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other_algorithms.py

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+import numpy as np
+import cv2
+import sys
+from random import randint
+
+TEXT_COLOR = (randint(0, 255), randint(0, 255), randint(0,255))
+#print(TEXT_COLOR)
+BORDER_COLOR = (randint(0, 255), randint(0, 255), randint(0,255))
+#print(BORDER_COLOR)
+FONT = cv2.FONT_HERSHEY_SIMPLEX
+VIDEO_SOURCE = 'videos/Traffic_4.mp4'
+
+BGS_TYPES = ['GMG', 'MOG2', 'MOG', 'KNN', 'CNT']
+#print(BGS_TYPES[1])
+
+def get_kernel(KERNEL_TYPE):
+    if KERNEL_TYPE == 'dilation':
+        kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (3,3))
+    if KERNEL_TYPE == 'opening':
+        kernel = np.ones((3,3), np.uint8)
+    if KERNEL_TYPE == 'closing':
+        kernel = np.ones((3,3), np.uint8)
+    return kernel
+
+#get_kernel('closing')
+
+def get_filter(img, filter):
+    if filter == 'closing':
+        return cv2.morphologyEx(img, cv2.MORPH_CLOSE, get_kernel('closing'), iterations=2)
+    if filter == 'opening':
+        return cv2.morphologyEx(img, cv2.MORPH_OPEN, get_kernel('opening'), iterations = 2)
+    if filter == 'dilation':
+        return cv2.dilate(img, get_kernel('dilation'), iterations = 2)
+    if filter == 'combine':
+        closing = cv2.morphologyEx(img, cv2.MORPH_CLOSE, get_kernel('closing'), iterations = 2)
+        opening = cv2.morphologyEx(closing, cv2.MORPH_OPEN, get_kernel('opening'), iterations = 2)
+        dilation = cv2.dilate(opening, get_kernel('dilation'), iterations=2)
+        return dilation
+
+def get_bgsubtractor(BGS_TYPE):
+    # https://docs.opencv.org/3.4/d1/d5c/classcv_1_1bgsegm_1_1BackgroundSubtractorGMG.html
+    if BGS_TYPE == 'GMG':
+        return cv2.bgsegm.createBackgroundSubtractorGMG(initializationFrames = 120,
+                                                        decisionThreshold = 0.8)
+    # https://docs.opencv.org/3.4/d6/da7/classcv_1_1bgsegm_1_1BackgroundSubtractorMOG.html
+    if BGS_TYPE == 'MOG':
+        return cv2.bgsegm.createBackgroundSubtractorMOG(history = 200, nmixtures = 5,
+                                                       backgroundRatio = 0.7, noiseSigma=0)
+    # https://docs.opencv.org/3.4/d7/d7b/classcv_1_1BackgroundSubtractorMOG2.html
+    if BGS_TYPE == 'MOG2':
+        return cv2.createBackgroundSubtractorMOG2(history = 500, detectShadows = True,
+                                                  varThreshold = 100)
+    # https://docs.opencv.org/3.4/db/d88/classcv_1_1BackgroundSubtractorKNN.html
+    if BGS_TYPE == 'KNN':
+        return cv2.createBackgroundSubtractorKNN(history = 500, dist2Threshold=400,
+                                                 detectShadows = True)
+    # https://docs.opencv.org/3.4/de/dca/classcv_1_1bgsegm_1_1BackgroundSubtractorCNT.html
+    if BGS_TYPE == 'CNT':
+        return cv2.bgsegm.createBackgroundSubtractorCNT(minPixelStability=15,
+                                                        useHistory = True,
+                                                        maxPixelStability = 15*60,
+                                                        isParallel=True)
+    print('Invalid detector!')
+    sys.exit(0)
+
+cap = cv2.VideoCapture(VIDEO_SOURCE)
+# 0 = GMG, 1 = MOG2, 2 = MOG, 3 = KNN,  4 = CNT
+bg_subtractor = get_bgsubtractor(BGS_TYPES[1])
+BGS_TYPE = BGS_TYPES[1]
+
+def main():
+    while cap.isOpened():
+        ok, frame = cap.read()
+        #print(ok)
+        #print(frame.shape)
+
+        frame = cv2.resize(frame, (0,0), fx=0.5, fy=0.5)
+        bg_mask = bg_subtractor.apply(frame)
+
+        fg_mask = get_filter(bg_mask, 'dilation')
+        fg_mask_closing = get_filter(bg_mask, 'closing')
+        fg_mask_opening = get_filter(bg_mask, 'opening')
+        fg_mask_combine = get_filter(bg_mask, 'combine')
+        #print(frame.shape)
+
+        # https://docs.opencv.org/master/d0/d86/tutorial_py_image_arithmetics.html
+        # https://www.pyimagesearch.com/2021/01/19/opencv-bitwise-and-or-xor-and-not/
+        res = cv2.bitwise_and(frame, frame, mask=fg_mask)
+        res_closing = cv2.bitwise_and(frame, frame, mask=fg_mask_closing)
+        res_opening = cv2.bitwise_and(frame, frame, mask=fg_mask_opening)
+        res_combine = cv2.bitwise_and(frame, frame, mask=fg_mask_combine)
+
+        cv2.putText(res_combine, 'Background subtractor: ' + BGS_TYPE, (10,50), FONT, 1, BORDER_COLOR, 3, cv2.LINE_AA)
+        cv2.putText(res_combine, 'Background subtractor: ' + BGS_TYPE, (10, 50), FONT, 1, BORDER_COLOR, 2, cv2.LINE_AA)
+
+        if not ok:
+            print('End processing the video')
+            break
+
+        if BGS_TYPE != 'MOG' and BGS_TYPE != 'GMG':
+            cv2.imshow('Background model', bg_subtractor.getBackgroundImage())
+
+        cv2.imshow('Frame', frame)
+        cv2.imshow('BG mask', bg_mask)
+        #cv2.imshow('Dilation', fg_mask)
+        cv2.imshow('Dilation final', res)
+        cv2.imshow('Closing final', res_closing)
+        cv2.imshow('Opening final', res_opening)
+        cv2.imshow('Combine final', res_combine)
+
+        if cv2.waitKey(1) & 0xFF == ord('q'):
+            break
+
+main()
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+

performance_comparison.py

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+import cv2
+import sys
+from random import randint
+
+TEXTCOLOR = (randint(0, 255), randint(0, 255), randint(0, 255))
+BORDERCOLOR = (randint(0, 255), randint(0, 255), randint(0, 255))
+FONT = cv2.FONT_HERSHEY_SIMPLEX
+VIDEO_SOURCE = "videos/people.mp4"
+BGS_TYPES = ["GMG", "MOG", "MOG2", "KNN", "CNT"]
+BGS_TYPE = BGS_TYPES[4]
+# GMG 38
+# MOG 28
+# MOG2 18
+# KNN 16
+# CNT 15
+
+def getBGSubtractor(BGS_TYPE):
+    if BGS_TYPE == "GMG":
+        return cv2.bgsegm.createBackgroundSubtractorGMG()
+    if BGS_TYPE == "MOG":
+        return cv2.bgsegm.createBackgroundSubtractorMOG()
+    if BGS_TYPE == "MOG2":
+        return cv2.createBackgroundSubtractorMOG2()
+    if BGS_TYPE == "KNN":
+        return cv2.createBackgroundSubtractorKNN()
+    if BGS_TYPE == "CNT":
+        return cv2.bgsegm.createBackgroundSubtractorCNT()
+    print("Unknown createBackgroundSubtractor type")
+    sys.exit(1)
+
+cap = cv2.VideoCapture(VIDEO_SOURCE)
+bg_subtractor = getBGSubtractor(BGS_TYPE)
+e1 = cv2.getTickCount()
+
+def main():
+    frame_number = -1
+    while (cap.isOpened):
+        ok, frame = cap.read()
+
+        if not ok:
+          print('Finish processing the video')
+          break
+
+        frame_number += 1
+
+        bg_mask = bg_subtractor.apply(frame)
+        res = cv2.bitwise_and(frame, frame, mask=bg_mask)
+
+        cv2.imshow('Frame', frame)
+        cv2.imshow('Mask', res)
+
+        if cv2.waitKey(1) & 0xFF == ord("q") or frame_number > 250:
+            break
+
+    e2 = cv2.getTickCount()
+    t = (e2 - e1) / cv2.getTickFrequency()
+    print(t)
+
+main()
+
+
+
+
+
+
+
+
+
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+
+
+
+
+
+
+
+
+

project1.py

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+import numpy as np
+import cv2
+import sys
+
+TEXT_COLOR = (0, 255, 0)
+TRACKER_COLOR = (255, 0, 0)
+FONT = cv2.FONT_HERSHEY_SIMPLEX
+VIDEO_SOURCE = "videos/Animal_1.mp4"
+
+BGS_TYPES = ["GMG", "MOG", "MOG2", "KNN", "CNT"]
+BGS_TYPE = BGS_TYPES[2]
+
+def getKernel(KERNEL_TYPE):
+    if KERNEL_TYPE == "dilation":
+        kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (3, 3))
+    if KERNEL_TYPE == "opening":
+        kernel = np.ones((3, 3), np.uint8)
+    if KERNEL_TYPE == "closing":
+        kernel = np.ones((3, 3), np.uint8)
+
+    return kernel
+
+def getFilter(img, filter):
+    if filter == 'closing':
+        return cv2.morphologyEx(img, cv2.MORPH_CLOSE, getKernel("closing"), iterations=2)
+
+    if filter == 'opening':
+        return cv2.morphologyEx(img, cv2.MORPH_OPEN, getKernel("opening"), iterations=2)
+
+    if filter == 'dilation':
+        return cv2.dilate(img, getKernel("dilation"), iterations=2)
+
+    if filter == 'combine':
+        closing = cv2.morphologyEx(img, cv2.MORPH_CLOSE, getKernel("closing"), iterations=2)
+        opening = cv2.morphologyEx(closing, cv2.MORPH_OPEN, getKernel("opening"), iterations=2)
+        dilation = cv2.dilate(opening, getKernel("dilation"), iterations=2)
+
+        return dilation
+
+def getBGSubtractor(BGS_TYPE):
+    if BGS_TYPE == "GMG":
+        return cv2.bgsegm.createBackgroundSubtractorGMG()
+    if BGS_TYPE == "MOG":
+        return cv2.bgsegm.createBackgroundSubtractorMOG()
+    if BGS_TYPE == "MOG2":
+        return cv2.createBackgroundSubtractorMOG2()
+    if BGS_TYPE == "KNN":
+        return cv2.createBackgroundSubtractorKNN()
+    if BGS_TYPE == "CNT":
+        return cv2.bgsegm.createBackgroundSubtractorCNT()
+    print("Invalid detector")
+    sys.exit(1)
+
+cap = cv2.VideoCapture(VIDEO_SOURCE)
+bg_subtractor = getBGSubtractor(BGS_TYPE)
+minArea = 250
+
+def main():
+    while (cap.isOpened):
+        ok, frame = cap.read()
+        if not ok:
+            print("Finished processing the video")
+            break
+
+        frame = cv2.resize(frame, (0, 0), fx=0.50, fy=0.50)
+
+        bg_mask = bg_subtractor.apply(frame)
+        bg_mask = getFilter(bg_mask, 'combine')
+        bg_mask = cv2.medianBlur(bg_mask, 5)
+
+        (contours, hierarchy) = cv2.findContours(bg_mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
+        #print(contours)
+        for cnt in contours:
+            area = cv2.contourArea(cnt)
+            if area >= minArea:
+                x, y, w, h = cv2.boundingRect(cnt)
+                cv2.rectangle(frame, (10,30), (250,55), (255,0,0), -1)
+                cv2.putText(frame, 'Motion detected!', (10,50), FONT, 0.8, TEXT_COLOR, 2, cv2.LINE_AA)
+
+                #cv2.drawContours(frame, cnt, -1, TRACKER_COLOR, 3)
+                #cv2.drawContours(frame, cnt, -1, (255,255,255), 1)
+                #cv2.rectangle(frame, (x,y), (x+w, y+h), TRACKER_COLOR, 3)
+                #cv2.rectangle(frame, (x, y), (x + w, y + h), (255,255,255), 1)
+
+                # https://www.pyimagesearch.com/2016/03/07/transparent-overlays-with-opencv/
+                for alpha in np.arange(0.8, 1.1, 0.9)[::-1]:
+                    frame_copy = frame.copy()
+                    output = frame.copy()
+                    cv2.drawContours(frame_copy, [cnt], -1, TRACKER_COLOR, -1)
+                    frame = cv2.addWeighted(frame_copy, alpha, output, 1 - alpha, 0, output)
+
+
+        result = cv2.bitwise_and(frame, frame, mask=bg_mask)
+        cv2.imshow('Frame', frame)
+        cv2.imshow('Mask', result)
+
+        if cv2.waitKey(1) & 0xFF == ord("q"):
+            break
+
+main()
+
+
+
+
+
+
+
+
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+
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+
+
+

project2.py

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+import numpy as np
+import cv2
+import sys
+
+TEXT_COLOR = (24, 201, 255)
+TRACKER_COLOR = (255, 128, 0)
+WARNING_COLOR = (24, 201, 255)
+FONT = cv2.FONT_HERSHEY_SIMPLEX
+VIDEO_SOURCE = "videos/Pedestrians_2.mp4"
+
+BGS_TYPES = ["GMG", "MOG", "MOG2", "KNN", "CNT"]
+BGS_TYPE = BGS_TYPES[0]
+
+def getKernel(KERNEL_TYPE):
+    if KERNEL_TYPE == "dilation":
+        kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (2, 2))
+    if KERNEL_TYPE == "opening":
+        kernel = np.ones((3, 5), np.uint8)
+    if KERNEL_TYPE == "closing":
+        kernel = np.ones((11, 11), np.uint8)
+
+    return kernel
+
+def getFilter(img, filter):
+    if filter == 'closing':
+        return cv2.morphologyEx(img, cv2.MORPH_CLOSE, getKernel("closing"), iterations=2)
+
+    if filter == 'opening':
+        return cv2.morphologyEx(img, cv2.MORPH_OPEN, getKernel("opening"), iterations=2)
+
+    if filter == 'dilation':
+        return cv2.dilate(img, getKernel("dilation"), iterations=2)
+
+    if filter == 'combine':
+        closing = cv2.morphologyEx(img, cv2.MORPH_CLOSE, getKernel("closing"), iterations=2)
+        opening = cv2.morphologyEx(closing, cv2.MORPH_OPEN, getKernel("opening"), iterations=2)
+        dilation = cv2.dilate(opening, getKernel("dilation"), iterations=2)
+
+        return dilation
+
+def getBGSubtractor(BGS_TYPE):
+    if BGS_TYPE == "GMG":
+        return cv2.bgsegm.createBackgroundSubtractorGMG()
+    if BGS_TYPE == "MOG":
+        return cv2.bgsegm.createBackgroundSubtractorMOG()
+    if BGS_TYPE == "MOG2":
+        return cv2.createBackgroundSubtractorMOG2(detectShadows=False, varThreshold=100)
+    if BGS_TYPE == "KNN":
+        return cv2.createBackgroundSubtractorKNN()
+    if BGS_TYPE == "CNT":
+        return cv2.bgsegm.createBackgroundSubtractorCNT()
+    print("Invalid detector")
+    sys.exit(1)
+
+cap = cv2.VideoCapture(VIDEO_SOURCE)
+bg_subtractor = getBGSubtractor(BGS_TYPE)
+minArea = 400
+maxArea = 3000
+
+def main():
+    while (cap.isOpened):
+        ok, frame = cap.read()
+
+        if not ok:
+            print("Finished processing the video")
+            break
+
+        frame = cv2.resize(frame, (0, 0), fx=0.50, fy=0.50)
+        bg_mask = bg_subtractor.apply(frame)
+        bg_mask = getFilter(bg_mask, 'combine')
+        bg_mask = cv2.medianBlur(bg_mask, 5)
+        (contours, hierarchy) = cv2.findContours(bg_mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
+        for cnt in contours:
+            area = cv2.contourArea(cnt)
+            if area >= minArea:
+                x, y, w, h = cv2.boundingRect(cnt)
+                cv2.drawContours(frame, cnt, 1, TRACKER_COLOR, 10)
+                cv2.drawContours(frame, cnt, 1, (255,255,255), 1)
+
+                if area >= maxArea:
+                    cv2.rectangle(frame, (x,y), (x + 120, y - 13), (49,49,49), -1)
+                    cv2.putText(frame, 'Warning', (x, y - 2), FONT, 0.4, (255,255,255), 1, cv2.LINE_AA)
+                    cv2.drawContours(frame, [cnt], -1, WARNING_COLOR, 2)
+                    cv2.drawContours(frame, [cnt], -1, WARNING_COLOR, 1)
+
+        res = cv2.bitwise_and(frame, frame, mask=bg_mask)
+
+        cv2.putText(res, BGS_TYPE, (10,50), FONT, 1, (255,255,255), 3, cv2.LINE_AA)
+        cv2.putText(res, BGS_TYPE, (10, 50), FONT, 1, TEXT_COLOR, 2, cv2.LINE_AA)
+
+        cv2.imshow('Frame', frame)
+        cv2.imshow('Mask', res)
+
+
+        if cv2.waitKey(1) & 0xFF == ord("q"):
+            break
+
+main()
+

project3.py

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+import numpy as np
+import cv2
+import sys
+import time
+import validator
+from random import randint
+
+LINE_IN_COLOR = (64, 255, 0)
+LINE_OUT_COLOR = (0, 0, 255)
+BOUNDING_BOX_COLOR = (255, 128, 0)
+TRACKER_COLOR = (randint(0, 255), randint(0, 255), randint(0, 255))
+CENTROID_COLOR = (randint(0, 255), randint(0, 255), randint(0, 255))
+TEXT_COLOR = (randint(0, 255), randint(0, 255), randint(0, 255))
+TEXT_POSITION_BGS = (10, 50)
+TEXT_POSITION_COUNT_CARS = (10, 100)
+TEXT_POSITION_COUNT_TRUCKS = (10, 150)
+TEXT_SIZE = 1.2
+FONT = cv2.FONT_HERSHEY_SIMPLEX
+SAVE_IMAGE = True
+IMAGE_DIR = "./vehicles"
+VIDEO_SOURCE = "videos/Traffic_3.mp4"
+VIDEO_OUT = "videos/results/result_traffic.avi"
+
+BGS_TYPES = ["GMG", "MOG", "MOG2", "KNN", "CNT"]
+BGS_TYPE = BGS_TYPES[2]
+
+def getKernel(KERNEL_TYPE):
+    if KERNEL_TYPE == "dilation":
+        kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (2, 2))
+    if KERNEL_TYPE == "opening":
+        kernel = np.ones((3, 3), np.uint8)
+    if KERNEL_TYPE == "closing":
+        kernel = np.ones((11, 11), np.uint8)
+
+    return kernel
+
+def getFilter(img, filter):
+    if filter == 'closing':
+        return cv2.morphologyEx(img, cv2.MORPH_CLOSE, getKernel("closing"), iterations=2)
+
+    if filter == 'opening':
+        return cv2.morphologyEx(img, cv2.MORPH_OPEN, getKernel("opening"), iterations=2)
+
+    if filter == 'dilation':
+        return cv2.dilate(img, getKernel("dilation"), iterations=2)
+
+    if filter == 'combine':
+        closing = cv2.morphologyEx(img, cv2.MORPH_CLOSE, getKernel("closing"), iterations=2)
+        opening = cv2.morphologyEx(closing, cv2.MORPH_OPEN, getKernel("opening"), iterations=2)
+        dilation = cv2.dilate(opening, getKernel("dilation"), iterations=2)
+
+        return dilation
+
+def getBGSubtractor(BGS_TYPE):
+    if BGS_TYPE == "GMG":
+        return cv2.bgsegm.createBackgroundSubtractorGMG(initializationFrames=120, decisionThreshold=.8)
+    if BGS_TYPE == "MOG":
+        return cv2.bgsegm.createBackgroundSubtractorMOG(history=200, nmixtures=5, backgroundRatio=.7, noiseSigma=0)
+    if BGS_TYPE == "MOG2":
+        return cv2.createBackgroundSubtractorMOG2(history=50, detectShadows=False, varThreshold=200)
+    if BGS_TYPE == "KNN":
+        return cv2.createBackgroundSubtractorKNN(history=100, dist2Threshold=400, detectShadows=True)
+    if BGS_TYPE == "CNT":
+        return cv2.bgsegm.createBackgroundSubtractorCNT(minPixelStability=15, useHistory=True,
+                                                        maxPixelStability=15 * 60, isParallel=True)
+    print("Invalid detector")
+    sys.exit(1)
+
+def getCentroid(x, y, w, h):
+    x1 = int(w / 2)
+    y1 = int(h / 2)
+    cx = x + x1
+    cy = y + y1
+    return (cx, cy)
+
+#print(getCentroid(50, 100, 100, 100))
+
+def save_frame(frame, file_name, flip=True):
+    if flip: # BGR -> RGB
+        cv2.imwrite(file_name, np.flip(frame, 2))
+    else:
+        cv2.imwrite(file_name, frame)
+
+cap = cv2.VideoCapture(VIDEO_SOURCE)
+hasFrame, frame = cap.read()
+
+fourcc = cv2.VideoWriter_fourcc(*'XVID')
+writer_video = cv2.VideoWriter(VIDEO_OUT, fourcc, 25, (frame.shape[1], frame.shape[0]), True)
+
+# ROI
+bbox = cv2.selectROI(frame, False)
+#print(bbox)
+(w1, h1, w2, h2) = bbox
+#print(w1, h1, w2, h2)
+
+frameArea = h2 * w2
+#print(frameArea)
+minArea = int(frameArea / 250)
+#print(minArea)
+maxArea = 15000
+
+line_IN = int(h1)
+line_OUT = int(h2 - 20)
+#print(line_IN, line_OUT)
+
+DOWN_limit = int(h1 / 4)
+print('Down IN limit Y', str(DOWN_limit))
+print('Down OUT limit Y', str(line_OUT))
+
+bg_subtractor = getBGSubtractor(BGS_TYPE)
+
+def main():
+    frame_number = -1
+    cnt_cars, cnt_trucks = 0, 0
+    objects = []
+    max_p_age = 2
+    pid = 1
+    while (cap.isOpened()):
+        ok, frame = cap.read()
+        if not ok:
+            print("Finished processing the video")
+            break
+
+        roi = frame[h1:h1 + h2, w1:w1 + w2]
+
+        for i in objects:
+            #print('test')
+            i.age_one()
+
+        frame_number += 1
+        bg_mask = bg_subtractor.apply(roi)
+        bg_mask = getFilter(bg_mask,'combine')
+        (contours, hierarchy) = cv2.findContours(bg_mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
+        for cnt in contours:
+            area = cv2.contourArea(cnt)
+
+            # Counting the cars
+            if area > minArea and area <= maxArea:
+                x,y,w,h = cv2.boundingRect(cnt)
+                centroid = getCentroid(x, y, w, h)
+                cx = centroid[0]
+                cy = centroid[1]
+                new = True
+                cv2.rectangle(roi, (x,y), (x + 50, y - 13), TRACKER_COLOR, -1)
+                cv2.putText(roi, 'CAR', (x, y-2), FONT, 0.5, (255,255,255), 1, cv2.LINE_AA)
+                for i in objects:
+                    if abs(x - i.getX()) <= w and abs(y - i.getY()) <= h:
+                        new = False
+                        i.updateCoords(cx, cy)
+
+                        if i.going_DOWN(DOWN_limit) == True:
+                            cnt_cars += 1
+                            if SAVE_IMAGE:
+                                save_frame(roi, IMAGE_DIR + '/car_DOWN_%04d.png' % frame_number)
+                                print('ID:', i.getId(), ' passed by the road in', time.strftime('%c'))
+                        break
+                    if i.getState() == '1':
+                        if i.getDir() == 'down' and i.getY() > line_OUT:
+                            i.setDone()
+                    if i.timedOut():
+                        index = objects.index(i)
+                        objects.pop(index)
+                        del i
+                if new == True:
+                    p = validator.MyValidator(pid, cx, cy, max_p_age)
+                    objects.append(p)
+                    pid += 1
+                cv2.circle(roi, (cx, cy), 5, CENTROID_COLOR, -1)
+
+            # Counting the trucks
+            elif area >= maxArea:
+                x, y, w, h = cv2.boundingRect(cnt)
+                centroid = getCentroid(x, y, w, h)
+                cx = centroid[0]
+                cy = centroid[1]
+
+                new = True
+
+                cv2.rectangle(roi, (x, y), (x + 50, y - 13), TRACKER_COLOR, -1)
+                cv2.putText(roi, 'TRUCK', (x, y - 2), FONT, .5, (255, 255, 255), 1, cv2.LINE_AA)
+
+                for i in objects:
+                    if abs(x - i.getX()) <= w and abs(y - i.getY()) <= h:
+                        new = False
+                        i.updateCoords(cx, cy)
+
+                        if i.going_DOWN(DOWN_limit) == True:
+                            cnt_trucks += 1
+                            if SAVE_IMAGE:
+                                save_frame(roi, IMAGE_DIR + "/truck_DOWN_%04d.png" % frame_number, flip=False)
+                                print("ID:", i.getId(), 'passed by the road', time.strftime("%c"))
+                        break
+                    if i.getState() == '1':
+                        if i.getDir() == 'down' and i.getY() > line_OUT:
+                            i.setDone()
+                    if i.timedOut():
+                        index = objects.index(i)
+                        objects.pop(index)
+                        del i
+                if new == True:
+                    p = validator.MyValidator(pid, cx, cy, max_p_age)
+                    objects.append(p)
+                    pid += 1
+                cv2.circle(roi, (cx, cy), 5, CENTROID_COLOR, -1)
+
+        for i in objects:
+            cv2.putText(roi, str(i.getId()), (i.getX(), i.getY()), FONT, 0.3, TEXT_COLOR, 1, cv2.LINE_AA)
+
+        str_cars = 'Cars: ' + str(cnt_cars)
+        str_trucks = 'Trucks: ' + str(cnt_trucks)
+
+        frame = cv2.line(frame, (w1, line_IN), (w1 + w2, line_IN), LINE_IN_COLOR, 2)
+        frame = cv2.line(frame, (w1, h1 + line_OUT), (w1 + w2, h1 + line_OUT), LINE_OUT_COLOR, 2)
+
+        cv2.putText(frame, str_cars, TEXT_POSITION_COUNT_CARS, FONT, 1, (255,255,255), 3, cv2.LINE_AA)
+        cv2.putText(frame, str_cars, TEXT_POSITION_COUNT_CARS, FONT, 1, (232, 162, 0), 2, cv2.LINE_AA)
+
+        cv2.putText(frame, str_trucks, TEXT_POSITION_COUNT_TRUCKS, FONT, 1, (255, 255, 255), 3, cv2.LINE_AA)
+        cv2.putText(frame, str_trucks, TEXT_POSITION_COUNT_TRUCKS, FONT, 1, (232, 162, 0), 2, cv2.LINE_AA)
+
+        cv2.putText(frame, 'Background Subtractor: ' + BGS_TYPE, TEXT_POSITION_BGS, FONT, TEXT_SIZE, (255, 255, 255), 3,cv2.LINE_AA)
+        cv2.putText(frame, 'Background Subtractor: ' + BGS_TYPE, TEXT_POSITION_BGS, FONT, TEXT_SIZE, (128, 0, 255), 2,
+                    cv2.LINE_AA)
+
+
+        cv2.imshow('Frame', frame)
+        cv2.imshow('Mask', bg_mask)
+
+        writer_video.write(frame)
+
+        if cv2.waitKey(1) & 0xFF == ord("q"):
+            break
+
+    cap.release()
+    cv2.destroyAllWindows()
+
+main()
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+

quality_comparison.py

@@ -0,0 +1,116 @@
+import numpy as np
+import cv2
+import sys
+from random import randint
+import csv
+
+fp = open('report.csv', mode='w')
+writer = csv.DictWriter(fp, fieldnames=['Frame', 'Pixel Count'])
+writer.writeheader()
+
+TEXT_COLOR = (randint(0, 255), randint(0,255), randint(0,255))
+BORDER_COLOR = (randint(0, 255), randint(0,255), randint(0,255))
+FONT = cv2.FONT_HERSHEY_SIMPLEX
+TEXT_SIZE = 1.2
+VIDEO_SOURCE = "videos/people.mp4"
+TITLE_TEXT_POSITION = (100, 40)
+
+BGS_TYPES = ["GMG", "MOG", "MOG2", "KNN", "CNT"]
+
+def getBGSubtractor(BGS_TYPE):
+    if BGS_TYPE == "GMG":
+        return cv2.bgsegm.createBackgroundSubtractorGMG()
+    if BGS_TYPE == "MOG":
+        return cv2.bgsegm.createBackgroundSubtractorMOG()
+    if BGS_TYPE == "MOG2":
+        return cv2.createBackgroundSubtractorMOG2()
+    if BGS_TYPE == "KNN":
+        return cv2.createBackgroundSubtractorKNN()
+    if BGS_TYPE == "CNT":
+        return cv2.bgsegm.createBackgroundSubtractorCNT()
+    print("Invalid detector")
+    sys.exit(1)
+
+cap = cv2.VideoCapture(VIDEO_SOURCE)
+
+bg_subtractor = []
+for i, a in enumerate(BGS_TYPES):
+    #print(i, a)
+    bg_subtractor.append(getBGSubtractor(a))
+
+#print(bg_subtractor)
+
+
+def main():
+    framecount = 0
+    while cap.isOpened():
+        ok, frame = cap.read()
+        #print(ok)
+
+        if not ok:
+            print('Finished processing the video')
+            break
+
+        framecount += 1
+        frame = cv2.resize(frame, (0, 0), fx=0.20, fy=0.20)
+
+        gmg = bg_subtractor[0].apply(frame)
+        mog = bg_subtractor[1].apply(frame)
+        mog2 = bg_subtractor[2].apply(frame)
+        knn = bg_subtractor[3].apply(frame)
+        cnt = bg_subtractor[4].apply(frame)
+
+        gmg_count = np.count_nonzero(gmg)
+        mog_count = np.count_nonzero(mog)
+        mog2_count = np.count_nonzero(mog2)
+        knn_count = np.count_nonzero(knn)
+        cnt_count = np.count_nonzero(cnt)
+
+        writer.writerow({'Frame': 'MOG', 'Pixel Count': mog_count})
+        writer.writerow({'Frame': 'MOG2', 'Pixel Count': mog2_count})
+        writer.writerow({'Frame': 'GMG', 'Pixel Count': gmg_count})
+        writer.writerow({'Frame': 'KNN', 'Pixel Count': knn_count})
+        writer.writerow({'Frame': 'CNT', 'Pixel Count': cnt_count})
+
+        cv2.putText(mog, 'MOG', TITLE_TEXT_POSITION, FONT, TEXT_SIZE, TEXT_COLOR, 2, cv2.LINE_AA)
+        cv2.putText(mog2, 'MOG2', TITLE_TEXT_POSITION, FONT, TEXT_SIZE, TEXT_COLOR, 2, cv2.LINE_AA)
+        cv2.putText(gmg, 'GMG', TITLE_TEXT_POSITION, FONT, TEXT_SIZE, TEXT_COLOR, 2, cv2.LINE_AA)
+        cv2.putText(knn, 'KNN', TITLE_TEXT_POSITION, FONT, TEXT_SIZE, TEXT_COLOR, 2, cv2.LINE_AA)
+        cv2.putText(cnt, 'CNT', TITLE_TEXT_POSITION, FONT, TEXT_SIZE, TEXT_COLOR, 2, cv2.LINE_AA)
+
+        cv2.imshow('Original', frame)
+        cv2.imshow('MOG', mog)
+        cv2.imshow('MOG2', mog2)
+        cv2.imshow('KNN', knn)
+        cv2.imshow('CNT', cnt)
+
+        cv2.moveWindow('Original', 0, 0)
+        cv2.moveWindow('MOG', 0, 250)
+        cv2.moveWindow('KNN', 0, 500)
+        cv2.moveWindow('GMG', 719, 0)
+        cv2.moveWindow('MOG2', 719, 250)
+        cv2.moveWindow('CNT', 719, 500)
+
+        k = cv2.waitKey(0) & 0xff
+        if k == 27:  # ESC
+            break
+
+
+main()
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+

quality_comparison_results.py

@@ -0,0 +1,11 @@
+import pandas as pd
+import matplotlib.pyplot as plt
+
+evaluation = pd.read_csv('report.csv')
+#print(evaluation)
+
+evaluation_results = evaluation.groupby(['Frame']).sum()
+print(evaluation_results)
+
+evaluation_results.plot.bar()
+plt.show()

report.csv

@@ -0,0 +1,1707 @@
+Frame,Pixel Count
+MOG,0
+MOG2,82944
+GMG,0
+KNN,82944
+CNT,82944
+
+MOG,1106
+MOG2,5550
+GMG,0
+KNN,82944
+CNT,82944
+MOG,1507
+MOG2,2984
+GMG,0
+KNN,82928
+CNT,82944
+MOG,1036
+MOG2,2826
+GMG,0
+KNN,82869
+CNT,82944
+MOG,1131
+MOG2,2634
+GMG,0
+KNN,7587
+CNT,82944
+MOG,1538
+MOG2,2814
+GMG,0
+KNN,6100
+CNT,82944
+MOG,1617
+MOG2,2536
+GMG,0
+KNN,5688
+CNT,82944
+MOG,2086
+MOG2,4659
+GMG,0
+KNN,6683
+CNT,82944
+MOG,2025
+MOG2,2656
+GMG,0
+KNN,6585
+CNT,82944
+MOG,2260
+MOG2,2671
+GMG,0
+KNN,6328
+CNT,82944
+MOG,2255
+MOG2,3642
+GMG,0
+KNN,7224
+CNT,82944
+MOG,1803
+MOG2,3770
+GMG,0
+KNN,7548
+CNT,82944
+MOG,2563
+MOG2,5775
+GMG,0
+KNN,9009
+CNT,82944
+MOG,2522
+MOG2,4708
+GMG,0
+KNN,8903
+CNT,82944
+MOG,2547
+MOG2,3572
+GMG,0
+KNN,9185
+CNT,82944
+MOG,2841
+MOG2,4361
+GMG,0
+KNN,9203
+CNT,11476
+MOG,2797
+MOG2,4978
+GMG,0
+KNN,9191
+CNT,11835
+MOG,3193
+MOG2,6423
+GMG,0
+KNN,9457
+CNT,12127
+MOG,3332
+MOG2,6143
+GMG,0
+KNN,9275
+CNT,12165
+MOG,3062
+MOG2,5828
+GMG,0
+KNN,8928
+CNT,12143
+MOG,2982
+MOG2,6405
+GMG,0
+KNN,8500
+CNT,12219
+MOG,3146
+MOG2,5641
+GMG,0
+KNN,9061
+CNT,12183
+MOG,3436
+MOG2,6863
+GMG,0
+KNN,9581
+CNT,12493
+MOG,3404
+MOG2,6557
+GMG,0
+KNN,9542
+CNT,12351
+MOG,3506
+MOG2,6465
+GMG,0
+KNN,9578
+CNT,12339
+MOG,3466
+MOG2,7325
+GMG,0
+KNN,9529
+CNT,12305
+MOG,3155
+MOG2,6867
+GMG,0
+KNN,9482
+CNT,12288
+MOG,3422
+MOG2,7185
+GMG,0
+KNN,9765
+CNT,12476
+MOG,3494
+MOG2,6794
+GMG,0
+KNN,9569
+CNT,12159
+MOG,3433
+MOG2,6516
+GMG,0
+KNN,9349
+CNT,12020
+MOG,3412
+MOG2,6533
+GMG,0
+KNN,9200
+CNT,11881
+MOG,3496
+MOG2,5648
+GMG,0
+KNN,9423
+CNT,11678
+MOG,3684
+MOG2,6334
+GMG,0
+KNN,9620
+CNT,11741
+MOG,3505
+MOG2,5913
+GMG,0
+KNN,9393
+CNT,11400
+MOG,3624
+MOG2,6065
+GMG,0
+KNN,9315
+CNT,11332
+MOG,3655
+MOG2,6356
+GMG,0
+KNN,9154
+CNT,11298
+MOG,3634
+MOG2,6153
+GMG,0
+KNN,8984
+CNT,11207
+MOG,3896
+MOG2,6744
+GMG,0
+KNN,9079
+CNT,11258
+MOG,3833
+MOG2,6697
+GMG,0
+KNN,8963
+CNT,10997
+MOG,3807
+MOG2,6509
+GMG,0
+KNN,8849
+CNT,10932
+MOG,3895
+MOG2,6628
+GMG,0
+KNN,9120
+CNT,10901
+MOG,3754
+MOG2,6495
+GMG,0
+KNN,9058
+CNT,10699
+MOG,3923
+MOG2,7065
+GMG,0
+KNN,9203
+CNT,10732
+MOG,3970
+MOG2,7028
+GMG,0
+KNN,9156
+CNT,10495
+MOG,3988
+MOG2,7177
+GMG,0
+KNN,9172
+CNT,10416
+MOG,4017
+MOG2,7259
+GMG,0
+KNN,9185
+CNT,10385
+MOG,4036
+MOG2,7306
+GMG,0
+KNN,9171
+CNT,10328
+MOG,4210
+MOG2,7862
+GMG,0
+KNN,9315
+CNT,10326
+MOG,4209
+MOG2,7954
+GMG,0
+KNN,9308
+CNT,10221
+MOG,4203
+MOG2,7968
+GMG,0
+KNN,9310
+CNT,10159
+MOG,4286
+MOG2,8613
+GMG,0
+KNN,9439
+CNT,10209
+MOG,4256
+MOG2,8520
+GMG,0
+KNN,9656
+CNT,10158
+MOG,4409
+MOG2,9092
+GMG,0
+KNN,9940
+CNT,10228
+MOG,4431
+MOG2,9139
+GMG,0
+KNN,10043
+CNT,10130
+MOG,4449
+MOG2,9281
+GMG,0
+KNN,10137
+CNT,10050
+MOG,4429
+MOG2,9425
+GMG,0
+KNN,10151
+CNT,10023
+MOG,4502
+MOG2,9545
+GMG,0
+KNN,10277
+CNT,9990
+MOG,4718
+MOG2,10163
+GMG,0
+KNN,10547
+CNT,10097
+MOG,4709
+MOG2,10031
+GMG,0
+KNN,10506
+CNT,10067
+MOG,4803
+MOG2,10160
+GMG,0
+KNN,10481
+CNT,10090
+MOG,4790
+MOG2,10168
+GMG,0
+KNN,10423
+CNT,10063
+MOG,4768
+MOG2,10106
+GMG,0
+KNN,10337
+CNT,10007
+MOG,4905
+MOG2,10435
+GMG,0
+KNN,10567
+CNT,10037
+MOG,4839
+MOG2,10347
+GMG,0
+KNN,10534
+CNT,9990
+MOG,4823
+MOG2,10287
+GMG,0
+KNN,10638
+CNT,9965
+MOG,4828
+MOG2,10299
+GMG,0
+KNN,10560
+CNT,9940
+MOG,4794
+MOG2,10273
+GMG,0
+KNN,10491
+CNT,9845
+MOG,4847
+MOG2,10745
+GMG,0
+KNN,10502
+CNT,9865
+MOG,4843
+MOG2,10819
+GMG,0
+KNN,10526
+CNT,9798
+MOG,4865
+MOG2,10796
+GMG,0
+KNN,10561
+CNT,9810
+MOG,4858
+MOG2,10667
+GMG,0
+KNN,10600
+CNT,9839
+MOG,4837
+MOG2,10723
+GMG,0
+KNN,10625
+CNT,9885
+MOG,5071
+MOG2,11237
+GMG,0
+KNN,11107
+CNT,10097
+MOG,5043
+MOG2,11313
+GMG,0
+KNN,11110
+CNT,10139
+MOG,5116
+MOG2,11269
+GMG,0
+KNN,11152
+CNT,10195
+MOG,5138
+MOG2,11805
+GMG,0
+KNN,11315
+CNT,10235
+MOG,5169
+MOG2,11695
+GMG,0
+KNN,11301
+CNT,10211
+MOG,5284
+MOG2,11772
+GMG,0
+KNN,11471
+CNT,10204
+MOG,5297
+MOG2,11747
+GMG,0
+KNN,11410
+CNT,10124
+MOG,5298
+MOG2,11661
+GMG,0
+KNN,11385
+CNT,10150
+MOG,5350
+MOG2,11590
+GMG,0
+KNN,11321
+CNT,10168
+MOG,5405
+MOG2,11582
+GMG,0
+KNN,11423
+CNT,10154
+MOG,5602
+MOG2,11800
+GMG,0
+KNN,11530
+CNT,10279
+MOG,5625
+MOG2,11615
+GMG,0
+KNN,11580
+CNT,10261
+MOG,5587
+MOG2,11605
+GMG,0
+KNN,11610
+CNT,10243
+MOG,5588
+MOG2,11701
+GMG,0
+KNN,11675
+CNT,10234
+MOG,5595
+MOG2,11708
+GMG,0
+KNN,11654
+CNT,10179
+MOG,5684
+MOG2,11974
+GMG,0
+KNN,11801
+CNT,10267
+MOG,5611
+MOG2,11918
+GMG,0
+KNN,11805
+CNT,10261
+MOG,5578
+MOG2,11738
+GMG,0
+KNN,11757
+CNT,10184
+MOG,5577
+MOG2,11654
+GMG,0
+KNN,11718
+CNT,10097
+MOG,5577
+MOG2,11577
+GMG,0
+KNN,11766
+CNT,10048
+MOG,5564
+MOG2,11650
+GMG,0
+KNN,11745
+CNT,9979
+MOG,5505
+MOG2,11525
+GMG,0
+KNN,11664
+CNT,9962
+MOG,5485
+MOG2,11413
+GMG,0
+KNN,11587
+CNT,9936
+MOG,5459
+MOG2,11322
+GMG,0
+KNN,11501
+CNT,9911
+MOG,5452
+MOG2,11245
+GMG,0
+KNN,11445
+CNT,9964
+MOG,5616
+MOG2,11413
+GMG,0
+KNN,11382
+CNT,10063
+MOG,5627
+MOG2,11288
+GMG,0
+KNN,11339
+CNT,9991
+MOG,5633
+MOG2,11189
+GMG,0
+KNN,11261
+CNT,10017
+MOG,5659
+MOG2,11464
+GMG,0
+KNN,11293
+CNT,10052
+MOG,5581
+MOG2,11375
+GMG,0
+KNN,11251
+CNT,10002
+MOG,5594
+MOG2,11947
+GMG,0
+KNN,11552
+CNT,9932
+MOG,5622
+MOG2,11776
+GMG,0
+KNN,11451
+CNT,9897
+MOG,5629
+MOG2,11666
+GMG,0
+KNN,11317
+CNT,9832
+MOG,5614
+MOG2,11602
+GMG,0
+KNN,11172
+CNT,9745
+MOG,5614
+MOG2,11570
+GMG,0
+KNN,11004
+CNT,9703
+MOG,5643
+MOG2,11355
+GMG,0
+KNN,10818
+CNT,9563
+MOG,5703
+MOG2,11405
+GMG,0
+KNN,10782
+CNT,9593
+MOG,5765
+MOG2,11524
+GMG,0
+KNN,10768
+CNT,9615
+MOG,5770
+MOG2,11512
+GMG,0
+KNN,10745
+CNT,9589
+MOG,5754
+MOG2,11590
+GMG,0
+KNN,10723
+CNT,9565
+MOG,5827
+MOG2,11551
+GMG,0
+KNN,10647
+CNT,9440
+MOG,5803
+MOG2,11674
+GMG,0
+KNN,10640
+CNT,9392
+MOG,5786
+MOG2,11730
+GMG,0
+KNN,10707
+CNT,9323
+MOG,5678
+MOG2,11615
+GMG,0
+KNN,10650
+CNT,9148
+MOG,5554
+MOG2,11456
+GMG,0
+KNN,10534
+CNT,9000
+MOG,5502
+MOG2,11713
+GMG,0
+KNN,10557
+CNT,8903
+MOG,5429
+MOG2,11477
+GMG,0
+KNN,10395
+CNT,8821
+MOG,5318
+MOG2,11372
+GMG,0
+KNN,10350
+CNT,8743
+MOG,5234
+MOG2,11300
+GMG,82944
+KNN,10262
+CNT,8700
+MOG,5064
+MOG2,10952
+GMG,2032
+KNN,10048
+CNT,8478
+MOG,5056
+MOG2,11285
+GMG,6140
+KNN,10111
+CNT,8402
+MOG,5008
+MOG2,10997
+GMG,8411
+KNN,10014
+CNT,8312
+MOG,4999
+MOG2,10943
+GMG,7571
+KNN,9940
+CNT,8266
+MOG,4956
+MOG2,11127
+GMG,8955
+KNN,10039
+CNT,8255
+MOG,4904
+MOG2,11054
+GMG,8722
+KNN,9984
+CNT,8215
+MOG,4827
+MOG2,10830
+GMG,10030
+KNN,9946
+CNT,8011
+MOG,4739
+MOG2,10658
+GMG,6216
+KNN,9940
+CNT,7935
+MOG,4687
+MOG2,10545
+GMG,5404
+KNN,9887
+CNT,7863
+MOG,4612
+MOG2,10388
+GMG,3767
+KNN,9793
+CNT,7826
+MOG,4540
+MOG2,10139
+GMG,3229
+KNN,9733
+CNT,7755
+MOG,4511
+MOG2,10094
+GMG,4513
+KNN,9621
+CNT,7742
+MOG,4449
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+GMG,2370
+KNN,8705
+CNT,7557
+MOG,4488
+MOG2,8883
+GMG,2326
+KNN,8741
+CNT,7542
+MOG,4417
+MOG2,8809
+GMG,1976
+KNN,8665
+CNT,7500

temporal_median_filter.py

@@ -0,0 +1,100 @@
+import numpy as np
+import cv2 # opencv
+#print(cv2.__version__)
+
+VIDEO_SOURCE = 'videos/Cars.mp4'
+VIDEO_OUT = 'videos/results/temporal_median_filter.avi'
+
+cap = cv2.VideoCapture(VIDEO_SOURCE)
+has_frame, frame = cap.read()
+#print(has_frame, frame.shape)
+
+fourcc = cv2.VideoWriter_fourcc(*'XVID')
+# https://docs.opencv.org/3.4/dd/d9e/classcv_1_1VideoWriter.html
+writer = cv2.VideoWriter(VIDEO_OUT, fourcc, 25, (frame.shape[1], frame.shape[0]), False)
+
+#print(cap.get(cv2.CAP_PROP_FRAME_COUNT))
+#print(np.random.uniform(size = 25))
+frames_ids = cap.get(cv2.CAP_PROP_FRAME_COUNT) * np.random.uniform(size = 25)
+#print(frames_ids)
+
+#cap.set(cv2.CAP_PROP_POS_FRAMES, 825)
+#has_frame, frame = cap.read()
+#cv2.imshow('Test', frame)
+#cv2.waitKey(0)
+
+frames = []
+for fid in frames_ids:
+    cap.set(cv2.CAP_PROP_POS_FRAMES, fid)
+    has_frame, frame = cap.read()
+    frames.append(frame)
+
+#print(np.asarray(frames).shape)
+#print(frames[1])
+
+#for frame in frames:
+#    cv2.imshow('Frame', frame)
+#    cv2.waitKey(0)
+
+#print(np.mean([1, 3, 5, 6, 8, 9]))
+#print((1 + 3 + 5 + 6 + 8 + 9) / 6)
+#print(np.median([[1, 3, 5, 6, 8, 9]]))
+#print((5 + 6) / 2)
+#print(np.median([1, 3, 4, 5, 6, 8, 9]))
+
+median_frame = np.median(frames, axis = 0).astype(dtype=np.uint8)
+#print(frame[0])
+#print(median_frame)
+#cv2.imshow('Median frame', median_frame)
+#cv2.waitKey(0)
+
+cv2.imwrite('model_median_frame.jpg', median_frame)
+
+cap.set(cv2.CAP_PROP_POS_FRAMES, 0)
+gray_median_frame = cv2.cvtColor(median_frame, cv2.COLOR_BGR2GRAY)
+#cv2.imshow('Gray', gray_median_frame)
+#cv2.waitKey(0)
+
+while (True):
+    has_frame, frame = cap.read()
+
+    if not has_frame:
+        print('End of the video')
+        break
+
+    frame_gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
+    dframe = cv2.absdiff(frame_gray, gray_median_frame)
+    # If pixel intensity is greater than the set threshold, value set to 255, else set to 0(black).
+    #th, dframe = cv2.threshold(dframe, 70, 255, cv2.THRESH_BINARY)
+    th, dframe = cv2.threshold(dframe, 0, 255, cv2.THRESH_BINARY | cv2.THRESH_OTSU)
+    print(th)
+
+    cv2.imshow('Frame', dframe)
+    writer.write(dframe)
+
+    if cv2.waitKey(1) & 0xFF == ord('q'):
+        break
+
+writer.release()
+cap.release()
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+

test.py

@@ -0,0 +1 @@
+print('Testing the project')

validator.py

@@ -0,0 +1,96 @@
+# Based on: https://github.com/sarful/People-counter-opencv-python3/blob/master/Person.py
+
+import math
+
+class MyValidator:
+    tracks = []
+
+    def __init__(self, i, xi, yi, max_age):
+        self.i = i
+        self.x = xi
+        self.y = yi
+        self.tracks = []
+        self.done = False
+        self.state = '0'
+        self.age = 0
+        self.max_age = max_age
+        self.dir = None
+
+    def getTracks(self):
+        return self.tracks
+
+    def getId(self):
+        return self.i
+
+    def getState(self):
+        return self.state
+
+    def getDir(self):
+        return self.dir
+
+    def getX(self):
+        return self.x
+
+    def getY(self):
+        return self.y
+
+    def updateCoords(self, xn, yn):
+        self.age = 0
+        self.tracks.append([self.x, self.y])
+        self.x = xn
+        self.y = yn
+
+    def setDone(self):
+        self.done = True
+
+    def timedOut(self):
+        return self.done
+
+    def going_DOWN(self, mid_start):
+
+        # Check if you have at least 2 coordinates of stored objects
+        if len(self.tracks) >= 2:
+
+            # Check if the condition of the object is zero
+            # The state of the object will only change when crossing the input threshold
+            if self.state == '0':
+
+                # Euclidian distance calculation
+                distance = math.sqrt(float((self.tracks[-1][1] - self.tracks[-2][1])**2) + float(
+                    (self.tracks[-1][1] - self.tracks[-2][1])**2))
+                if distance < 10:
+                    # [-2] are two previous positions of the vector record and [1] is the column containing the
+                    # vertical values (y) of each object
+                    # If the anterior vertical position of the object is greater than the input threshold and if in
+                    # two anterior vertical positions the value is less than or equal to the input threshold
+                    # we updated the state of the object to 1 and indicated that it moved downwards (down)
+                    # We do this to make sure that the object crossed the entrance line, moving from top to bottom
+                    if self.tracks[-1][1] > mid_start and self.tracks[-2][1] <= mid_start:
+                        state = '1'
+                        self.dir = 'down'
+                        return True
+            else:
+                return False
+        else:
+            return False
+
+    def going_UP(self, mid_end):
+        if len(self.tracks) >= 2:
+            if self.state == '0':
+                distance = math.sqrt(float((self.tracks[-1][1] - self.tracks[-2][1])**2) + float(
+                    (self.tracks[-1][1] - self.tracks[-2][1])**2))
+                if distance < 10:
+                    if self.tracks[-1][1] < mid_end and self.tracks[-2][1] >= mid_end:
+                        state = '1'
+                        self.dir = 'up'
+                        return True
+            else:
+                return False
+        else:
+            return False
+
+    def age_one(self):
+        self.age += 1
+        if self.age > self.max_age:
+            self.done = True
+        return True