I have below two input images:
First:
Second:
Now I wanna detect the square area.
And here is my code:
#!/usr/bin/env python
# coding: utf-8
import numpy as np
import cv2
import argparse
import math
import sys
ap = argparse.ArgumentParser()
ap.add_argument("-i", "--image", required=True, help="path to input image")
args = vars(ap.parse_args())
img = cv2.imread(args["image"])
# img = cv2.bitwise_not(img,img)
# gray = cv2.imread(args["image"],0)
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
gray = cv2.GaussianBlur(gray, (5, 5), 0)
cv2.imshow('gray', gray)
cv2.waitKey(0)
gray = cv2.normalize(gray, gray, alpha=0, beta=255, norm_type=cv2.NORM_MINMAX, dtype=cv2.CV_8UC1)
cv2.imshow('normalized', gray)
cv2.waitKey(0)
# sys.exit()
ret, thresh = cv2.threshold(gray, 230, 255, cv2.THRESH_BINARY_INV)
cv2.imshow('thresh', thresh)
# cv2.imwrite('./wni230.png',thresh)
cv2.waitKey(0)
square_cnts = []
thresh = cv2.morphologyEx(thresh, cv2.MORPH_CLOSE, np.ones((5, 5), np.uint8))
cv2.imshow('dilated', thresh)
cv2.waitKey(0)
# sys.exit()
tmpimage, contours, h = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
cnt0 = contours[0]
tmp_h = h[::-1]
for index, cnt in enumerate(contours[::-1]):
print '!!!!!!!!!!! new round !!!!!!!!!!'
print cv2.arcLength(cnt, True)
# print cnt
approx = cv2.approxPolyDP(cnt, 0.1*cv2.arcLength(cnt, True), True)
print '**********approx*****'
print approx
print len(approx)
print '**********approx end*****'
if len(approx) == 4:
print 'h[i][2]:%s' % tmp_h[0][index][2]
print 'h[i][3]:%s' % tmp_h[0][index][3]
if ret > 0.5:
print "Parallelogram"
elif 0.3 < ret < 0.5:
print "Rectangle"
elif 0 < ret < 0.3:
print "Rhombus"
else:
print "square"
print cv2.arcLength(cnt, True)
print approx
cv2.drawContours(img, [cnt], 0, (0, 0, 255), 2)
cv2.imshow('tmpsquare', img)
cv2.waitKey(0)
if int(cv2.arcLength(cnt, True)) >= 96:
x, y, w, h = cv2.boundingRect(approx)
for j in approx:
cv2.circle(img, (int(j[0][0]), int(j[0][1])), 1, (0, 255, 0), 2)
cv2.imshow('final', img[y:y+h, x:x+w])
cv2.waitKey(0)
print 'target but long squere detected...'
cv2.waitKey(0)
cv2.imshow('img', img)
cv2.imwrite('tmp.png', img)
cv2.waitKey(0)
cv2.destroyAllWindows()
So, I got the two output images:
First:
Second:
Line in red is contour, point in green is points detected by approxPolyDP.
So, the first one is what I wanna.
However, how approxPolyDP locate the up most point for the second one?
Could anyone please explain the rules?
Env:
Python: 2.7.10
Opencv: 3.2.0
Thanks.
Wesley
Related
I am trying to make a motion detector (using the internal camera) in python(3), I am using linux (debian), and I keep getting this error
[ WARN:0#0.724] global /io/opencv/modules/videoio/src/cap_v4l.cpp (889) open VIDEOIO(V4L2:/dev/video0): can't open camera by index
here's the code I'm using
from imutils.video import VideoStream
import argparse
import datetime
import imutils
import time
import cv2
ap = argparse.ArgumentParser()
ap.add_argument("-v", "--video", help="")
ap.add_argument("-a", "--min-area", type=int, default=500, help="minimum area size")
args = vars(ap.parse_args())
if args.get("video", None) is None:
vs = VideoStream(src=0).start()
time.sleep(2.0)
else:
vs = cv2.VideoCapture(args["Video"])
firstFrame = None
while True:
frame = vs.read()
frame = frame if args.get("video", None) is None else frame[1]
text = "Muon is stuck in helium"
if frame is None:
break
frame = imutils.resize(frame, width=500)
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
gray = cv2.GaussianBlur(gray, (21, 21), 0)
if firstFrame is None:
firstFrame = gray
continue
frameDelta = cv2.absdiff(firstFrame, gray)
thresh = cv2.threshold(frameDelta, 25, 255, cv2.THRESH_BINARY)[1]
thresh = cv2.dilate(thresh, None, iterations=2)
cnts = cv2.findContours(thresh.copy(), cv2.RETR_EXTERNAL,
cv2.CHAIN_APPROX_SIMPLE)
cnts = imutils.grab_contours(cnts)
for c in cnts:
if cv2.contourArea(c) < args["min_area"]:
continue
(x, y, w, h) = cv2.boundingRect(c)
cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0), 2)
text = "Muon is fusing hydrogen"
cv2.putText(frame, "Room Status: {}".format(text), (10, 20),
cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255), 2)
cv2.putText(frame, datetime.datetime.now().strftime("%A %d %B %Y %I:%M:%S%p"),
(10, frame.shape[0] - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.35, (0, 0, 255), 1)
cv2.imshow("Security Feed", frame)
cv2.imshow("Thresh", thresh)
cv2.imshow("Frame Delta", frameDelta)
key = cv2.waitKey(1) & 0xFF
if key == ord("q"):
break
vs.stop() if args.get("video", None) is None else vs.release()
cv2.destroyAllWindows()
personally, I think that the problem is linux is having trouble using the internal camera, but I ofc have been wrong before, but if that is the problem, can somebody please help me fix it, and if it isn't, can somebody please help me out, and tell me what I need to fix please
How do I return the contours of the the inner square. Here is my code: I need the contours of the inner square so that I can use the corner points to image warp that area.I have used black tape to help detects the edges. enter image description here
def getBase(img, minarea=100, filter=0, draw=False):
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
mask = cv2.inRange(gray, 0, 50)
result = cv2.bitwise_not(gray, gray, mask=mask)
cv2.imshow("result", result)
_, contours, hierarchy = cv2.findContours(
mask, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE
)
finalContours = []
for i in contours:
area = cv2.contourArea(i)
if area > minarea:
peri = cv2.arcLength(i, True)
approx = cv2.approxPolyDP(i, 0.02 * peri, True)
bbox = cv2.boundingRect(approx)
if filter > 0:
if len(approx) == filter:
finalContours.append((len(approx), area, approx, bbox, i))
else:
finalContours.append((len(approx), area, approx, bbox, i))
finalContours = sorted(finalContours, key=lambda x: x[1], reverse=True)
if draw:
for con in finalContours:
cv2.drawContours(img, con[4], -1, (0, 0, 255), 3)
return img, finalContours
Here is the solution to detect inner rectangle and extract its left corner point, height and width.
import cv2
import numpy as np
def is_rect_contour(contour):
peri = cv2.arcLength(contour, True)
approx = cv2.approxPolyDP(contour, 0.01 * peri, True)
if len(approx) == 4:
return True
else:
return False
image = cv2.imread("./rect.jpg")
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
blurred = cv2.GaussianBlur(gray, (7, 7), 0)
thresh = cv2.threshold(blurred, 40, 255, cv2.THRESH_BINARY_INV)[1]
cnts = cv2.findContours(thresh.copy(), cv2.RETR_CCOMP, cv2.CHAIN_APPROX_SIMPLE)
cnts = cnts[0]
refined_contours = []
for cnt in cnts:
if is_rect_contour(cnt):
refined_contours.append(cnt)
inner_rect_cnt = min(refined_contours, key=cv2.contourArea)
(x, y, w, h) = cv2.boundingRect(inner_rect_cnt)
print("x: {}, y:{}, widhth:{}, height:{}".format(x, y, w, h))
cv2.drawContours(image, [inner_rect_cnt], -1, (0, 255, 0), 2)
cv2.imshow('Contours', image)
cv2.waitKey(0)
cv2.destroyAllWindows()
Output:
x: 425, y:126, widhth:1104, height:720
import cv2
import numpy as np
import warnings
warnings.filterwarnings("ignore")
cap = cv2.VideoCapture(0)
while True :
ret ,frame = cap.read()
hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)
lower_yellow = np.array([20,0,0])
upper_yellow = np.array([40,255,255])
mask = cv2.inRange(hsv, lower_yellow, upper_yellow)
res = cv2.bitwise_and(frame,frame, mask= mask)
img = cv2.medianBlur(res, 5)
ccimg = cv2.cvtColor(img, cv2.COLOR_HSV2BGR)
cimg = cv2.cvtColor(ccimg, cv2.COLOR_BGR2GRAY)
circles = cv2.HoughCircles(cimg, cv2.HOUGH_GRADIENT, 1, 20,param1=50, param2=30, minRadius=20, maxRadius=30)
if circles is not None:
print("circle is found")
circles = np.uint16(np.around(circles))
for i in circles[0, :]:
cv2.circle(cimg, (i[0], i[1]), i[2], (0, 255, 0), 2)
cv2.circle(cimg, (i[0], i[1]), 2, (0, 0, 255), 3)
cv2.imshow('detected circles', cimg)
cv2.imshow('res',res)
if cv2.waitKey(1) & 0xFF == ord("q"):
break
cv2.destroyAllWindows()
I am tring to detect trafict light by using opencv , initially i want to detect yellow color by using HSV space and then median filtering and finding the circle but it raises errors async ReadSample() call is failed with error status: -1072873821 and OnReadSample() is called with error status: -1072873821 probably errors are caused by the if state for checking if it finds any circle or not also the error is a long list but these two are repeated.
The capture device is failing to read a frame. The OnReadSample() call is failing on cap.read() and you should implement logic to handle a frame not being read. I've demonstrated this below:
import cv2
import numpy as np
import warnings
warnings.filterwarnings("ignore")
cap = cv2.VideoCapture(0)
while True:
ret, frame = cap.read()
if ret == True:
hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)
lower_yellow = np.array([20, 0, 0])
upper_yellow = np.array([40, 255, 255])
mask = cv2.inRange(hsv, lower_yellow, upper_yellow)
res = cv2.bitwise_and(frame, frame, mask=mask)
img = cv2.medianBlur(res, 5)
ccimg = cv2.cvtColor(img, cv2.COLOR_HSV2BGR)
cimg = cv2.cvtColor(ccimg, cv2.COLOR_BGR2GRAY)
circles = cv2.HoughCircles(cimg, cv2.HOUGH_GRADIENT, 1, 20, param1=50, param2=30, minRadius=20, maxRadius=30)
if circles is not None:
print("circle is found")
circles = np.uint16(np.around(circles))
for i in circles[0, :]:
cv2.circle(cimg, (i[0], i[1]), i[2], (0, 255, 0), 2)
cv2.circle(cimg, (i[0], i[1]), 2, (0, 0, 255), 3)
cv2.imshow('detected circles', cimg)
cv2.imshow('res', res)
else:
print("Read Failed")
if cv2.waitKey(1) & 0xFF == ord("q"):
break
cv2.destroyAllWindows()
In this problem we are trying to detect persons in a WEBCAM video in REAL TIME. The code is working fine for 1 person but when more than one person is entering then the code is failing miserably. Here is the code :-
import cv2
import numpy as np
cap = cv2.VideoCapture(0)
kernel = np.ones((5,5), np.uint8)
background = None
while True:
ret,frame = cap.read()
gray = frame.copy()
gray = cv2.cvtColor(gray, cv2.COLOR_BGR2GRAY)
gray = cv2.GaussianBlur(gray, (11,11), 0)
if background is None:
background = gray
continue
delta = cv2.absdiff(background, gray)
thresh = cv2.threshold(delta, 5, 255,
cv2.THRESH_BINARY+cv2.THRESH_OTSU)[1]
thresh = cv2.dilate(thresh, kernel, iterations=2)
_,contours,hierarchy = cv2.findContours(thresh, cv2.RETR_EXTERNAL,
cv2.CHAIN_APPROX_SIMPLE)
if(len(contours)==0):
continue
#areas = [cv2.contourArea(c) for c in contours]
#max_index = np.argmax(areas)
#cnt=contours[max_index]
#(x,y,w,h) = cv2.boundingRect(cnt)
#if(1.0*(w*h)/(640*480)<0.75):
#cv2.rectangle(frame, (x,y), (x+w,y+h), (0,0,255), 3)
#print("Area: ",w*h)
for i in range(len(contours)):
(x,y,w,h) = cv2.boundingRect(contours[i])
if(w*h<=90000):
cv2.rectangle(frame, (x,y), (x+w,y+h), (0,0,255), 5)
#cv2.imshow('thresh', thresh)
cv2.imshow('frame', frame)
if cv2.waitKey(1)==27:
break
cap.release()
cv2.destroyAllWindows()
I think the problem is that the code is not able to separate the different contours of the different persons detected but I may not be the only reason. Can someone help me?
Here is the python code I have written :-
import cv2
import argparse
ap = argparse.ArgumentParser()
ap.add_argument("-v", "--video",
help = "path to the (optional) video file")
args = vars(ap.parse_args())
if not args.get("video", False):
cap = cv2.VideoCapture(0)
else:
cap = cv2.VideoCapture(args["video"])
fgbg = cv2.bgsegm.createBackgroundSubtractorMOG()
while True:
ret, frame = cap.read()
fgmask = fgbg.apply(frame)
cv2.imshow('frame',fgmask)
k = cv2.waitKey(30) & 0xff
if k == 27:
break
cap.release()
cv2.destroyAllWindows()
How to put bounding box around the detected human outline and improve efficiency of the python code to perform background subtraction on the live video feed taken from webcam. Can someone help?
Drawing Contour Using Background Subtraction
import cv2
import argparse
ap = argparse.ArgumentParser()
ap.add_argument("-v", "--video",
help = "path to the (optional) video file")
args = vars(ap.parse_args())
if not args.get("video", False):
cap = cv2.VideoCapture(0)
else:
cap = cv2.VideoCapture(args["video"])
fgbg = cv2.bgsegm.createBackgroundSubtractorMOG()
while True:
ret, frame = cap.read()
fgmask = fgbg.apply(frame)
gray=cv2.cvtColor(fgmask,cv2.COLOR_BGR2GRAY)
ret,th1 = cv2.threshold(gray,25,255,cv2.THRESH_BINARY)
_,contours,hierarchy = cv2.findContours(th1,cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE)
for cnt in contours:
area = cv2.contourArea(cnt)
if area > 1000 and area < 40000:
x,y,w,h = cv2.boundingRect(cnt)
cv2.rectangle(fgmask,(x,y),(x+w,y+h),(255,0,0),2)
cv2.imshow('frame',fgmask)
k = cv2.waitKey(30) & 0xff
if k == 27:
break
cap.release()
cv2.destroyAllWindows()
Drawing Contour using HSV Masking and Convex Hull
Set value for hsv mask.
import cv2
import argparse
ap = argparse.ArgumentParser()
ap.add_argument("-v", "--video",
help = "path to the (optional) video file")
args = vars(ap.parse_args())
if not args.get("video", False):
cap = cv2.VideoCapture(0)
else:
cap = cv2.VideoCapture(args["video"])
fgbg = cv2.bgsegm.createBackgroundSubtractorMOG()
while True:
ret, frame = cap.read()
frame = cv2.imread(frame)
hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)
lower = np.array([50,103,40])
upper = np.array([255,255, 255])
mask = cv2.inRange(hsv, lower, upper)
fg = cv2.bitwise_and(frame, frame, mask=255-mask)
fg = cv2.cvtColor(fg.copy(),cv2.COLOR_HSV2BGR)
fg = cv2.cvtColor(fg,cv2.COLOR_BGR2GRAY)
fg = cv2.threshold(fg, 120,255,cv2.THRESH_BINARY+cv2.THRESH_OTSU)[1]
#plt.imshow(fg)
#plt.show()
fgclosing = cv2.morphologyEx(fg.copy(), cv2.MORPH_CLOSE, kernel)
se = np.ones((3,3),np.uint8)
#fgdilated = cv2.morphologyEx(fgclosing, cv2.MORPH_CLOSE,cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (4,4)))
fgdilated = cv2.dilate(fgclosing, kernel = se , iterations = 8)
img = frame.copy()
ret, threshed_img = cv2.threshold(fgdilated,
127, 255, cv2.THRESH_BINARY)
image, contours, hier = cv2.findContours(threshed_img,cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_NONE)
for cnt in contours:
#print(cv2.contourArea(cnt))
if cv2.contourArea(cnt) > 44000:
# get convex hull
hull = cv2.convexHull(cnt)
#cv2.drawContours(img, [hull], -1, (0, 0, 255), 1)
#print(hull)
(x,y,w,h) = cv2.boundingRect(cnt)
#cv2.rectangle(img, (x,y), (x+w,y+h), (255, 0, 0), 2)
contours = hull
#c1 = max(contours, key=cv2.contourArea)
hull = cv2.convexHull(cnt)
c = hull
#print(c)
cv2.drawContours(img, [hull], -1, (0, 0, 255), 1)
# determine the most extreme points along the contour
extLeft = tuple(c[c[:, :, 0].argmin()][0])
extRight = tuple(c[c[:, :, 0].argmax()][0])
extTop = tuple(c[c[:, :, 1].argmin()][0])
extBot = tuple(c[c[:, :, 1].argmax()][0])
cv2.drawContours(img, [c], -1, (0, 255, 255), 2)
cv2.circle(img, extLeft, 8, (0, 0, 255), -1)
cv2.circle(img, extRight, 8, (0, 255, 0), -1)
cv2.circle(img, extTop, 8, (255, 0, 0), -1)
cv2.circle(img, extBot, 8, (255, 255, 0), -1)
lx = extLeft[1]
ly = extLeft[0]
rx = extRight[1]
ry = extRight[0]
tx = extTop[1]
ty = extTop[0]
bx = extBot[1]
by = extBot[0]
x,y = lx,by
w,h = abs(rx-lx),abs(ty-by)
#cv2.rectangle(img, (x,y), (x+w,y+h), (255, 0, 0), 2)
font = cv2.FONT_HERSHEY_SIMPLEX
cv2.putText(img,str(extLeft[0])+','+str(extLeft[1]),(extLeft), font, 2,(0, 0, 255),2,cv2.LINE_AA)
cv2.putText(img,str(extRight[0])+','+str(extRight[1]),(extRight), font, 2,(0, 255, 0),2,cv2.LINE_AA)
cv2.putText(img,str(extTop[0])+','+str(extTop[1]),(extTop), font, 2,(255, 0, 0),2,cv2.LINE_AA)
cv2.putText(img,str(extBot[0])+','+str(extBot[1]),(extBot), font, 2,(255, 255, 0),2,cv2.LINE_AA)
im = frame[tx:bx,ly:ry,:]
cx = im.shape[1]//2
cy = im.shape[0]//2
cv2.circle(im, (cx,cy), 15, (0, 255, 0))
plt.imshow(img)
plt.show()
You can use findContours.
import cv2
import argparse
ap = argparse.ArgumentParser()
ap.add_argument("-v", "--video",
help = "path to the (optional) video file")
args = vars(ap.parse_args())
if not args.get("video", False):
cap = cv2.VideoCapture(0)
else:
cap = cv2.VideoCapture(args["video"])
fgbg = cv2.bgsegm.createBackgroundSubtractorMOG()
while True:
ret, frame = cap.read()
fgmask = fgbg.apply(frame)
mask = 255 - fgmask
_, contours, _ = cv2.findContours(
mask, cv2.RETR_TREE, cv2.CHAIN_APPROX_NONE)
fgmask = cv2.cvtColor(fgmask, cv2.COLOR_GRAY2BGR)
for contour in contours:
area = cv2.contourArea(contour)
#only show contours that match area criterea
if area > 500 and area < 20000:
rect = cv2.boundingRect(contour)
x, y, w, h = rect
cv2.rectangle(fgmask, (x, y), (x+w, y+h), (0, 255, 0), 3)
cv2.imshow('frame',fgmask)
k = cv2.waitKey(30) & 0xff
if k == 27:
break
cap.release()
cv2.destroyAllWindows()
I have tested with the video https://github.com/opencv/opencv/blob/master/samples/data/vtest.avi