def tracking(self, frame):
ct = CentroidTracker(maxDisappeared=10, maxDistance=30)
# use the centroid tracker to associate the (1) old object
# centroids with (2) the newly computed object centroids
objects = ct.update(self.rects)
for (objectID, centroid) in objects.items():
# check to see if a trackable object exists for the current
# object ID
to = self.trackableObjects.get(objectID, None)
# print("ID: " + str(objectID) + " cX, cY: " + str(centroid))
# if there is no existing trackable object, create one
if to is None:
to = TrackableObject(objectID, centroid)
# otherwise, there is a trackable object so we can utilize it
# to determine direction
else:
# the difference between the y-coordinate of the *current*
# centroid and the mean of *previous* centroids will tell
# us in which direction the object is moving (negative for
# 'up' and positive for 'down')
y = [c[1] for c in to.centroids]
print(y)
direction = centroid[1] - np.mean(y)
# print(str(np.mean(x)))
# print("direction: " + str(direction) + " centroid: " + str(centroid[0]))
# print("dir: " + str(direction) + "centroid:" + str(centroid[0]) + ", " + str(centroid[1]))
to.centroids.append(centroid)
# check to see if the object has been counted or not
if not to.counted:
# if the direction is negative (indicating the object
# is moving up) AND the centroid is above the center
# line, count the object
if direction < 0 and y[0] >= 240 and y[-1] <= 240:
self.totalUp += 1
to.counted = True
# if the direction is positive (indicating the object
# is moving down) AND the centroid is below the
# center line, count the object
elif direction > 0 and y[0] <= 240 and y[-1] >= 240:
self.totalDown += 1
to.counted = True
# store the trackable object in our dictionary
self.trackableObjects[objectID] = to
# отображение центроида объекта
cv2.circle(frame, (centroid[0], centroid[1]), 6, (255, 255, 255),
-1)
# ID объекта
text = "ID {}".format(objectID)
cv2.putText(frame, text, (centroid[0] - 10, centroid[1] - 10),
cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 255, 255), 2)
def MarkPeople(objects, total):
count = 0
# loop over the tracked objects
for (objectID, centroid) in objects.items():
# check to see if a trackable object exists for the current
# object ID
to = trackableObjects.get(objectID, None)
# if there is no existing trackable object, create one
if to is None:
to = TrackableObject(objectID, centroid)
# otherwise, there is a trackable object so we can utilize it
else:
to.centroids.append(centroid)
# check to see if the object has been counted or not
if not to.counted:
total += 1
to.counted = True
# store the trackable object in our dictionary
trackableObjects[objectID] = to
# draw both the ID of the object and the centroid of the
# object on the output frame
text = "ID {}".format(objectID)
cv.putText(frame, text, (centroid[0] - 10, centroid[1] - 10),
cv.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2)
cv.circle(frame, (centroid[0], centroid[1]), 4, (0, 255, 0), -1)
count += 1
return count, total
def countVehicles(param):
# param -> path of the video
# list -> number of vehicles will be written in the list
# index ->Index at which data has to be written
tf.disable_v2_behavior()
# Image size must be '416x416' as YoloV3 network expects that specific image size as input
img_size = 416
inputs = tf.placeholder(tf.float32, [None, img_size, img_size, 3])
model = nets.YOLOv3COCO(inputs, nets.Darknet19)
ct = CentroidTracker(
maxDisappeared=5, maxDistance=50
) # Look into 'CentroidTracker' for further info about parameters
trackers = [] # List of all dlib trackers
trackableObjects = {
} # Dictionary of trackable objects containing object's ID and its' corresponding centroid/s
skip_frames = 10 # Numbers of frames to skip from detecting
confidence_level = 0.40 # The confidence level of a detection
total = 0 # Total number of detected objects from classes of interest
use_original_video_size_as_output_size = True # Shows original video as output and not the 416x416 image that is used as yolov3 input (NOTE: Detection still happens with 416x416 img size but the output is displayed in original video size if this parameter is True)
video_path = os.getcwd() + param # "/videos/4.mp4"
video_name = os.path.basename(video_path)
# print("Loading video {video_path}...".format(video_path=video_path))
if not os.path.exists(video_path):
print("File does not exist. Exited.")
exit()
# YoloV3 detects 80 classes represented below
all_classes = ["person", "bicycle", "car", "motorbike", "aeroplane", "bus", "train", "truck", \
"boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", \
"bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", \
"backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", \
"sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", \
"tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", \
"apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", \
"chair", "sofa", "pottedplant", "bed", "diningtable", "toilet", "tvmonitor", "laptop", "mouse", \
"remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", \
"book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"]
# Classes of interest (with their corresponding indexes for easier looping)
classes = {1: 'bicycle', 2: 'car', 3: 'motorbike', 5: 'bus', 7: 'truck'}
with tf.Session() as sess:
sess.run(model.pretrained())
cap = cv2.VideoCapture(video_path)
# Get video size (just for log purposes)
width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
# Scale used for output window size and net size
width_scale = 1
height_scale = 1
if use_original_video_size_as_output_size:
width_scale = width / img_size
height_scale = height / img_size
def drawRectangleCV2(img,
pt1,
pt2,
color,
thickness,
width_scale=width_scale,
height_scale=height_scale):
point1 = (int(pt1[0] * width_scale), int(pt1[1] * height_scale))
point2 = (int(pt2[0] * width_scale), int(pt2[1] * height_scale))
return cv2.rectangle(img, point1, point2, color, thickness)
def drawTextCV2(img,
text,
pt,
font,
font_scale,
color,
lineType,
width_scale=width_scale,
height_scale=height_scale):
pt = (int(pt[0] * width_scale), int(pt[1] * height_scale))
cv2.putText(img, text, pt, font, font_scale, color, lineType)
def drawCircleCV2(img,
center,
radius,
color,
thickness,
width_scale=width_scale,
height_scale=height_scale):
center = (int(center[0] * width_scale),
int(center[1] * height_scale))
cv2.circle(img, center, radius, color, thickness)
# Python 3.5.6 does not support f-strings (next line will generate syntax error)
#print(f"Loaded {video_path}. Width: {width}, Height: {height}")
# print("Loaded {video_path}. Width: {width}, Height: {height}".format(video_path=video_path, width=width, height=height))
skipped_frames_counter = 0
while (cap.isOpened()):
try:
ret, frame = cap.read()
img = cv2.resize(frame, (img_size, img_size))
except:
print(total_str)
output_img = frame if use_original_video_size_as_output_size else img
tracker_rects = []
if skipped_frames_counter == skip_frames:
# Detecting happens after number of frames have passes specified by 'skip_frames' variable value
# print("[DETECTING]")
trackers = []
skipped_frames_counter = 0 # reset counter
np_img = np.array(img).reshape(-1, img_size, img_size, 3)
start_time = time.time()
predictions = sess.run(model.preds,
{inputs: model.preprocess(np_img)})
# print("Detection took %s seconds" % (time.time() - start_time))
# model.get_boxes returns a 80 element array containing information about detected classes
# each element contains a list of detected boxes, confidence level ...
detections = model.get_boxes(predictions, np_img.shape[1:3])
np_detections = np.array(detections)
# Loop only through classes we are interested in
for class_index in classes.keys():
local_count = 0
class_name = classes[class_index]
# Loop through detected infos of a class we are interested in
for i in range(len(np_detections[class_index])):
box = np_detections[class_index][i]
if np_detections[class_index][i][4] >= confidence_level:
# print("Detected ", class_name, " with confidence of ", np_detections[class_index][i][4])
local_count += 1
startX, startY, endX, endY = box[0], box[1], box[
2], box[3]
drawRectangleCV2(output_img, (startX, startY),
(endX, endY), (0, 255, 0), 1)
drawTextCV2(output_img, class_name,
(startX, startY),
cv2.FONT_HERSHEY_SIMPLEX, .5,
(0, 0, 255), 1)
# Construct a dlib rectangle object from the bounding box coordinates and then start the dlib correlation
tracker = dlib.correlation_tracker()
rect = dlib.rectangle(int(startX), int(startY),
int(endX), int(endY))
tracker.start_track(img, rect)
# Add the tracker to our list of trackers so we can utilize it during skip frames
trackers.append(tracker)
# Write the total number of detected objects for a given class on this frame
# print(class_name," : ", local_count)
else:
# If detection is not happening then track previously detected objects (if any)
# print("[TRACKING]")
skipped_frames_counter += 1 # Increase the number frames for which we did not use detection
# Loop through tracker, update each of them and display their rectangle
for tracker in trackers:
tracker.update(img)
pos = tracker.get_position()
# Unpack the position object
startX = int(pos.left())
startY = int(pos.top())
endX = int(pos.right())
endY = int(pos.bottom())
# Add the bounding box coordinates to the tracking rectangles list
tracker_rects.append((startX, startY, endX, endY))
# Draw tracking rectangles
drawRectangleCV2(output_img, (startX, startY),
(endX, endY), (255, 0, 0), 1)
# Use the centroid tracker to associate the (1) old object centroids with (2) the newly computed object centroids
objects = ct.update(tracker_rects)
# Loop over the tracked objects
for (objectID, centroid) in objects.items():
# Check to see if a trackable object exists for the current object ID
to = trackableObjects.get(objectID, None)
if to is None:
# If there is no existing trackable object, create one
to = TrackableObject(objectID, centroid)
else:
to.centroids.append(centroid)
# If the object has not been counted, count it and mark it as counted
if not to.counted:
total += 1
to.counted = True
# Store the trackable object in our dictionary
trackableObjects[objectID] = to
# Draw both the ID of the object and the centroid of the object on the output frame
object_id = "ID {}".format(objectID)
drawTextCV2(output_img, object_id,
(centroid[0] - 10, centroid[1] - 10),
cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 1)
drawCircleCV2(output_img, (centroid[0], centroid[1]), 2,
(0, 255, 0), -1)
# Display the total count so far
total_str = str(total)
drawTextCV2(output_img, total_str, (10, 30),
cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 0, 255), 2)
# Display the current frame (with all annotations drawn up to this point)
cv2.imshow(video_name, output_img)
key = cv2.waitKey(1) & 0xFF
if key == ord('q'): # QUIT (exits)
break
elif key == ord('p'):
cv2.waitKey(0) # PAUSE (Enter any key to continue)
cap.release()
cv2.destroyAllWindows()
print("Exited")
"""
def counter(filenameOpen, filenameSave):
# 0.55,30 for örnek1-2 ; 0.5,5 for örnek3 ;0.45,11 for örnek4 ;0,55,20 for örnek5 ;
defaultConfidence = 0.55 #threshold value
defaultSkipFrames = 20 #skipped frame
W = None
H = None
writer = None
# loading model-artificial neural networks
net = cv2.dnn.readNetFromCaffe(
"mobilenet_ssd/MobileNetSSD_deploy.prototxt",
"mobilenet_ssd/MobileNetSSD_deploy.caffemodel")
print("Video yükleniyor..")
vs = cv2.VideoCapture(filenameOpen)
#each id is given to store objects.25,20 for örnek 6; others 40,50 ;
ct = CentroidTracker(
maxDisappeared=25,
maxDistance=20) #boundary lines of objects are determined
trackers = []
trackableObjects = {}
# variables are determined
totalFrames = 0
totalDown = 0
totalUp = 0
#fps output estimator per second
fps = FPS().start()
stat = {}
while True:
# VideoCapture
ok, frame = vs.read()
if filenameOpen is not None and frame is None:
break
#we resized the frame to a maximum of 500 pixels.
#(the less data it has, the faster we can process it),
#then convert the frame for dlib from BGR to RGB.
frame = cv2.resize(frame, (640, 480))
rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
#frame sizes are adjusted according to the video.
if W is None or H is None:
(H, W) = frame.shape[:2]
#to save the file
if filenameSave is not None and writer is None:
fourcc = cv2.VideoWriter_fourcc(*"MJPG")
writer = cv2.VideoWriter(filenameSave, fourcc, 30, (W, H), True)
status = "Bekleniyor"
rects = []
#we set the sampling time to avoid tiring the processor
videotime = vs.get(cv2.CAP_PROP_POS_MSEC) / 1000
summ = totalUp + totalDown
if totalFrames % 50 == 0:
stat["{:.4s}".format(str(videotime))] = str(summ)
#input to print total number
#print("{:.4s}".format(str(videotime)) + " people: " + str(summ))
if totalFrames % defaultSkipFrames == 0:
# set the status and initialize our new set of object trackers
status = "Bulundu"
trackers = []
#the part where we convert the frame dimensions to blob
blob = cv2.dnn.blobFromImage(frame, 0.007843, (W, H), 127.5)
net.setInput(blob)
detections = net.forward()
#all detected objects
for i in np.arange(0, detections.shape[2]):
confidence = detections[0, 0, i, 2]
#is the forecast value greater than the threshold value?
if confidence > defaultConfidence:
idx = int(detections[0, 0, i, 1])
#ignore if not human
if CLASSES[idx] != "person":
continue
#frame the object
box = detections[0, 0, i, 3:7] * np.array([W, H, W, H])
(startX, startY, endX, endY) = box.astype("int")
tracker = dlib.correlation_tracker()
rect = dlib.rectangle(startX, startY, endX, endY)
tracker.start_track(rgb, rect)
trackers.append(tracker)
else:
#loop over the trackers
for tracker in trackers:
status = "Takip"
#update the viewer and capture the updated location
tracker.update(rgb)
pos = tracker.get_position()
startX = int(pos.left())
startY = int(pos.top())
endX = int(pos.right())
endY = int(pos.bottom())
#bounding box coordinates added to list of rectangles
rects.append((startX, startY, endX, endY))
#A line is drawn to see if they go up or down
#If it changes here, line 149 needs to be corrected.
#cv2.line(frame, (0, 0), (W, H), (0, 255, 255), 2)#cross
cv2.line(frame, (0, H // 2), (W, H // 2), (255, 255, 0),
2) #horizontal
#cv2.line(frame, (W//2, 0), (W//2, H), (0, 255, 255), 2) #vertical
#associating objects.
objects = ct.update(rects)
#object is looped
for (objectID, centroid) in objects.items():
to = trackableObjects.get(objectID, None)
if to is None:
to = TrackableObject(objectID, centroid)
#this function is entered to determine the direction.
else:
#depending on the direction of movement, whether it is up or down
y = [c[1] for c in to.centroids]
direction = centroid[1] - np.mean(y)
to.centroids.append(centroid)
if not to.counted:
if direction < 0 and centroid[1] < (H) // 2:
totalUp += 1
to.counted = True
elif direction > 0 and centroid[1] > (H) // 2:
totalDown += 1
to.counted = True
#traceable object is given id
trackableObjects[objectID] = to
#the center of the object is selected and the id number is written
text = "ID {}".format(objectID)
cv2.putText(frame, text, (centroid[0] - 10, centroid[1] - 10),
cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 255), 2)
cv2.circle(frame, (centroid[0], centroid[1]), 4, (0, 255, 255), -1)
#screen printouts
info = [("Yukari", totalUp), ("Asagi", totalDown),
("Sure", "{:.2f}".format(videotime))]
for (i, (k, v)) in enumerate(info):
text = "{}: {}".format(k, v)
cv2.putText(frame, text, (10, H - ((i * 20) + 20)),
cv2.FONT_HERSHEY_SIMPLEX, 0.6, (255, 255, 0), 2)
#checking if the video has been saved
if writer is not None:
writer.write(frame)
# show the output frame
cv2.imshow(
"Ege Universitesi Bitirme Tezi(Caner YILDIRIM-Emir Kaan YERLI)",
frame)
key = cv2.waitKey(1) & 0xFF
#exit the loop
if key == ord("q") or key == ord("Q"):
break
totalFrames += 1
fps.update()
fps.stop()
#video recording is ending
if writer is not None:
writer.release()
#if we are not using a video file, stop the camera video stream
if not filenameOpen:
vs.stop()
#otherwise, release the video file pointer
else:
vs.release()
#close the windows
cv2.destroyAllWindows()
return info, stat
# moving 'up' or 'down'
cv2.line(frame, (0, H // 2), (W, H // 2), (0, 255, 255), 2)
# use the centroid tracker to associate the (1) old object
# centroids with (2) the newly computed object centroids
objects = ct.update(rects)
# loop over the tracked objects
for (objectID, centroid) in objects.items():
# check to see if a trackable object exists for the current
# object ID
to = trackableObjects.get(objectID, None)
# if there is no existing trackable object, create one
if to is None:
to = TrackableObject(objectID, centroid)
# otherwise, there is a trackable object so we can utilize it
# to determine direction
else:
# the difference between the y-coordinate of the *current*
# centroid and the mean of *previous* centroids will tell
# us in which direction the object is moving (negative for
# 'up' and positive for 'down')
y = [c[1] for c in to.centroids]
direction = centroid[1] - np.mean(y)
to.centroids.append(centroid)
# check to see if the object has been counted or not
if not to.counted:
# if the direction is negative (indicating the object
def counter(filenameOpen, filenameSave):
# construct the argument parse and parse the arguments
# ap = argparse.ArgumentParser()
# ap.add_argument("-i", "--input", type=str,
# help="path to optional input video file")
# ap.add_argument("-o", "--output", type=str,
# help="path to optional output video file")
# args = vars(ap.parse_args())
defaultConfidence = 0.4 # минимальный процент вероятности обнаружения
defaultSkipFrames = 30 # пропущенное количество кадров между обнаружениями
W = None # размеры кадра
H = None
writer = None
# загрузка модели
net = cv2.dnn.readNetFromCaffe(
"mobilenet_ssd/MobileNetSSD_deploy.prototxt",
"mobilenet_ssd/MobileNetSSD_deploy.caffemodel")
# if not args.get("input", False): # если отсутствует путь к видео -- захватить видео с веб-камеры
# print("[INFO] starting video stream...")
# vs = VideoStream(src=0).start()
# time.sleep(2.0)
# else: # в противном случае взять видеофайл
print("[INFO] opening video file...")
vs = cv2.VideoCapture(filenameOpen)
# instantiate our centroid tracker, then initialize a list to store
# each of our dlib correlation trackers, followed by a dictionary to
# map each unique object ID to a TrackableObject
ct = CentroidTracker(maxDisappeared=40, maxDistance=50)
trackers = []
trackableObjects = {}
# initialize the total number of frames processed thus far, along
# with the total number of objects that have moved either up or down
totalFrames = 0
totalDown = 0
totalUp = 0
# start the frames per second throughput estimator
fps = FPS().start()
stat = {}
# loop over frames from the video stream
while True:
# grab the next frame and handle if we are reading from either
# VideoCapture or VideoStream
ok, frame = vs.read()
# frame = frame[1] if args.get("input", False) else frame
# if we are viewing a video and we did not grab a frame then we
# have reached the end of the video
if filenameOpen is not None and frame is None:
break
# resize the frame to have a maximum width of 500 pixels (the
# less data we have, the faster we can process it), then convert
# the frame from BGR to RGB for dlib
frame = cv2.resize(frame, (640, 480))
rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
# if the frame dimensions are empty, set them
if W is None or H is None:
(H, W) = frame.shape[:2]
# if we are supposed to be writing a video to disk, initialize
# the writer
if filenameSave is not None and writer is None:
fourcc = cv2.VideoWriter_fourcc(*"MJPG")
writer = cv2.VideoWriter(filenameSave, fourcc, 30, (W, H), True)
# initialize the current status along with our list of bounding
# box rectangles returned by either (1) our object detector or
# (2) the correlation trackers
status = "Waiting"
rects = []
# check to see if we should run a more computationally expensive
# object detection method to aid our tracker
videotime = vs.get(cv2.CAP_PROP_POS_MSEC) / 1000
summ = totalUp + totalDown
if totalFrames % 50 == 0:
stat["{:.4s}".format(str(videotime))] = str(summ)
# print("{:.4s}".format(str(videotime)) + " people: " + str(summ))
if totalFrames % defaultSkipFrames == 0:
# set the status and initialize our new set of object trackers
status = "Detecting"
trackers = []
# convert the frame to a blob and pass the blob through the
# network and obtain the detections
blob = cv2.dnn.blobFromImage(frame, 0.007843, (W, H), 127.5)
net.setInput(blob)
detections = net.forward()
# loop over the detections
for i in np.arange(0, detections.shape[2]):
# extract the confidence (i.e., probability) associated
# with the prediction
confidence = detections[0, 0, i, 2]
# filter out weak detections by requiring a minimum
# confidence
if confidence > defaultConfidence:
# extract the index of the class label from the
# detections list
idx = int(detections[0, 0, i, 1])
# if the class label is not a person, ignore it
if classes.CLASSES[idx] != "person":
continue
# compute the (x, y)-coordinates of the bounding box
# for the object
box = detections[0, 0, i, 3:7] * np.array([W, H, W, H])
(startX, startY, endX, endY) = box.astype("int")
# construct a dlib rectangle object from the bounding
# box coordinates and then start the dlib correlation
# tracker
tracker = dlib.correlation_tracker()
rect = dlib.rectangle(startX, startY, endX, endY)
tracker.start_track(rgb, rect)
# add the tracker to our list of trackers so we can
# utilize it during skip frames
trackers.append(tracker)
# otherwise, we should utilize our object *trackers* rather than
# object *detectors* to obtain a higher frame processing throughput
else:
# loop over the trackers
for tracker in trackers:
# set the status of our system to be 'tracking' rather
# than 'waiting' or 'detecting'
status = "Tracking"
# update the tracker and grab the updated position
tracker.update(rgb)
pos = tracker.get_position()
# unpack the position object
startX = int(pos.left())
startY = int(pos.top())
endX = int(pos.right())
endY = int(pos.bottom())
# add the bounding box coordinates to the rectangles list
rects.append((startX, startY, endX, endY))
# draw a horizontal line in the center of the frame -- once an
# object crosses this line we will determine whether they were
# moving 'up' or 'down'
# cv2.line(frame, (0, 0), (W, H), (0, 255, 255), 2)
cv2.line(frame, (0, H // 2), (W, H // 2), (0, 255, 255), 2)
# use the centroid tracker to associate the (1) old object
# centroids with (2) the newly computed object centroids
objects = ct.update(rects)
# loop over the tracked objects
for (objectID, centroid) in objects.items():
# check to see if a trackable object exists for the current
# object ID
to = trackableObjects.get(objectID, None)
# if there is no existing trackable object, create one
if to is None:
to = TrackableObject(objectID, centroid)
# otherwise, there is a trackable object so we can utilize it
# to determine direction
else:
# the difference between the y-coordinate of the *current*
# centroid and the mean of *previous* centroids will tell
# us in which direction the object is moving (negative for
# 'up' and positive for 'down')
y = [c[1] for c in to.centroids]
direction = centroid[1] - np.mean(y)
to.centroids.append(centroid)
# check to see if the object has been counted or not
if not to.counted:
# if the direction is negative (indicating the object
# is moving up) AND the centroid is above the center
# line, count the object
if direction < 0 and centroid[1] < H // 2:
totalUp += 1
to.counted = True
# if the direction is positive (indicating the object
# is moving down) AND the centroid is below the
# center line, count the object
elif direction > 0 and centroid[1] > H // 2:
totalDown += 1
to.counted = True
# store the trackable object in our dictionary
trackableObjects[objectID] = to
# draw both the ID of the object and the centroid of the
# object on the output frame
text = "ID {}".format(objectID)
cv2.putText(frame, text, (centroid[0] - 10, centroid[1] - 10),
cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2)
cv2.circle(frame, (centroid[0], centroid[1]), 4, (0, 255, 0), -1)
# construct a tuple of information we will be displaying on the
# frame
info = [("Up", totalUp), ("Down", totalDown),
("Time", "{:.4f}".format(videotime))]
# loop over the info tuples and draw them on our frame
for (i, (k, v)) in enumerate(info):
text = "{}: {}".format(k, v)
cv2.putText(frame, text, (10, H - ((i * 20) + 20)),
cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 0, 255), 2)
# check to see if we should write the frame to disk
if writer is not None:
writer.write(frame)
# show the output frame
cv2.imshow("Frame", frame)
key = cv2.waitKey(1) & 0xFF
# if the `q` key was pressed, break from the loop
if key == ord("q"):
break
# increment the total number of frames processed thus far and
# then update the FPS counter
totalFrames += 1
fps.update()
# stop the timer and display FPS information
fps.stop()
# check to see if we need to release the video writer pointer
if writer is not None:
writer.release()
# if we are not using a video file, stop the camera video stream
if not filenameOpen:
vs.stop()
# otherwise, release the video file pointer
else:
vs.release()
# close any open windows
cv2.destroyAllWindows()
# print(stat)
return info, stat