps += 0
+        elif full_marks_mask[i]:  # high confidence mark detected
+            if math.modf(numofReps)[0] < 0.2:  # probably false positive/late detection
+                numofReps = float(int(numofReps))
+            else:
+                numofReps = float(int(numofReps) + 1.01)  # round up
         else:
             numofReps += max(0, periodicity_mask[i]/(periodLength[i]))
         count.append(round(float(numofReps), 2))