Develop Reference

Only detect contours within a square

Hello I have a code which draws me the outlines of shapes/objects which have 3 or 4 vertices. So far everything works. But now I want that only shapes/objects are recognized, which are in a square. I wanted to solve it in such a way that I create a square or a picture, in which a square is represented, with cv2.imread read and then with cv2.matchShapes check, how much the recognized contour agrees with the contour of the square. And if this match is good enough, corresponding objects/shapes within this square should be recognized.
Following is the code I have written so far:
import math
import cv2
import numpy as np
from graphics import *
cam = cv2.VideoCapture(0)
cam.set(cv2.CAP_PROP_FRAME_WIDTH, 1920)
cam.set(cv2.CAP_PROP_FRAME_HEIGHT, 1080)
rect = cv2.imread("Rectangle.png")
gray = cv2.cvtColor(rect, cv2.COLOR_BGR2GRAY)
rectcontour, _ = cv2.findContours(gray, cv2.RETR_TREE, cv2.CHAIN_APPROX_NONE)
while True:
_ , img = cam.read()
img = cv2.resize(img, None, fx = 0.5, fy = 0.5, interpolation=cv2.INTER_AREA)
gray1 = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
#edges = cv2.Canny(gray1, 250, 300)
_ , binary1 = cv2.threshold(gray1, 130, 255, cv2.THRESH_BINARY)
inverted_binary1 = ~binary1
contours1, _ = cv2.findContours(inverted_binary1, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
a = 0
for a in contours1:
x = cv2.matchShapes(contours1[a], rectcontour[0], cv2.CONTOURS_MATCH_I1, 0.0)
print(x)
a = a + 1
for contour in contours1:
area = cv2.contourArea(contour)
if area > 100 and area < 20000:
approx = cv2.approxPolyDP(contour, 0.06 * cv2.arcLength(contour, True), True)
print(len(approx))
if 2 < len(approx) < 5:
moment = cv2.moments(contour)
cx = int(moment['m10'] / moment['m00'])
cy = int(moment['m01'] / moment['m00'])
#print(x, y)
print(cx, cy)
cv2.drawContours(img, [contour], -1, (0, 0, 255), 3)
n = approx.ravel()
print(n)
i = 0
for j in n:
if (i % 2 == 0):
x = n[i]
y = n[i + 1]
# String containing the co-ordinates.
string = str(x) + " " + str(y)
if (i == 0):
# text on topmost co-ordinate.
cv2.putText(img, string, (x, y),cv2.FONT_HERSHEY_COMPLEX_SMALL, 1, (255, 0, 0))
else:
# text on remaining co-ordinates.
cv2.putText(img, string, (x, y),cv2.FONT_HERSHEY_COMPLEX_SMALL, 1, (0, 255, 0))
i = i + 1
cv2.imshow("schwarz, weiß", inverted_binary1)
cv2.imshow('my webcam', img)
if cv2.waitKey(1) & 0xFF == ord("q"):
break
At the moment I get following error:
Traceback (most recent call last):
File "C:\Users\Paul\PycharmProjects\Opencv\test3.py", line 27, in <module>
x = cv2.matchShapes(contours1[a], rectcontour[0], cv2.CONTOURS_MATCH_I1, 0.0)
TypeError: only integer scalar arrays can be converted to a scalar index
[ WARN:0] global D:\a\opencv-python\opencv-python\opencv\modules\videoio\src\cap_msmf.cpp (438) `anonymous-namespace'::SourceReaderCB::~SourceReaderCB terminating async callback
And I dont exactly know how I can fix that.
The square image I am using:
And another Picture of the shape I actually want to detect:

Capture icv2.imshow and save it as an image

I'm trying to save the output imgCanny as an image/screenshot. How do I incorporate this so a screenshot of that window can be captured when a certain key is pressed. I am taking the live feed from the webcam and processing it. I would then like to be able to press a key on the keyboard to capture and save a screenshot of the imgCanny Window.
cap = cv2.VideoCapture(1)
cv2.waitKey(0)
cap.set(3, frameWidth)
cap.set(4, frameHeight)
def empty(a):
pass
cv2.namedWindow("Parameters")
cv2.resizeWindow("Parameters", 640, 240)
cv2.createTrackbar("Threshold1", "Parameters", 150, 500, empty)
cv2.createTrackbar("Threshold2", "Parameters", 255, 500, empty)
def getContours(img, imgContour):
contours, hierarchy = cv2.findContours(img, cv2.RETR_EXTERNAL,
cv2.CHAIN_APPROX_NONE) #retreival method. External, only extreme
outer contours
#change APPROX to simple for less points
#remove small bits of noise
for cnt in contours:
area = cv2.contourArea(cnt)
# Used to flatted the array containing
# the co-ordinates of the vertices.
approx = cv2.approxPolyDP(cnt, 0.009 * cv2.arcLength(cnt, True),
True)
n = approx.ravel()
i = 0
for j in n:
if (i % 2 == 0):
x = n[i]
y = n[i + 1]
# String containing the co-ordinates.
string = str(int(x / 3.5)) + " " + str(int(y / 3.5))
print(string)
if (i == 0):
# text on topmost co-ordinate.
cv2.putText(imgContour, "", (x, y),
font, 0.5, (255, 0, 0))
else:
# text on remaining co-ordinates.
cv2.putText(imgContour, string, (x, y),
font, 0.5, (0, 255, 0))
i = i + 1
if area > 3000:
cv2.drawContours(imgContour, cnt, -1, (255, 0, 255), 1)
peri = cv2.arcLength(cnt, True) #true means contour is closed
while True:
success, img = cap.read()
imgContour = img.copy()
imgBlur = cv2.GaussianBlur(img, (31, 31), 1)
imgGray = cv2.cvtColor(imgBlur, cv2.COLOR_BGR2GRAY)
threshold1 = cv2.getTrackbarPos("Threshold1", "Parameters")
threshold2 = cv2.getTrackbarPos("Threshold2", "Parameters")
imgCanny = cv2.Canny(imgGray, threshold1, threshold2)
kernel = np.ones((5, 5))
imgDil = cv2.dilate(imgCanny, kernel, iterations=1)
getContours(imgDil, imgContour)
#cv2.imshow("Results", img )
#cv2.imshow("Mask", imgGray)
cv2.imshow("Canny", imgCanny)
cv2.imshow("Dilated", imgContour)
cv2.imshow("Test", imgDil)
key = cv2.waitKey(100)
if key == 27: #kills with Esc
break
cap.release()
cv2.destroyAllWindows()

How to solve this error: (-215:Assertion failed) npoints >= 0 && (depth == CV_32F || depth == CV_32S) in function 'cv::contourArea'

I am trying to build a college project Sign Language translator and the code for detecting the gestures is below.
This code to create data set for hand signs, whenever i run this code I get this error:
this is my first time asking a question so idk proper way of asking a question.
This the error i am getting:
Traceback (most recent call last):
File "create_gesture_data.py", line 74, in <module>
hand = segment_hand(gray_frame)
File "create_gesture_data.py", line 40, in segment_hand
hand_segment_max_cont = max(contours, key=cv2.contourArea(cv2.UMat()))
cv2.error: OpenCV(4.4.0) C:\Users\appveyor\AppData\Local\Temp\1\pip-req-build-zsozjuva\openc
\modules\imgproc\src\shapedescr.cpp:315: error: (-215:Assertion failed) npoints >= 0 &&
(depth==CV_32F||depth == CV_32S) in function 'cv::contourArea'
[ WARN:0] global C:\Users\appveyor\AppData\Local\Temp\1\pip-req-build-zsozjuva\opencv\module\video
\src\cap_msmf.cpp (435) `anonymous-namespace'::SourceReaderCB::~SourceReaderCB terminating async
callback
import cv2
import numpy as np
background = None
accumulated_weight = 0.5
ROI_top = 100
ROI_bottom = 300
ROI_right = 150
ROI_left = 350
def cal_accum_avg(frame, accumulated_weight):
global background
if background is None:
background = frame.copy().astype("float")
return None
cv2.accumulateWeighted(frame, background, accumulated_weight)
def segment_hand(frame, threshold=25):
global background
diff = cv2.absdiff(background.astype("uint8"), frame)
_ , thresholded = cv2.threshold(diff, threshold, 255, cv2.THRESH_BINARY)
# Grab the external contours for the image
image = cv2.findContours(thresholded.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
contours = cv2.findContours(thresholded.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
hierarchy = cv2.findContours(thresholded.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
if len(contours) == 0:
return None
else:
hand_segment_max_cont = max(contours, key=cv2.contourArea(cv2.UMat()))
return (thresholded, hand_segment_max_cont)
cam = cv2.VideoCapture(0)
num_frames = 0
element = 10
num_imgs_taken = 0
while True:
ret, frame = cam.read()
# filpping the frame to prevent inverted image of captured frame...
frame = cv2.flip(frame, 1)
frame_copy = frame.copy()
roi = frame[ROI_top:ROI_bottom, ROI_right:ROI_left]
gray_frame = cv2.cvtColor(roi, cv2.COLOR_BGR2GRAY)
gray_frame = cv2.GaussianBlur(gray_frame, (9, 9), 0)
if num_frames < 60:
cal_accum_avg(gray_frame, accumulated_weight)
if num_frames <= 59:
cv2.putText(frame_copy, "FETCHING BACKGROUND...PLEASE WAIT", (80, 400),
cv2.FONT_HERSHEY_SIMPLEX, 0.9, (0,0,255), 2)
#cv2.imshow("Sign Detection",frame_copy)
#Time to configure the hand specifically into the ROI...
elif num_frames <= 300:
hand = segment_hand(gray_frame)
cv2.putText(frame_copy, "Adjust hand...Gesture for" + str(element), (200, 400),
cv2.FONT_HERSHEY_SIMPLEX, 1, (0,0,255), 2)
# Checking if hand is actually detected by counting number of contours detected...
if hand is not None:
thresholded, hand_segment = hand
# Draw contours around hand segment
cv2.drawContours(frame_copy, [hand_segment + (ROI_right, ROI_top)], -1, (255, 0, 0),1)
cv2.putText(frame_copy, str(num_frames)+"For" + str(element), (70, 45),
cv2.FONT_HERSHEY_SIMPLEX, 1, (0,0,255), 2)
# Also display the thresholded image
cv2.imshow("Thresholded Hand Image", thresholded)
else:
# Segmenting the hand region...
hand = segment_hand(gray_frame)
# Checking if we are able to detect the hand...
if hand is not None:
# unpack the thresholded img and the max_contour...
thresholded, hand_segment = hand
# Drawing contours around hand segment
cv2.drawContours(frame_copy, [hand_segment + (ROI_right, ROI_top)], -1, (255, 0, 0),1)
cv2.putText(frame_copy, str(num_frames), (70, 45), cv2.FONT_HERSHEY_SIMPLEX, 1,
(0,0,255), 2)
#cv2.putText(frame_copy, str(num_frames)+"For" + str(element), (70, 45),
cv2.FONT_HERSHEY_SIMPLEX, 1, (0,0,255), 2)
cv2.putText(frame_copy, str(num_imgs_taken) + 'images' +"For" + str(element), (200,400),
cv2.FONT_HERSHEY_SIMPLEX, 1, (0,0,255), 2)
# Displaying the thresholded image
cv2.imshow("Thresholded Hand Image", thresholded)
if num_imgs_taken <= 300:
#cv2.imwrite(r"D:\\gesture\\train\\"+str(element)+"\\" + str(num_imgs_taken+300)
+'.jpg', thresholded)
cv2.imwrite(r"D:\\gesture\\x"+"\\" + str(num_imgs_taken) + '.jpg', thresholded)
else:
break
num_imgs_taken +=1
else:
cv2.putText(frame_copy, 'No hand detected...', (200, 400), cv2.FONT_HERSHEY_SIMPLEX, 1,
(0,0,255), 2)
# Drawing ROI on frame copy
cv2.rectangle(frame_copy, (ROI_left, ROI_top), (ROI_right, ROI_bottom), (255,128,0), 3)
cv2.putText(frame_copy, "DataFlair hand sign recognition_ _ _", (10, 20), cv2.FONT_ITALIC, 0.5,
(51,255,51), 1)
# increment the number of frames for tracking
num_frames += 1
# Display the frame with segmented hand
cv2.imshow("Sign Detection", frame_copy)
# Closing windows with Esc key...(any other key with ord can be used too.)
k = cv2.waitKey(30) & 0xFF
if k == 27:
break
# Releasing camera & destroying all the windows...
cv2.destroyAllWindows()
cam.release()

Due to python max doc, it should look as:
hand_segment_max_cont = max(contours, key=cv2.contourArea)
cv2.findContours returns a tuple of list of contours and hierachy, but not the list of contours:
contours,hierachy = cv2.findContours(thresholded.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)

WARN:0 terminating async callback error in cv2

i stuck in below code please help me to solve error which is WARN:0 terminating async callback error in cv2
# this is a client program which run on client side
import cv2
import socket
import numpy as np
import math
import time
try:
start_time = time.time()
state1 = "off"
state2 = "off"
state3 = "off"
mode = "on"
host = "192.168.0.106" # socket which acording server in our case #ip address of rapberry Pi
port = 9345
mySocket = socket.socket()
mySocket.connect((host,port))
cap = cv2.VideoCapture(0)
while(cap.isOpened()):
# read image
ret, img = cap.read()
# get hand data from the rectangle sub window on the screen
cv2.rectangle(img, (300,300), (100,100), (0,255,0),0)
crop_img = img[100:300, 100:300]
# convert to grayscale
grey = cv2.cvtColor(crop_img, cv2.COLOR_BGR2GRAY)
# applying gaussian blur
value = (35, 35)
blurred = cv2.GaussianBlur(grey, value, 0)
# thresholdin: Otsu's Binarization method
_, thresh1 = cv2.threshold(blurred, 127, 255,
cv2.THRESH_BINARY_INV+cv2.THRESH_OTSU)
# show thresholded image
cv2.imshow('Thresholded', thresh1)
# check OpenCV version to avoid unpacking error
(version, _, _) = cv2.__version__.split('.')
if version == '3':
image, contours, hierarchy = cv2.findContours(thresh1.copy(), \
cv2.RETR_TREE, cv2.CHAIN_APPROX_NONE)
elif version == '2':
contours, hierarchy = cv2.findContours(thresh1.copy(),cv2.RETR_TREE, \
cv2.CHAIN_APPROX_NONE)
# find contour with max area
cnt = max(contours, key = lambda x: cv2.contourArea(x))
# create bounding rectangle around the contour (can skip below two lines)
x, y, w, h = cv2.boundingRect(cnt)
cv2.rectangle(crop_img, (x, y), (x+w, y+h), (0, 0, 255), 0)
# finding convex hull
hull = cv2.convexHull(cnt)
# drawing contours
drawing = np.zeros(crop_img.shape,np.uint8)
cv2.drawContours(drawing, [cnt], 0, (0, 255, 0), 0)
cv2.drawContours(drawing, [hull], 0,(0, 0, 255), 0)
# finding convex hull
hull = cv2.convexHull(cnt, returnPoints=False)
# finding convexity defects
defects = cv2.convexityDefects(cnt, hull)
count_defects = 0
cv2.drawContours(thresh1, contours, -1, (0, 255, 0), 3)
# applying Cosine Rule to find angle for all defects (between fingers)
# with angle > 90 degrees and ignore defects
for i in range(defects.shape[0]):
s,e,f,d = defects[i,0]
start = tuple(cnt[s][0])
end = tuple(cnt[e][0])
far = tuple(cnt[f][0])
# find length of all sides of triangle
a = math.sqrt((end[0] - start[0])**2 + (end[1] - start[1])**2)
b = math.sqrt((far[0] - start[0])**2 + (far[1] - start[1])**2)
c = math.sqrt((end[0] - far[0])**2 + (end[1] - far[1])**2)
# apply cosine rule here
angle = math.acos((b**2 + c**2 - a**2)/(2*b*c)) * 57
# ignore angles > 90 and highlight rest with red dots
if angle <= 90:
count_defects += 1
cv2.circle(crop_img, far, 1, [0,0,255], -1)
#dist = cv2.pointPolygonTest(cnt,far,True)
# draw a line from start to end i.e. the convex points (finger tips)
# (can skip this part)
cv2.line(crop_img,start, end, [0,255,0], 2)
#cv2.circle(crop_img,far,5,[0,0,255],-1)
# define actions required
if count_defects == 1:
if (time.time()>start_time+2):
if mode == "on":
mySocket.send("on_led1".encode())
state1 = "on"
print("led 1 is on")
else:
mySocket.send("off_led1".encode())
state1 = "off"
print("led 1 is off")
start_time = time.time()
cv2.putText(img, "led 1 is "+state1, (5, 50), cv2.FONT_HERSHEY_SIMPLEX, 1, 2)
elif count_defects == 2:
if (time.time()>start_time+2):
if mode == "on":
mySocket.send("on_led2".encode())
state2 = "on"
print("led 2 is on")
else:
mySocket.send("off_led2".encode())
state2 = "off"
print("led 2 is off")
start_time = time.time()
cv2.putText(img, "led 2 is "+state2, (5, 50), cv2.FONT_HERSHEY_SIMPLEX, 1, 2)
elif count_defects == 3:
if (time.time()>start_time+2):
if mode == "on":
mySocket.send("on_led3".encode())
state3 = "on"
print("led 3 is on")
else:
mySocket.send("off_led3".encode())
state3 = "off"
print("led 3 is off")
start_time = time.time()
cv2.putText(img, "led 3 is "+state3, (5, 50), cv2.FONT_HERSHEY_SIMPLEX, 1, 2)
elif count_defects == 4:
cv2.putText(img,"mode is "+mode, (50, 50), cv2.FONT_HERSHEY_SIMPLEX, 2, 2)
if(time.time() > start_time+2):
if mode == "on":
mode = "off"
else:
mode = "on"
start_time = time.time()
print(mode)
else:
cv2.putText(img, "use your fingure for turn On/Off lights current mode is "+mode, (5, 50), cv2.FONT_HERSHEY_SIMPLEX, 1, 2)
# show appropriate images in windows
cv2.imshow('Gesture', img)
all_img = np.hstack((drawing, crop_img))
cv2.imshow('Contours', all_img)
k = cv2.waitKey(10)
if k == 27:
break
mySocket.close()
except:
mySocket.send("close_all".encode())
mySocket.close()
above is a program which i created for accessing embedded device using cv2 all things was working properly but still i get an error which [ WARN:0] terminating async callback i also use camera.release() cv2.destroyAllWindows() function but it is not work , any help would be appreciated
i also try this suggestionsuggestion but still it is not working i am using windows 10 operating system
here is full code with explanation code

This warning come from MSMF backend. Try:
cap = cv2.VideoCapture(cv2.CAP_DSHOW)
or
cap = cv2.VideoCapture(0, cv2.CAP_DSHOW)

i find problem in your code. maybe you was updated your cv2 library to version 4. that's why your both condition, which given below is not satisfied.
if version == '3':
image, contours, hierarchy = cv2.findContours(thresh1.copy(), \
cv2.RETR_TREE, cv2.CHAIN_APPROX_NONE)
elif version == '2':
contours, hierarchy = cv2.findContours(thresh1.copy(),cv2.RETR_TREE, \
cv2.CHAIN_APPROX_NONE)
you should remove elif version == '2': and simply use else: it might be help.

python opencv draw contours from frame differencee

Hi in the code below I'm making frame difference and then I want to
find contours that appears from frame difference and drwa them whenever they appear
the code below is working well in differentiating but my problem is with contours I can't draw them when they appear . When I run it this error message appears :
cnt=contours[0] :index error list index out of range
I have used cnt=contours[0] in previous examples it works but I don't know
what is the problem here . Thanks for help
import cv2
import sys
import time
import numpy as np
BLUR_SIZE = 3
NOISE_CUTOFF = 12
cam = cv2.VideoCapture(0)
cam.set(3,640)
cam.set(4,480)
window_name = "delta view"
window_name_now = "now view"
frame_now = cam.read()[1]
frame_now = cam.read()[1]
frame_now = cv2.cvtColor(frame_now, cv2.COLOR_RGB2GRAY)
frame_now = cv2.blur(frame_now, (BLUR_SIZE, BLUR_SIZE))
frame_prior = frame_now
delta_count_last = 1
while True:
frame_delta = cv2.absdiff(frame_prior, frame_now)
frame_delta = cv2.threshold(frame_delta, NOISE_CUTOFF, 255, 3)[1]
delta_count = cv2.countNonZero(frame_delta)
cv2.normalize(frame_delta, frame_delta, 0, 255, cv2.NORM_MINMAX)
frame_delta = cv2.flip(frame_delta, 1)
kernel = np.ones((3,3),np.uint8)
opening = cv2.morphologyEx(frame_delta, cv2.MORPH_OPEN, kernel)
cont_img = opening.copy()
image,contours, hierarchy = cv2.findContours(cont_img, cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE)
cnt=contours[0]
for cnt in contours:
area = cv2.contourArea(cnt)
if area < 2000 or area > 4000:
continue
if len(cnt) < 5:
continue
ellipse = cv2.fitEllipse(cnt)
cv2.ellipse(roi, ellipse, (0,255,0), 2)
cv2.imshow('Contours', roi)
cv2.putText(frame_delta, "DELTA: %d" % (delta_count),
(5, 15), cv2.FONT_HERSHEY_PLAIN, 0.8, (255, 255, 255))
cv2.imshow(window_name, opening)
#frame_delta = cv2.threshold(frame_delta, 92, 255, 0)[1]
dst = cv2.flip(frame_now, 1)
dst = cv2.addWeighted(dst,1.0, frame_delta,0.9,0)
cv2.imshow(window_name_now, dst)
if (delta_count_last == 0 and delta_count != 0):
sys.stdout.write("MOVEMENT %f\n" % time.time())
sys.stdout.flush()
elif delta_count_last != 0 and delta_count == 0:
sys.stdout.write("STILL %f\n" % time.time())
sys.stdout.flush()
delta_count_last = delta_count
frame_prior = frame_now
frame_now = cam.read()[1]
frame_now = cv2.cvtColor(frame_now, cv2.COLOR_RGB2GRAY)
frame_now = cv2.blur(frame_now, (BLUR_SIZE, BLUR_SIZE))
key = cv2.waitKey(10)
if key == 0x1b or key == ord('q'):
cv2.destroyWindow(window_name)
break