#!/usr/bin/env python3
#----------------------------------------------------------------------------
# Copyright (c) 2018 FIRST. All Rights Reserved.
# Open Source Software - may be modified and shared by FRC teams. The code
# must be accompanied by the FIRST BSD license file in the root directory of
# the project.
# My 2019 license: use it as much as you want. Crediting is recommended because it lets me know that I am being useful.
# Credit to Screaming Chickens 3997
# This is meant to be used in conjuction with WPILib Raspberry Pi image: https://github.com/wpilibsuite/FRCVision-pi-gen
#----------------------------------------------------------------------------
import json
import time
import sys
from threading import Thread
from cscore import CameraServer, VideoSource
from networktables import NetworkTablesInstance
import cv2
import numpy as np
from networktables import NetworkTables
import math
########### SET RESOLUTION TO 256x144 !!!! ############
# import the necessary packages
import datetime
#Class to examine Frames per second of camera stream. Currently not used.
class FPS:
def __init__(self):
# store the start time, end time, and total number of frames
# that were examined between the start and end intervals
self._start = None
self._end = None
self._numFrames = 0
def start(self):
# start the timer
self._start = datetime.datetime.now()
return self
def stop(self):
# stop the timer
self._end = datetime.datetime.now()
def update(self):
# increment the total number of frames examined during the
# start and end intervals
self._numFrames += 1
def elapsed(self):
# return the total number of seconds between the start and
# end interval
return (self._end - self._start).total_seconds()
def fps(self):
# compute the (approximate) frames per second
return self._numFrames / self.elapsed()
# class that runs separate thread for showing video,
class VideoShow:
"""
Class that continuously shows a frame using a dedicated thread.
"""
def __init__(self, imgWidth, imgHeight, cameraServer, frame=None, name='stream'):
self.outputStream = cameraServer.putVideo(name, imgWidth, imgHeight)
self.frame = frame
self.stopped = False
def start(self):
Thread(target=self.show, args=()).start()
return self
def show(self):
while not self.stopped:
self.outputStream.putFrame(self.frame)
def stop(self):
self.stopped = True
def notifyError(self, error):
self.outputStream.notifyError(error)
class WebcamVideoStream:
def __init__(self, camera, cameraServer, frameWidth, frameHeight, name="WebcamVideoStream"):
# initialize the video camera stream and read the first frame
# from the stream
#Automatically sets exposure to 0 to track tape
self.webcam = camera
self.webcam.setExposureManual(0)
#Some booleans so that we don't keep setting exposure over and over to the same value
self.autoExpose = False
self.prevValue = self.autoExpose
#Make a blank image to write on
self.img = np.zeros(shape=(frameWidth, frameHeight, 3), dtype=np.uint8)
#Gets the video
self.stream = cameraServer.getVideo(camera = camera)
(self.timestamp, self.img) = self.stream.grabFrame(self.img)
# initialize the thread name
self.name = name
# initialize the variable used to indicate if the thread should
# be stopped
self.stopped = False
def start(self):
# start the thread to read frames from the video stream
t = Thread(target=self.update, name=self.name, args=())
t.daemon = True
t.start()
return self
def update(self):
# keep looping infinitely until the thread is stopped
while True:
# if the thread indicator variable is set, stop the thread
if self.stopped:
return
#Boolean logic we don't keep setting exposure over and over to the same value
if self.autoExpose:
self.webcam.setExposureAuto()
else:
self.webcam.setExposureManual(0)
#gets the image and timestamp from cameraserver
(self.timestamp, self.img) = self.stream.grabFrame(self.img)
def read(self):
# return the frame most recently read
return self.timestamp, self.img
def stop(self):
# indicate that the thread should be stopped
self.stopped = True
def getError(self):
return self.stream.getError()
###################### PROCESSING OPENCV ################################
#Angles in radians
#image size ratioed to 16:9
image_width = 256
image_height = 144
#Lifecam 3000 from datasheet
#Datasheet: https://dl2jx7zfbtwvr.cloudfront.net/specsheets/WEBC1010.pdf
diagonalView = math.radians(68.5)
#16:9 aspect ratio
horizontalAspect = 16
verticalAspect = 9
#Reasons for using diagonal aspect is to calculate horizontal field of view.
diagonalAspect = math.hypot(horizontalAspect, verticalAspect)
#Calculations: http://vrguy.blogspot.com/2013/04/converting-diagonal-field-of-view-and.html
horizontalView = math.atan(math.tan(diagonalView/2) * (horizontalAspect / diagonalAspect)) * 2
verticalView = math.atan(math.tan(diagonalView/2) * (verticalAspect / diagonalAspect)) * 2
#Focal Length calculations: https://docs.google.com/presentation/d/1ediRsI-oR3-kwawFJZ34_ZTlQS2SDBLjZasjzZ-eXbQ/pub?start=false&loop=false&slide=id.g12c083cffa_0_165
H_FOCAL_LENGTH = image_width / (2*math.tan((horizontalView/2)))
V_FOCAL_LENGTH = image_height / (2*math.tan((verticalView/2)))
#blurs have to be odd
green_blur = 7
orange_blur = 27
# define range of green of retroreflective tape in HSV
lower_green = np.array([0,220,25])
upper_green = np.array([101, 255, 255])
#define range of orange from cargo ball in HSV
lower_orange = np.array([0,193,92])
upper_orange = np.array([23, 255, 255])
#Flip image if camera mounted upside down
def flipImage(frame):
return cv2.flip( frame, -1 )
#Blurs frame
def blurImg(frame, blur_radius):
img = frame.copy()
blur = cv2.blur(img,(blur_radius,blur_radius))
return blur
# Masks the video based on a range of hsv colors
# Takes in a frame, range of color, and a blurred frame, returns a masked frame
def threshold_video(lower_color, upper_color, blur):
# Convert BGR to HSV
hsv = cv2.cvtColor(blur, cv2.COLOR_BGR2HSV)
# hold the HSV image to get only red colors
mask = cv2.inRange(hsv, lower_color, upper_color)
# Returns the masked imageBlurs video to smooth out image
return mask
# Finds the tape targets from the masked image and displays them on original stream + network tales
def findTargets(frame, mask):
# Finds contours
_, contours, _ = cv2.findContours(mask, cv2.RETR_TREE, cv2.CHAIN_APPROX_TC89_KCOS)
# Take each frame
# Gets the shape of video
screenHeight, screenWidth, _ = frame.shape
# Gets center of height and width
centerX = (screenWidth / 2) - .5
centerY = (screenHeight / 2) - .5
# Copies frame and stores it in image
image = frame.copy()
# Processes the contours, takes in (contours, output_image, (centerOfImage)
if len(contours) != 0:
image = findTape(contours, image, centerX, centerY)
else:
# pushes that it deosn't see vision target to network tables
networkTable.putBoolean("tapeDetected", False)
# Shows the contours overlayed on the original video
return image
# Finds the balls from the masked image and displays them on original stream + network tables
def findCargo(frame, mask):
# Finds contours
_, contours, _ = cv2.findContours(mask, cv2.RETR_TREE, cv2.CHAIN_APPROX_TC89_KCOS)
# Take each frame
# Gets the shape of video
screenHeight, screenWidth, _ = frame.shape
# Gets center of height and width
centerX = (screenWidth / 2) - .5
centerY = (screenHeight / 2) - .5
# Copies frame and stores it in image
image = frame.copy()
# Processes the contours, takes in (contours, output_image, (centerOfImage)
if len(contours) != 0:
image = findBall(contours, image, centerX, centerY)
else:
# pushes that it doesn't see cargo to network tables
networkTable.putBoolean("cargoDetected", False)
# Shows the contours overlayed on the original video
return image
# Draws Contours and finds center and yaw of orange ball
# centerX is center x coordinate of image
# centerY is center y coordinate of image
def findBall(contours, image, centerX, centerY):
screenHeight, screenWidth, channels = image.shape;
#Seen vision targets (correct angle, adjacent to each other)
cargo = []
if len(contours) > 0:
#Sort contours by area size (biggest to smallest)
cntsSorted = sorted(contours, key=lambda x: cv2.contourArea(x), reverse=True)
biggestCargo = []
for cnt in cntsSorted:
x, y, w, h = cv2.boundingRect(cnt)
aspect_ratio = float(w) / h
# Get moments of contour; mainly for centroid
M = cv2.moments(cnt)
# Get convex hull (bounding polygon on contour)
hull = cv2.convexHull(cnt)
# Calculate Contour area
cntArea = cv2.contourArea(cnt)
# Filters contours based off of size
if (checkBall(cntArea, aspect_ratio)):
### MOSTLY DRAWING CODE, BUT CALCULATES IMPORTANT INFO ###
# Gets the centeroids of contour
if M["m00"] != 0:
cx = int(M["m10"] / M["m00"])
cy = int(M["m01"] / M["m00"])
else:
cx, cy = 0, 0
if(len(biggestCargo) < 3):
##### DRAWS CONTOUR######
# Gets rotated bounding rectangle of contour
rect = cv2.minAreaRect(cnt)
# Creates box around that rectangle
box = cv2.boxPoints(rect)
# Not exactly sure
box = np.int0(box)
# Draws rotated rectangle
cv2.drawContours(image, [box], 0, (23, 184, 80), 3)
# Draws a vertical white line passing through center of contour
cv2.line(image, (cx, screenHeight), (cx, 0), (255, 255, 255))
# Draws a white circle at center of contour
cv2.circle(image, (cx, cy), 6, (255, 255, 255))
# Draws the contours
cv2.drawContours(image, [cnt], 0, (23, 184, 80), 1)
# Gets the (x, y) and radius of the enclosing circle of contour
(x, y), radius = cv2.minEnclosingCircle(cnt)
# Rounds center of enclosing circle
center = (int(x), int(y))
# Rounds radius of enclosning circle
radius = int(radius)
# Makes bounding rectangle of contour
rx, ry, rw, rh = cv2.boundingRect(cnt)
# Draws countour of bounding rectangle and enclosing circle in green
cv2.rectangle(image, (rx, ry), (rx + rw, ry + rh), (23, 184, 80), 1)
cv2.circle(image, center, radius, (23, 184, 80), 1)
# Appends important info to array
if not biggestCargo:
biggestCargo.append([cx, cy])
elif [cx, cy, cnt] not in biggestCargo:
biggestCargo.append([cx, cy])
# Check if there are cargo seen
if (len(biggestCargo) > 0):
#pushes that it sees cargo to network tables
networkTable.putBoolean("cargoDetected", True)
# Sorts targets based on x coords to break any angle tie
biggestCargo.sort(key=lambda x: math.fabs(x[0]))
closestCargo = min(biggestCargo, key=lambda x: (math.fabs(x[0] - centerX)))
xCoord = closestCargo[0]
finalTarget = calculateYaw(xCoord, centerX, H_FOCAL_LENGTH)
print("Yaw: " + str(finalTarget))
# Puts the yaw on screen
# Draws yaw of target + line where center of target is
cv2.putText(image, "Yaw: " + str(finalTarget), (40, 40), cv2.FONT_HERSHEY_COMPLEX, .6,
(255, 255, 255))
cv2.line(image, (int(xCoord), screenHeight), (int(xCoord), 0), (255, 0, 0), 2)
currentAngleError = finalTarget
#pushes cargo angle to network tables
networkTable.putNumber("cargoYaw", currentAngleError)
else:
#pushes that it doesn't see cargo to network tables
networkTable.putBoolean("cargoDetected", False)
cv2.line(image, (int(centerX), screenHeight), (int(centerX), 0), (255, 255, 255), 2)
return image
# Draws Contours and finds center and yaw of vision targets
# centerX is center x coordinate of image
# centerY is center y coordinate of image
def findTape(contours, image, centerX, centerY):
screenHeight, screenWidth, channels = image.shape;
#Seen vision targets (correct angle, adjacent to each other)
targets = []
if len(contours) >= 2:
#Sort contours by area size (biggest to smallest)
cntsSorted = sorted(contours, key=lambda x: cv2.contourArea(x), reverse=True)
biggestCnts = []
for cnt in cntsSorted:
# Get moments of contour; mainly for centroid
M = cv2.moments(cnt)
# Get convex hull (bounding polygon on contour)
hull = cv2.convexHull(cnt)
# Calculate Contour area
cntArea = cv2.contourArea(cnt)
# calculate area of convex hull
hullArea = cv2.contourArea(hull)
# Filters contours based off of size
if (checkContours(cntArea, hullArea)):
### MOSTLY DRAWING CODE, BUT CALCULATES IMPORTANT INFO ###
# Gets the centeroids of contour
if M["m00"] != 0:
cx = int(M["m10"] / M["m00"])
cy = int(M["m01"] / M["m00"])
else:
cx, cy = 0, 0
if(len(biggestCnts) < 13):
#### CALCULATES ROTATION OF CONTOUR BY FITTING ELLIPSE ##########
rotation = getEllipseRotation(image, cnt)
# Calculates yaw of contour (horizontal position in degrees)
yaw = calculateYaw(cx, centerX, H_FOCAL_LENGTH)
# Calculates yaw of contour (horizontal position in degrees)
pitch = calculatePitch(cy, centerY, V_FOCAL_LENGTH)
##### DRAWS CONTOUR######
# Gets rotated bounding rectangle of contour
rect = cv2.minAreaRect(cnt)
# Creates box around that rectangle
box = cv2.boxPoints(rect)
# Not exactly sure
box = np.int0(box)
# Draws rotated rectangle
cv2.drawContours(image, [box], 0, (23, 184, 80), 3)
# Calculates yaw of contour (horizontal position in degrees)
yaw = calculateYaw(cx, centerX, H_FOCAL_LENGTH)
# Calculates yaw of contour (horizontal position in degrees)
pitch = calculatePitch(cy, centerY, V_FOCAL_LENGTH)
# Draws a vertical white line passing through center of contour
cv2.line(image, (cx, screenHeight), (cx, 0), (255, 255, 255))
# Draws a white circle at center of contour
cv2.circle(image, (cx, cy), 6, (255, 255, 255))
# Draws the contours
cv2.drawContours(image, [cnt], 0, (23, 184, 80), 1)
# Gets the (x, y) and radius of the enclosing circle of contour
(x, y), radius = cv2.minEnclosingCircle(cnt)
# Rounds center of enclosing circle
center = (int(x), int(y))
# Rounds radius of enclosning circle
radius = int(radius)
# Makes bounding rectangle of contour
rx, ry, rw, rh = cv2.boundingRect(cnt)
boundingRect = cv2.boundingRect(cnt)
# Draws countour of bounding rectangle and enclosing circle in green
cv2.rectangle(image, (rx, ry), (rx + rw, ry + rh), (23, 184, 80), 1)
cv2.circle(image, center, radius, (23, 184, 80), 1)
# Appends important info to array
if not biggestCnts:
biggestCnts.append([cx, cy, rotation])
elif [cx, cy, rotation] not in biggestCnts:
biggestCnts.append([cx, cy, rotation])
# Sorts array based on coordinates (leftmost to rightmost) to make sure contours are adjacent
biggestCnts = sorted(biggestCnts, key=lambda x: x[0])
# Target Checking
for i in range(len(biggestCnts) - 1):
#Rotation of two adjacent contours
tilt1 = biggestCnts[i][2]
tilt2 = biggestCnts[i + 1][2]
#x coords of contours
cx1 = biggestCnts[i][0]
cx2 = biggestCnts[i + 1][0]
cy1 = biggestCnts[i][1]
cy2 = biggestCnts[i + 1][1]
# If contour angles are opposite
if (np.sign(tilt1) != np.sign(tilt2)):
centerOfTarget = math.floor((cx1 + cx2) / 2)
#ellipse negative tilt means rotated to right
#Note: if using rotated rect (min area rectangle)
# negative tilt means rotated to left
# If left contour rotation is tilted to the left then skip iteration
if (tilt1 > 0):
if (cx1 < cx2):
continue
# If left contour rotation is tilted to the left then skip iteration
if (tilt2 > 0):
if (cx2 < cx1):
continue
#Angle from center of camera to target (what you should pass into gyro)
yawToTarget = calculateYaw(centerOfTarget, centerX, H_FOCAL_LENGTH)
#Make sure no duplicates, then append
if not targets:
targets.append([centerOfTarget, yawToTarget])
elif [centerOfTarget, yawToTarget] not in targets:
targets.append([centerOfTarget, yawToTarget])
#Check if there are targets seen
if (len(targets) > 0):
# pushes that it sees vision target to network tables
networkTable.putBoolean("tapeDetected", True)
#Sorts targets based on x coords to break any angle tie
targets.sort(key=lambda x: math.fabs(x[0]))
finalTarget = min(targets, key=lambda x: math.fabs(x[1]))
# Puts the yaw on screen
#Draws yaw of target + line where center of target is
cv2.putText(image, "Yaw: " + str(finalTarget[1]), (40, 40), cv2.FONT_HERSHEY_COMPLEX, .6,
(255, 255, 255))
cv2.line(image, (finalTarget[0], screenHeight), (finalTarget[0], 0), (255, 0, 0), 2)
currentAngleError = finalTarget[1]
# pushes vision target angle to network tables
networkTable.putNumber("tapeYaw", currentAngleError)
else:
# pushes that it deosn't see vision target to network tables
networkTable.putBoolean("tapeDetected", False)
cv2.line(image, (round(centerX), screenHeight), (round(centerX), 0), (255, 255, 255), 2)
return image
# Checks if tape contours are worthy based off of contour area and (not currently) hull area
def checkContours(cntSize, hullSize):
return cntSize > (image_width / 6)
# Checks if ball contours are worthy based off of contour area and (not currently) hull area
def checkBall(cntSize, cntAspectRatio):
return (cntSize > (image_width / 2)) and (round(cntAspectRatio) == 1)
#Forgot how exactly it works, but it works!
def translateRotation(rotation, width, height):
if (width < height):
rotation = -1 * (rotation - 90)
if (rotation > 90):
rotation = -1 * (rotation - 180)
rotation *= -1
return round(rotation)
def calculateDistance(heightOfCamera, heightOfTarget, pitch):
heightOfTargetFromCamera = heightOfTarget - heightOfCamera
# Uses trig and pitch to find distance to target
'''
d = distance
h = height between camera and target
a = angle = pitch
tan a = h/d (opposite over adjacent)
d = h / tan a
.
/|
/ |
/ |h
/a |
camera -----
d
'''
distance = math.fabs(heightOfTargetFromCamera / math.tan(math.radians(pitch)))
return distance
# Uses trig and focal length of camera to find yaw.
# Link to further explanation: https://docs.google.com/presentation/d/1ediRsI-oR3-kwawFJZ34_ZTlQS2SDBLjZasjzZ-eXbQ/pub?start=false&loop=false&slide=id.g12c083cffa_0_298
def calculateYaw(pixelX, centerX, hFocalLength):
yaw = math.degrees(math.atan((pixelX - centerX) / hFocalLength))
return round(yaw)
# Link to further explanation: https://docs.google.com/presentation/d/1ediRsI-oR3-kwawFJZ34_ZTlQS2SDBLjZasjzZ-eXbQ/pub?start=false&loop=false&slide=id.g12c083cffa_0_298
def calculatePitch(pixelY, centerY, vFocalLength):
pitch = math.degrees(math.atan((pixelY - centerY) / vFocalLength))
# Just stopped working have to do this:
pitch *= -1
return round(pitch)
def getEllipseRotation(image, cnt):
try:
# Gets rotated bounding ellipse of contour
ellipse = cv2.fitEllipse(cnt)
centerE = ellipse[0]
# Gets rotation of ellipse; same as rotation of contour
rotation = ellipse[2]
# Gets width and height of rotated ellipse
widthE = ellipse[1][0]
heightE = ellipse[1][1]
# Maps rotation to (-90 to 90). Makes it easier to tell direction of slant
rotation = translateRotation(rotation, widthE, heightE)
cv2.ellipse(image, ellipse, (23, 184, 80), 3)
return rotation
except:
# Gets rotated bounding rectangle of contour
rect = cv2.minAreaRect(cnt)
# Creates box around that rectangle
box = cv2.boxPoints(rect)
# Not exactly sure
box = np.int0(box)
# Gets center of rotated rectangle
center = rect[0]
# Gets rotation of rectangle; same as rotation of contour
rotation = rect[2]
# Gets width and height of rotated rectangle
width = rect[1][0]
height = rect[1][1]
# Maps rotation to (-90 to 90). Makes it easier to tell direction of slant
rotation = translateRotation(rotation, width, height)
return rotation
#################### FRC VISION PI Image Specific #############
configFile = "/boot/frc.json"
class CameraConfig: pass
team = None
server = False
cameraConfigs = []
"""Report parse error."""
def parseError(str):
print("config error in '" + configFile + "': " + str, file=sys.stderr)
"""Read single camera configuration."""
def readCameraConfig(config):
cam = CameraConfig()
# name
try:
cam.name = config["name"]
except KeyError:
parseError("could not read camera name")
return False
# path
try:
cam.path = config["path"]
except KeyError:
parseError("camera '{}': could not read path".format(cam.name))
return False
cam.config = config
cameraConfigs.append(cam)
return True
"""Read configuration file."""
def readConfig():
global team
global server
# parse file
try:
with open(configFile, "rt") as f:
j = json.load(f)
except OSError as err:
print("could not open '{}': {}".format(configFile, err), file=sys.stderr)
return False
# top level must be an object
if not isinstance(j, dict):
parseError("must be JSON object")
return False
# team number
try:
team = j["team"]
except KeyError:
parseError("could not read team number")
return False
# ntmode (optional)
if "ntmode" in j:
str = j["ntmode"]
if str.lower() == "client":
server = False
elif str.lower() == "server":
server = True
else:
parseError("could not understand ntmode value '{}'".format(str))
# cameras
try:
cameras = j["cameras"]
except KeyError:
parseError("could not read cameras")
return False
for camera in cameras:
if not readCameraConfig(camera):
return False
return True
"""Start running the camera."""
def startCamera(config):
print("Starting camera '{}' on {}".format(config.name, config.path))
cs = CameraServer.getInstance()
camera = cs.startAutomaticCapture(name=config.name, path=config.path)
camera.setConfigJson(json.dumps(config.config))
return cs, camera
if __name__ == "__main__":
if len(sys.argv) >= 2:
configFile = sys.argv[1]
# read configuration
if not readConfig():
sys.exit(1)
# start NetworkTables
ntinst = NetworkTablesInstance.getDefault()
#Name of network table - this is how it communicates with robot. IMPORTANT
networkTable = NetworkTables.getTable('ChickenVision')
if server:
print("Setting up NetworkTables server")
ntinst.startServer()
else:
print("Setting up NetworkTables client for team {}".format(team))
ntinst.startClientTeam(team)
# start cameras
cameras = []
streams = []
for cameraConfig in cameraConfigs:
cs, cameraCapture = startCamera(cameraConfig)
streams.append(cs)
cameras.append(cameraCapture)
#Get the first camera
webcam = cameras[0]
cameraServer = streams[0]
#Start thread reading camera
cap = WebcamVideoStream(webcam, cameraServer, image_width, image_height).start()
# (optional) Setup a CvSource. This will send images back to the Dashboard
# Allocating new images is very expensive, always try to preallocate
img = np.zeros(shape=(image_height, image_width, 3), dtype=np.uint8)
#Start thread outputing stream
streamViewer = VideoShow(image_width,image_height, cameraServer, frame=img, name="ChickenVision").start()
#cap.autoExpose=True;
tape = False
fps = FPS().start()
#TOTAL_FRAMES = 200;
# loop forever
while True:
# Tell the CvSink to grab a frame from the camera and put it
# in the source image. If there is an error notify the output.
timestamp, img = cap.read()
#Uncomment if camera is mounted upside down
frame = flipImage(img)
#Comment out if camera is mounted upside down
#frame = img
if timestamp == 0:
# Send the output the error.
streamViewer.notifyError(cap.getError());
# skip the rest of the current iteration
continue
#Checks if you just want camera for driver (No processing), False by default
if(networkTable.getBoolean("Driver", False)):
cap.autoExpose = True
processed = frame
else:
# Checks if you just want camera for Tape processing , False by default
if(networkTable.getBoolean("Tape", True)):
#Lowers exposure to 0
cap.autoExpose = False
boxBlur = blurImg(frame, green_blur)
threshold = threshold_video(lower_green, upper_green, boxBlur)
processed = findTargets(frame, threshold)
else:
# Checks if you just want camera for Cargo processing, by dent of everything else being false, true by default
cap.autoExpose = True
boxBlur = blurImg(frame, orange_blur)
threshold = threshold_video(lower_orange, upper_orange, boxBlur)
processed = findCargo(frame, threshold)
#Puts timestamp of camera on netowrk tables
networkTable.putNumber("VideoTimestamp", timestamp)
streamViewer.frame = processed
# update the FPS counter
fps.update()
#Flushes camera values to reduce latency
ntinst.flush()
#Doesn't do anything at the moment. You can easily get this working by indenting these three lines
# and setting while loop to: while fps._numFrames < TOTAL_FRAMES
fps.stop()
print("[INFO] elasped time: {:.2f}".format(fps.elapsed()))
print("[INFO] approx. FPS: {:.2f}".format(fps.fps()))
