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my videowriter in opencv is not saving any output

Using openCV in python to tag an object in a video and write that new output and save as .avi file. Everything is working fine except for the vide writer. I can not get it to write and/or save the output. It looks the same as I've done before so this is driving me insane. Any ideas on what I might be doing wrong are greatly appreciated.
import numpy as np
import cv2
def draw_pyramid(pyramid, H):
pyramid_new = []
i=0
for p in pyramid:
if i !=4:
temp_p = p
temp_p = np.append(temp_p, 1)
M_ext = H[0:3, :]
p = M_ext # temp_p
pyramid_new.append((p[0], p[1], p[2]))
i += 1
else:
pyramid_new.append((p[0], p[1], p[2]))
i +=1
return pyramid_new
cap = cv2.VideoCapture('hw4.avi')
got_image, img = cap.read()
videoWriter = cv2.VideoWriter("switch.avi",
fourcc=cv2.VideoWriter_fourcc('M','J','P','G'),
fps=30.0,
frameSize=(img.shape[1], img.shape[0]))
fx, fy, cx, cy = 675.0, 675.0, 320.0, 240.0
k = np.array([[fx,0.0,cx],[0.0,fy,cy],[0.0,0.0,1.0]])
while True:
got_img, img = cap.read()
if not got_img:
break
else:
gray_image = cv2.cvtColor(src=img,
code=cv2.COLOR_BGR2GRAY) # convert BGR to grayscale
output_thresh, binary_image = cv2.threshold(src=gray_image,
maxval=255,
type=cv2.THRESH_OTSU, # determine threshold automatically from image
thresh=0) # ignore this if using THRESH_OTSU
arucoDict = cv2.aruco.getPredefinedDictionary(cv2.aruco.DICT_4X4_100)
corners, ids, _ = cv2.aruco.detectMarkers(image=img,
dictionary=arucoDict)
if ids is not None and corners:
cv2.aruco.drawDetectedMarkers(image=img,
corners=corners,
ids=ids,
borderColor=(0, 255, 0))
rvecs, tvecs, _ = cv2.aruco.estimatePoseSingleMarkers(corners=corners,
markerLength=2.0,
cameraMatrix=k,
distCoeffs=None)
cv2.aruco.drawAxis(image=img,
cameraMatrix=k,
distCoeffs=None,
rvec=rvecs,
tvec=tvecs,
length=2.0)
if ids[0][0]==1:
pyr = np.array(((-3.5/3,-1.5/3,-1.5/3,-3.5/3,-2.5),
(-1, -1, -1/3, -1/3, -2),
(-4/3, -4/3, -4/3, -4/3, -5))).T
R = cv2.Rodrigues(rvecs)[0]
r = R[0]
# rvec_m_c = rvecs[0] # This is a 1x3 rotation vector
tm_c = np.array(tvecs[0]).T # This is a 1x3 translation vector
t = np.array([[-2.5, -2, -5]]).T
H = np.block([[R, t], [0, 0, 0, 1]])
if ids[0][0] == 0:
pyr = np.array(((1.5/3, 3.5/3, 3.5/3, 1.5/3, 2.5),
(-1,-1,-1/3, -1/3, -2),
(-4/3, -4/3, -4/3, -4/3, -1))).T
R = cv2.Rodrigues(rvecs)[0]
# r = R[0]
tm_c = np.array(tvecs[0]).T # This is a 1x3 translation vector
t = np.array([[2.5, -2, -1]]).T
H = np.block([[R, t], [0,0,0,1]])
pyramid_new = draw_pyramid(pyr, H)
pyramid_new = np.asarray(pyramid_new)
pyramid_new = pyramid_new.reshape(-1, 3)
dist_coeff = np.zeros((4,1))
pyramid_points, jacobian = cv2.projectPoints(pyramid_new,
rvecs,
tvecs,
np.array(k),
distCoeffs=dist_coeff)
pyramid_points = pyramid_points.astype(int)
froml = [1,2,3,0,0,1,2,3]
fromr = [4,4,4,4,1,2,3,0]
color = (0,0,255)
for i, j in zip(froml, fromr):
cv2.line(img, tuple(pyramid_points[i][0]),
tuple(pyramid_points[j][0]), color, 1)
videoWriter.write(img)
cv2.imshow('switch', img)
cv2.waitKey(30)
videoWriter.release()

Save Video from Opencv

I create space detection code by using gray, gausian blur but now I dont know where to put these code to save my opencv video.
I already tried to put the code in random line but it only comes out of the file at the output, I cant play it and the video also just 5.6KB. I even tried to record a video for a very long time.
My code runs fine without save feature but I want to add save video feature:
fourcc = open_cv.VideoWriter_fourcc(*'DIVX')
out = open_cv.VideoWriter('output.avi',fourcc, 20.0, (640,480))
these is my code that i want to be add save video coding from above :
import cv2 as open_cv
import numpy as np
import logging
from drawing_utils import draw_contours
from colors import COLOR_GREEN, COLOR_WHITE, COLOR_BLUE
class MotionDetector:
LAPLACIAN = 1.4
DETECT_DELAY = 1
def __init__(self, video, coordinates, start_frame):
self.video = 0
self.coordinates_data = coordinates
self.start_frame = start_frame
self.contours = []
self.bounds = []
self.mask = []
def detect_motion(self):
capture = open_cv.VideoCapture(self.video)
capture.set(open_cv.CAP_PROP_POS_FRAMES, self.start_frame)
coordinates_data = self.coordinates_data
logging.debug("coordinates data: %s", coordinates_data)
for p in coordinates_data:
coordinates = self._coordinates(p)
logging.debug("coordinates: %s", coordinates)
rect = open_cv.boundingRect(coordinates)
logging.debug("rect: %s", rect)
new_coordinates = coordinates.copy()
new_coordinates[:, 0] = coordinates[:, 0] - rect[0]
new_coordinates[:, 1] = coordinates[:, 1] - rect[1]
logging.debug("new_coordinates: %s", new_coordinates)
self.contours.append(coordinates)
self.bounds.append(rect)
mask = open_cv.drawContours(
np.zeros((rect[3], rect[2]), dtype=np.uint8),
[new_coordinates],
contourIdx=-1,
color=255,
thickness=-1,
lineType=open_cv.LINE_8)
mask = mask == 255
self.mask.append(mask)
logging.debug("mask: %s", self.mask)
statuses = [False] * len(coordinates_data)
times = [None] * len(coordinates_data)
while capture.isOpened():
result, frame = capture.read()
if frame is None:
break
if not result:
raise CaptureReadError("Error reading video capture on frame %s" % str(frame))
blurred = open_cv.GaussianBlur(frame.copy(), (5, 5), 3)
grayed = open_cv.cvtColor(blurred, open_cv.COLOR_BGR2GRAY)
new_frame = frame.copy()
logging.debug("new_frame: %s", new_frame)
position_in_seconds = capture.get(open_cv.CAP_PROP_POS_MSEC) / 1000.0
for index, c in enumerate(coordinates_data):
status = self.__apply(grayed, index, c)
if times[index] is not None and self.same_status(statuses, index, status):
times[index] = None
continue
if times[index] is not None and self.status_changed(statuses, index, status):
if position_in_seconds - times[index] >= MotionDetector.DETECT_DELAY:
statuses[index] = status
times[index] = None
continue
if times[index] is None and self.status_changed(statuses, index, status):
times[index] = position_in_seconds
for index, p in enumerate(coordinates_data):
coordinates = self._coordinates(p)
color = COLOR_GREEN if statuses[index] else COLOR_BLUE
draw_contours(new_frame, coordinates, str(p["id"] + 1), COLOR_WHITE, color)
open_cv.imshow(str(self.video), new_frame)
k = open_cv.waitKey(1)
if k == ord("q"):
break
capture.release()
open_cv.destroyAllWindows()
def __apply(self, grayed, index, p):
coordinates = self._coordinates(p)
logging.debug("points: %s", coordinates)
rect = self.bounds[index]
logging.debug("rect: %s", rect)
roi_gray = grayed[rect[1]:(rect[1] + rect[3]), rect[0]:(rect[0] + rect[2])]
laplacian = open_cv.Laplacian(roi_gray, open_cv.CV_64F)
logging.debug("laplacian: %s", laplacian)
coordinates[:, 0] = coordinates[:, 0] - rect[0]
coordinates[:, 1] = coordinates[:, 1] - rect[1]
status = np.mean(np.abs(laplacian * self.mask[index])) < MotionDetector.LAPLACIAN
logging.debug("status: %s", status)
return status
#staticmethod
def _coordinates(p):
return np.array(p["coordinates"])
#staticmethod
def same_status(coordinates_status, index, status):
return status == coordinates_status[index]
#staticmethod
def status_changed(coordinates_status, index, status):
return status != coordinates_status[index]
class CaptureReadError(Exception):
pass

Create the file for the video to go in. You can think of this file like a book, but a book with no pages. This code is used to create the file:
fourcc = open_cv.VideoWriter_fourcc(*'DIVX')
out = open_cv.VideoWriter('output.avi',fourcc, 20.0, (640,480))
Not all codecs work on all systems. I use Ubuntu, and this is the code I use to create a video file:
out = cv2.VideoWriter('output.avi',cv2.VideoWriter_fourcc('M','J','P','G'), 30,(w,h))
If you try to play this video, nothing will happen. There are no frames in the video, so there is nothing to play (like a book with no pages). After you process each frame, the frame needs to be written to the video (like putting a page in a book):
out.write(new_frame)
You should do this around .imshow():
out.write(new_frame)
open_cv.imshow(str(self.video), new_frame)
k = open_cv.waitKey(1)
if k == ord("q"):
break

pool map no performance gain

I'm doing video capture from opencv using the following
frame_count = 90000
while f < frame_count:
ret, frame = cap.read()
f+=1
if (f > 41300):
dst = cv2.warpPerspective(frame.copy(), matrix, (frame.shape[1], frame.shape[0]))
pipeline(pool_1, pool_2, frame, car_tracker, ped_tracker, df_region, region_buffered, df_line_car, df_line_ped, det, dst, matrix)
cv2.imshow("frame", frame)
cv2.imshow('dst', dst)
cv2.waitKey(1)
In function pipeline:
df_cars= pool_1.map(compute, cars)
df_peds = pool_2.map(compute, peds)
compute is:
def compute(v):
gb = ('trajectory_id',)
global general_pd
id = v[0]
x = v[1]
y = v[2]
frame_id = v[3]
general_pd.loc[len(general_pd)] = [id, x,y, frame_id]
grouped = general_pd.loc[general_pd['trajectory_id'] == id]
df_region = v[4]
region_buffered = v[5]
df_line = v[6]
rpp = RawParameterProcessor(grouped, df_line, frame_idx, df_region, region_buffered, gb=gb)
df_parameter_car = rpp.compute()
return df_parameter_car
I don't know what I'm doing wrong, so that I can't get the correct performance gain, I launch two process, and each frame of the video capture I launch a process and do the jobs async, but I don't get any performance gain.

Calculating the distance and yaw between ArUco marker and camera?

I am trying to calculate the exact( 3 cm error rate is acceptable ) distance between aruco marker and camera. I use python, opencv and aruco. I can detect them ( marker side is 0.023 meters which is 2.3 cm ) but I can't interpret the distance because for 40 cm distance the norm of the translation vector gives 1 meter. I am so confused about this. Can anyone help? Full code ( sorry , not documented well ):
import numpy as np
import cv2
import cv2.aruco as aruco
import glob
import argparse
import math
# Marker id infos. Global to access everywhere. It is unnecessary to change it to local.
firstMarkerID = None
secondMarkerID = None
cap = cv2.VideoCapture(0)
image_width = 0
image_height = 0
#hyper parameters
distanceBetweenTwoMarkers = 0.0245 # in meters, 2.45 cm
oneSideOfTheMarker = 0.023 # in meters, 2.3 cm
# termination criteria
criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 30, 0.001)
class Namespace:
def __init__(self, **kwargs):
self.__dict__.update(kwargs)
def calibrate(dirpath):
""" Apply camera calibration operation for images in the given directory path. """
# prepare object points, like (0,0,0), (1,0,0), (2,0,0) ....,(8,6,0)
objp = np.zeros((6*9, 3), np.float32)
objp[:, :2] = np.mgrid[0:9, 0:6].T.reshape(-1, 2)
# Arrays to store object points and image points from all the images.
objpoints = [] # 3d point in real world space
imgpoints = [] # 2d points in image plane.
images = glob.glob(dirpath+'/*.jpg')
for fname in images:
img = cv2.imread(fname)
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
# Find the chess board corners
ret, corners = cv2.findChessboardCorners(gray, (9, 6), None)
# If found, add object points, image points (after refining them)
if ret:
objpoints.append(objp)
corners2 = cv2.cornerSubPix(gray, corners, (11, 11), (-1, -1), criteria)
imgpoints.append(corners2)
# Draw and display the corners
img = cv2.drawChessboardCorners(img, (9, 6), corners2, ret)
ret, mtx, dist, rvecs, tvecs = cv2.calibrateCamera(objpoints, imgpoints, gray.shape[::-1], None, None)
return [ret, mtx, dist, rvecs, tvecs]
def saveCoefficients(mtx, dist, path):
""" Save the camera matrix and the distortion coefficients to given path/file. """
cv_file = cv2.FileStorage(path, cv2.FILE_STORAGE_WRITE)
cv_file.write("camera_matrix", mtx)
cv_file.write("dist_coeff", dist)
# note you *release* you don't close() a FileStorage object
cv_file.release()
def loadCoefficients(path):
""" Loads camera matrix and distortion coefficients. """
# FILE_STORAGE_READ
cv_file = cv2.FileStorage(path, cv2.FILE_STORAGE_READ)
# note we also have to specify the type to retrieve other wise we only get a
# FileNode object back instead of a matrix
camera_matrix = cv_file.getNode("camera_matrix").mat()
dist_matrix = cv_file.getNode("dist_coeff").mat()
# Debug: print the values
# print("camera_matrix : ", camera_matrix.tolist())
# print("dist_matrix : ", dist_matrix.tolist())
cv_file.release()
return [camera_matrix, dist_matrix]
def inversePerspective(rvec, tvec):
""" Applies perspective transform for given rvec and tvec. """
rvec, tvec = rvec.reshape((3, 1)), tvec.reshape((3, 1))
R, _ = cv2.Rodrigues(rvec)
R = np.matrix(R).T
invTvec = np.dot(R, np.matrix(-tvec))
invRvec, _ = cv2.Rodrigues(R)
invTvec = invTvec.reshape((3, 1))
invTvec = invTvec.reshape((3, 1))
return invRvec, invTvec
def make_1080p():
global image_width
global image_height
image_width = 1920
image_height = 1080
change_res(image_width, image_height)
def make_720p():
global image_width
global image_height
image_width = 1280
image_height = 720
change_res(image_width, image_height)
def make_480p():
global image_width
global image_height
image_width = 640
image_height = 480
change_res(image_width, image_height)
def change_res(width, height):
cap.set(3, width)
cap.set(4, height)
def relativePosition(rvec1, tvec1, rvec2, tvec2):
""" Get relative position for rvec2 & tvec2. Compose the returned rvec & tvec to use composeRT with rvec2 & tvec2 """
rvec1, tvec1 = rvec1.reshape((3, 1)), tvec1.reshape((3, 1))
rvec2, tvec2 = rvec2.reshape((3, 1)), tvec2.reshape((3, 1))
# Inverse the second marker, the right one in the image
invRvec, invTvec = inversePerspective(rvec2, tvec2)
info = cv2.composeRT(rvec1, tvec1, invRvec, invTvec)
composedRvec, composedTvec = info[0], info[1]
composedRvec = composedRvec.reshape((3, 1))
composedTvec = composedTvec.reshape((3, 1))
return composedRvec, composedTvec
def euclideanDistanceOfTvecs(tvec1, tvec2):
return math.sqrt(math.pow(tvec1[0]-tvec2[0], 2) + math.pow(tvec1[1]-tvec2[1], 2) + math.pow(tvec1[2]-tvec2[2], 2))
def euclideanDistanceOfTvec(tvec):
return euclideanDistanceOfTvecs(tvec, [0, 0, 0])
def draw(img, imgpts, color):
""" draw a line between given two points. """
imgpts = np.int32(imgpts).reshape(-1, 2)
for pointf in range(len(imgpts)):
for points in range(len(imgpts)):
img = cv2.line(img, tuple(imgpts[pointf]), tuple(imgpts[points]), color, 3)
return img
def track(matrix_coefficients, distortion_coefficients):
global image_width
global image_height
""" Real time ArUco marker tracking. """
needleComposeRvec, needleComposeTvec = None, None # Composed for needle
ultraSoundComposeRvec, ultraSoundComposeTvec = None, None # Composed for ultrasound
savedNeedleRvec, savedNeedleTvec = None, None # Pure Composed
savedUltraSoundRvec, savedUltraSoundTvec = None, None # Pure Composed
TcomposedRvecNeedle, TcomposedTvecNeedle = None, None
TcomposedRvecUltrasound, TcomposedTvecUltrasound = None, None
make_480p()
while True:
isFirstMarkerDetected = False
isSecondMarkerDetected = False
ret, frame = cap.read()
# operations on the frame come here
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) # Change grayscale
aruco_dict = aruco.Dictionary_get(aruco.DICT_5X5_250) # Use 5x5 dictionary to find markers
parameters = aruco.DetectorParameters_create() # Marker detection parameters
# lists of ids and the corners beloning to each id
corners, ids, rejected_img_points = aruco.detectMarkers(gray, aruco_dict,
parameters=parameters,
cameraMatrix=matrix_coefficients,
distCoeff=distortion_coefficients)
if np.all(ids is not None): # If there are markers found by detector
zipped = zip(ids, corners)
ids, corners = zip(*(sorted(zipped)))
# print(ids)
for i in range(0, len(ids)): # Iterate in markers
# Estimate pose of each marker and return the values rvec and tvec---different from camera coefficients
rvec, tvec, markerPoints = aruco.estimatePoseSingleMarkers(corners[i], oneSideOfTheMarker, matrix_coefficients,
distortion_coefficients)
if ids[i] == firstMarkerID:
firstRvec = rvec
firstTvec = tvec
isFirstMarkerDetected = True
firstMarkerCorners = corners[i]
elif ids[i] == secondMarkerID:
secondRvec = rvec
secondTvec = tvec
isSecondMarkerDetected = True
secondMarkerCorners = corners[i]
(rvec - tvec).any() # get rid of that nasty numpy value array error
# aruco.drawAxis(frame, matrix_coefficients, distortion_coefficients, rvec, tvec, 0.01) # Draw Axis
aruco.drawDetectedMarkers(frame, corners) # Draw A square around the markers
''' First try
if isFirstMarkerDetected and isSecondMarkerDetected:
composedRvec, composedTvec = relativePosition(firstRvec, firstTvec, secondRvec, secondTvec)
info = cv2.composeRT(composedRvec, composedTvec, secondRvec.T, secondTvec.T)
composedRvec, composedTvec = info[0], info[1]
composedRvec, composedTvec = composedRvec.T, composedTvec.T
differenceRvec, differenceTvec = composedRvec-secondRvec, composedTvec-secondTvec
# print infos
print("composed Rvec: ", composedRvec)
print("composed Tvec: ", composedTvec)
print("Second marker Rvec: ", secondRvec)
print("Second marker Tvec: ", secondTvec)
print("differenceRvec: ", differenceRvec)
print("differenceTvec: ", differenceTvec)
print("real difference: ", euclideanDistanceOfTvecs(composedTvec[0], secondTvec[0][0]))
# draw axis to estimated location
aruco.drawAxis(frame, mtx, dist, composedRvec, composedTvec, 0.0115)
realDistanceInTvec = euclideanDistanceOfTvec(secondTvec[0][0])
difference = euclideanDistanceOfTvecs(composedTvec[0], secondTvec[0][0])
calculatedDistance = realDistanceInTvec * (distanceBetweenTwoMarkers / difference)
calculatedDistance = realDistanceInTvec * (distanceBetweenTwoMarkers / (secondTvec[0][0][2] - firstTvec[0][0][2]))
print(calculatedDistance)
'''
if isFirstMarkerDetected and isSecondMarkerDetected:
composedRvec, composedTvec = relativePosition(firstRvec, firstTvec, secondRvec, secondTvec)
camerafirstRvec, cameraFirstTvec = inversePerspective(firstRvec, firstTvec)
camerasecondRvec, camerasecondTvec = inversePerspective(secondRvec, secondTvec)
differenceRvec, differenceTvec = camerafirstRvec - camerasecondRvec, cameraFirstTvec - camerasecondTvec
# print infos
print("first Rvec: ", camerafirstRvec)
print("first Tvec: ", cameraFirstTvec)
print("Second marker Rvec: ", camerasecondRvec)
print("Second marker Tvec: ", camerasecondTvec)
# print("differenceRvec: ", differenceRvec)
# print("differenceTvec: ", differenceTvec)
realDistanceInTvec = euclideanDistanceOfTvec(secondTvec[0][0])
print(cv2.norm(secondTvec[0][0]))
difference = euclideanDistanceOfTvecs(composedTvec.T[0], secondTvec[0][0])
calculatedDistance = realDistanceInTvec * (distanceBetweenTwoMarkers / difference)
# print(calculatedDistance)
# Display the resulting frame
cv2.namedWindow('frame', cv2.WINDOW_NORMAL)
cv2.resizeWindow('frame', image_width, image_height)
cv2.imshow('frame', frame)
# Wait 3 milisecoonds for an interaction. Check the key and do the corresponding job.
key = cv2.waitKey(3) & 0xFF
if key == ord('q'): # Quit
break
elif key == ord('p'): # print necessary information here
pass # Insert necessary print here
# When everything done, release the capture
cap.release()
cv2.destroyAllWindows()
if __name__ == '__main__':
parser = argparse.ArgumentParser(description='Aruco Marker Tracking')
parser.add_argument('--coefficients', metavar='bool', required=True,
help='File name for matrix coefficients and distortion coefficients')
parser.add_argument('--firstMarker', metavar='int', required=True,
help='first')
parser.add_argument('--secondMarker', metavar='int', required=True,
help='second')
# Parse the arguments and take action for that.
args = parser.parse_args()
firstMarkerID = int(args.firstMarker)
secondMarkerID = int(args.secondMarker)
if args.coefficients == '1':
mtx, dist = loadCoefficients("test.yaml")
ret = True
else:
ret, mtx, dist, rvecs, tvecs = calibrate("calib_images")
saveCoefficients(mtx, dist, "calibrationCoefficients.yaml")
print("Calibration is completed. Starting tracking sequence.")
if ret:
track(mtx, dist)

I got the answer. Problem is in the calibration. When calibration with chessboard, I gave the object points like this:
(0,0,0), (1,0,0) and so on.
The thing is when doing pose estimation, camera should be calibrated nicely. My chessboard square size was 1.5 centimeters which means 0.015 meters. I changed the object point matrix as:
(0,0,0), (0.015,0,0) and so on.
So I said to program that the calibration should be in meters. If you do the calibration with different object points matrix than it should be, the pose estimation fails. That was included in the opencv documentation but I couldn't see it. In the docs it was said like "you can pass them like that." and I didn't think that it fails at the pose estimation.

OpenCV: Error (-215) depth == CV_8U || depth == CV_16U|| depth == CV_32F in function cv:::cvtColor

Doing a motion capture program, ran into this beautiful error. It appears that there is a problem with the depth, however nowhere in the code should this be a problem.
in get_x_centroid
grayscale_image = cv2.cvtColor(image,cv2.COLOR_BGR2GRAY)
error: C:\projects\opencv-
python\opencv\modules\imgproc\src\color.cpp:10600: error: (-215) depth
== CV_8U || depth == CV_16U || depth == CV_32F in function cv::cvtColor
I researched this error but no one had a solution for live video feed. Please help, this is due soon. Will attach all of the code below.
import time
import cv2
import numpy as np
cap =cv2.VideoCapture(0)
def on_mouse(event, x, y, flags, params):
if event == cv2.EVENT_LBUTTONDOWN:
# print "Left mouse button clicked at: x =", x, ",y=", y
hsv = hsv_img[y,x]
#print "hue:", hsv_img[0],"saturation:", hsv[1],"value:",hsv[2]
cv2.namedWindow("Orginal")
cv2.namedWindow("Red")
cv2.namedWindow("Blue")
cv2.namedWindow("Green")
cv2.setMouseCallback("Original", on_mouse, param = None)
#Find the actual numbers,, captureimage3 has the clicky
lower_b = [163,180,183]
upper_b = [129,132,255]
lower_g = [109,93,100]
upper_g = [95,98,255]
lower_r = [97,10,24]
upper_r = [180,140,255]
lower_R = [0,70,195]
upper_R = [20,140,255]
lower_y = [122,113,116]
upper_y = [170,65, 255]
lower_b = np.array(lower_b,dtype = "uint8")
upper_b = np.array(upper_b,dtype = "uint8")
lower_g = np.array(lower_g,dtype = "uint8")
upper_g = np.array(upper_g,dtype = "uint8")
lower_r = np.array(lower_r,dtype = "uint8")
upper_r = np.array(upper_r,dtype = "uint8")
lower_R = np.array(lower_R,dtype = "uint8")
upper_R = np.array(upper_R,dtype = "uint8")
lower_y = np.array(lower_y,dtype = "uint8")
upper_y = np.array(upper_y,dtype = "uint8")
kernel = np.ones((0,0), np.uint8)
def show_filtered_image(window_name,lower,upper):
new_mask = cv2.inRange(hsv_img,lower,upper)
filtered_image = cv2.bitwise_or(frame, frame, mask = new_mask)
eroded_image = cv2.erode(filtered_image, kernel, iterations=1)
dilated_image = cv2.dilate(eroded_image, kernel, iterations=1)
cv2.imshow(window_name, dilated_image)
def get_x_centroid(image):
grayscale_image = cv2.cvtColor(image,cv2.COLOR_BGR2GRAY)
moment_values = cv2.moments(grayscale_image)
if moment_values["m00"] !=0:
x_centroid = moment_values ["m10"]/moment_values["m00"]
else:
x_centroid = 1
return x_centroid
def get_y_centroid(image):
grayscale_image = cv2.cvtColor(image,cv2.COLOR_BGR2GRAY)
moment_values = cv2.moments(grayscale_image)
if moment_values["m00"] !=0:
y_centroid = moment_values ["m01"]/moment_values["m00"]
else:
y_centroid = 1
return y_centroid
k = cv2.waitKey(5) & 0xFF
while (k != 27):
ret, frame = cap.read()
hsv_img = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)
cv2.imshow("Original", frame)
show_filtered_image("Blue", lower_b, upper_b)
show_filtered_image("Green", lower_g, upper_g)
show_filtered_image("Yellow", lower_y, upper_y)
blue_mask = cv2.inRange(hsv_img, lower_b, upper_b)
filtered_b = cv2.bitwise_or(frame, frame, mask = blue_mask)
green_mask = cv2.inRange(hsv_img, lower_g, upper_g)
filtered_g = cv2.bitwise_or(frame, frame, mask = green_mask)
yellow_mask = cv2.inRange(hsv_img, lower_y, upper_y)
filtered_y = cv2.bitwise_or(frame, frame, mask = yellow_mask)
#filtered_combo_red = cv2.bitwise_or(filtered_r, filtered_R)
#cv2.imshow("Red", filtered_combo_red)
#red_x = get_x_centroid(filtered_combo_red)
#red_y = get_y_centroid(filtered_combo_red)
blue_img = get_x_centroid(filtered_b)
blue_img = get_y_centroid(filtered_b)
green_img = get_x_centroid(filtered_g)
green_img = get_y_centroid(filtered_g)
yellow_img = get_x_centroid(filtered_y)
yellow_img = get_y_centroid(filtered_y)
#print "Red X=", red_x," Red Y=", red_y
#red_x = int(get_x_centroid(filtered_combo_red))
#red_y = int(get_y_centroid(filtered_combo_red))
blue_x = int(get_x_centroid(blue_img))
blue_y = int(get_y_centroid(blue_img))
green_x = int(get_x_centroid(green_img))
green_y = int(get_y_centroid(green_img))
yellow_x = int(get_x_centroid(yellow_img))
yellow_y = int(get_y_centroid(yellow_img))
cv2.line(frame,(red_x, red_y),(green_x, green_y), (255,255,255), 3)
cv2.line(frame,(blue_x, blue_y),(yellow_x, yellow_y), (255,255,255), 3)
key_pressed = cv2.waitKey(30)
if key_pressed == 27:
break

The error message implies that cv2.cvtColor expects an image with a (color) depth of 8 or 16 bit unsigned int (8U, 16U)
or 32 bit float (32F).
image needs to be casted or created as cv2.cvtColor expects, for example uint8. CV_8U is an alias for the data type uint8
A conversion is needed. Try:
import numpy as np
image1copy = np.uint8(image)
grayscale_image = cv2.cvtColor(image1copy, cv2.COLOR_HSV2BGR)
BTW:
OpenCV 2.4.13.6 documentation » OpenCV API Reference » core. The Core Functionality »
bitwise_or
Calculates the per-element bit-wise disjunction of two arrays or an array and a scalar.
C++: void bitwise_or(InputArray src1, InputArray src2, OutputArray dst, InputArray mask=noArray())
and
Python: cv2.bitwise_or(src1, src2[, dst[, mask]]) → dst