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()
Related
I followed a video online about motion detection using openCV however I came across the problem that the findContours function is not returning a value. Any help is appreceated.
Here is the code:
import cv2
import time
import datetime
import imutils
def motion_detection():
video_capture = cv2.VideoCapture(0, cv2.CAP_DSHOW)
time.sleep(2)
first_frame = None
while True:
frame = video_capture.read()[1]
text = 'Unoccupied'
greyscale_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
gaussian_frame = cv2.GaussianBlur(greyscale_frame, (21, 21), 0)
blur_frame = cv2.blur(gaussian_frame, (5, 5))
greyscale_image = blur_frame
if first_frame is None:
first_frame = greyscale_image
else:
pass
frame = imutils.resize(frame, width=500)
frame_delta = cv2.absdiff(first_frame, greyscale_image)
# edit the ** thresh ** depending on the light/dark in room,
# change the 100(anything pixel value over 100 will become 255(white)
thresh = cv2.threshold(frame_delta, 100, 255, cv2.THRESH_BINARY)[1]
dilate_image = cv2.dilate(thresh, None, iterations=2)
cnt = cv2.findContours(dilate_image.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)[1]
for c in cnt:
if cv2.contourArea(c) > 800:
(x, y, w, h) = cv2.boundingRect(
c)
cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0), 2)
text = 'Occupied'
# text that appears when there is motion in video feed
else:
pass
''' now draw text and timestamp on security feed '''
font = cv2.FONT_HERSHEY_SIMPLEX
cv2.putText(frame, '{+} Room Status: %s' % 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), font, 0.35, (0, 0, 255), 1)
cv2.imshow('Security Feed', frame)
cv2.imshow('Threshold(foreground mask)', dilate_image)
cv2.imshow('Frame_delta', frame_delta)
key = cv2.waitKey(1) & 0xFF
if key == ord('q'):
cv2.destroyAllWindows()
break
if __name__ == '__main__':
motion_detection()
I have tried to debug and find the problem the code is exactly what the video said to write and I have had no luck.
I'm working on a project that requires me to detect a red rectangle in real time. so far I've managed to get the colour and shape detected together but it can't differentiate between other objects that are red.
How might I go about doing this?
import cv2
import numpy as np
def nothing():
pass
cap = cv2.VideoCapture(0)
font = cv2.FONT_HERSHEY_COMPLEX
while True:
_, frame = cap.read()
hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)
# Red color
low_red = np.array([175, 50, 20])
high_red = np.array([180, 255, 255])
mask1 = cv2.inRange(hsv, low_red, high_red)
kernel = np.ones((5, 5), np.uint8)
mask2 = cv2.erode(mask1, kernel)
red = cv2.bitwise_and(frame, frame, mask=mask2)
contours, _ = cv2.findContours(mask2, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
for cnt in contours:
area = cv2.contourArea(cnt)
approx = cv2.approxPolyDP(cnt, 0.02 * cv2.arcLength(cnt, True), True)
hull = cv2.convexHull(cnt)
x = approx.ravel()[0]
y = approx.ravel()[1]
if area > 400:
cv2.drawContours(frame, [approx], 0, (0, 0, 0), 5)
if len(approx) == 4:
cv2.putText(frame, "Rectangle", (x, y), font, 1, (0, 0, 0))
edges = cv2.Canny(frame, 100, 200)
_, threshold_binary = cv2.threshold(frame, 128, 255, cv2.THRESH_BINARY)
_, threshold_binary_inv = cv2.threshold(frame, 128, 255, cv2.THRESH_BINARY_INV)
_, threshold_trunc = cv2.threshold(frame, 128, 255, cv2.THRESH_TRUNC)
_, threshold_to_zero = cv2.threshold(frame, 12, 255, cv2.THRESH_TOZERO)
cv2.imshow("Frame", frame)
cv2.imshow('edges', edges)
cv2.imshow('red', red)
cv2.imshow("mask", mask1)
key = cv2.waitKey(1)
if key == 27:
cap.release()
cv2.destroyAllWindows()
break
Output image:
I am building handwriting recognition project but I am getting this error of cvtColor while trying to change the image from BGR2HSV.
cap = cv2.VideoCapture(0)
Lower_blue = np.array([110, 50, 50])
Upper_blue = np.array([130, 255, 255])
pred_class = 0
pts = deque(maxlen = 512)
blackboard = np.zeros((480, 640, 3), dtype = np.uint8)
digit = np.zeros((200, 200, 3), dtype = np.uint8)
while(cap.isOpened()):
ret, img = cap.read()
img = cv2.flip(img, 1)
imgHSV = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
mask = cv2.inRange(imgHSV, Lower_blue, Upper_blue)
blur = cv2.medianBlur(mask, 15)
blur = cv2.GaussianBlur(blur, (5, 5), 0)
thresh = cv2.threshold(blur, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)[1]
cnts = cv2.findContours(thresh.copy(), cv2.RETR_TREE, cv2.CHAIN_APPROX_NONE)[1]
center = None
if len(cnts) >= 1:
contour = max(cnts, key = cv2.contourArea)
if cv2.contourArea(contour) > 250:
((x, y), radius) = cv2.minEnclosingCircle(contour)
cv2.circle(img, (int(x), int(y)), int(radius), (0, 255, 255), 2)
cv2.circle(img, center, 5, (0, 255, 255), -1)
M = cv2.moments(contour)
center = (int(M['m10'] / M['m00']), int(M['m01'] / M['m00']))
pts.appendleft(center)
for i in range (1, len(pts)):
if pts[i - 1] is None or pts[i] is None:
continue
cv2.line(blackboard, pts[i - 1], pts[i], (255, 255, 255), 10)
cv2.line(img, pts[i - 1], pts[i], (0, 0,255), 5)
elif len(cnts) == 0:
if len(pts) != []:
blackboard_gray = cv2.cvtColor(blackboard, cv2.COLOR_BGR2GRAY)
blur1 = cv2.medianBlur(blackboard_gray, 15)
blur1 = cv2.GaussianBlur(blur1, (5, 5), 0)
thresh1 = cv2.threshold(blur1, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)[1]
blackboard_cnts = cv2.findContours(thresh1.copy(), cv2.RETR_TREE, cv2.CHAIN_APPROX_NONE)[1]
if len(blackboard_cnts) >= 1:
cnt = max(blackboard_cnts, key = cv2.contourArea)
print(cv2.contourArea(cnt))
if cv2.contourArea(cnt) > 2000:
x, y, w, h = cv2.boundingRect(cnt)
digit = blackboard_gray[y:y + h, x:x + w]
#new Image = process_letter(digit)
pred_probab, pred_class = keras_predict(model1, digit)
print(pred_class, pred_probab)
pts = deque(maxlen = 512)
blackboard = np.zeros((480, 640, 3), dtype = uint8)
cv2.putText(img, "Conv Network : " + str(letter_count [pred_class]), (10, 470),
cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 255), 2)
cv2.imshow("Frame", img)
cv2.imshow("Contours", thresh)
k = cv2.waitkey(10)
if k == 27:
break
How do I fix this issue of OpenCV error? Please help!
I am getting following error:
error: OpenCV(3.4.1) C:\Miniconda3\conda-bld\opencv-suite_1533128839831\work\modules\imgproc\src\color.cpp:11214: error: (-215) (scn == 3 || scn == 4) && (depth == 0 || depth == 5) in function cv::cvtColor
I think following error: error: (-215) (scn == 3 || scn == 4) && (depth == 0 || depth == 5) in function cv::cvtColor is because of camera unable to capture frame correctly. Check this by printing ret value. It camera is able to capture frame correctly, it will return True, else False. Above error is happening because None is passed to cv2.cvtColor function. You can use below code as a safety check:
if ret is True:
# your code goes here
else:
break
Moreover, please consider below points:
It's cv2.waitKey(10), not cv2.waitkey(10)[Capital 'K']
cv2.findContours function return 3 values(i.e. image, contours and hierarchy) in OpenCV 3.x but just 2 values(i.e. contours, hierarchy) in openCV 4.x
len(pts) != [] doesn't make sense. Here, you are trying to compare a number to an empty list. Change it to len(pts) != 0.
What's letter_count inside cv2.putText function. Kindly recheck it.
In place of re-initializing deque(pts = deque(maxlen = 512)) in case of len(cnts)==0 and len(pts) != 0, you can use the old pts by simply clearing it using pts.clear(). Just a thought!
Also, add below code at the end:
cap.release()
cv2.destroyAllWindows()
Try below modified code(Tested using openCV v4.0.0):
import cv2
import numpy as np
from collections import deque
cap = cv2.VideoCapture(0)
Lower_blue = np.array([110, 50, 50])
Upper_blue = np.array([130, 255, 255])
pred_class = 0
pts = deque(maxlen = 512)
blackboard = np.zeros((480, 640, 3), dtype = np.uint8)
digit = np.zeros((200, 200, 3), dtype = np.uint8)
while(cap.isOpened()):
ret, img = cap.read()
img = cv2.flip(img, 1)
imgHSV = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
mask = cv2.inRange(imgHSV, Lower_blue, Upper_blue)
blur = cv2.medianBlur(mask, 15)
blur = cv2.GaussianBlur(blur, (5, 5), 0)
thresh = cv2.threshold(blur, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)[1]
cnts = cv2.findContours(thresh.copy(), cv2.RETR_TREE, cv2.CHAIN_APPROX_NONE)[0]
center = None
if len(cnts) >= 1:
contour = max(cnts, key = cv2.contourArea)
if cv2.contourArea(contour) > 250:
((x, y), radius) = cv2.minEnclosingCircle(contour)
cv2.circle(img, (int(x), int(y)), int(radius), (0, 255, 255), 2)
cv2.circle(img, center, 5, (0, 255, 255), -1)
M = cv2.moments(contour)
center = (int(M['m10'] / M['m00']), int(M['m01'] / M['m00']))
pts.appendleft(center)
# print(pts)
for i in range (1, len(pts)):
if pts[i - 1] is None or pts[i] is None:
print("Continue")
continue
cv2.line(blackboard, pts[i - 1], pts[i], (255, 255, 255), 10)
cv2.line(img, pts[i - 1], pts[i], (0, 0,255), 5)
cv2.imshow("Frame", img)
cv2.imshow("Contours", thresh)
# press q to stop
if cv2.waitKey(1) & 0xFF == ord('q'):
break
else:
if len(pts) != 0:
blackboard_gray = cv2.cvtColor(blackboard, cv2.COLOR_BGR2GRAY)
blur1 = cv2.medianBlur(blackboard_gray, 15)
blur1 = cv2.GaussianBlur(blur1, (5, 5), 0)
thresh1 = cv2.threshold(blur1, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)[1]
print('Hello')
blackboard_cnts = cv2.findContours(thresh1.copy(), cv2.RETR_TREE, cv2.CHAIN_APPROX_NONE)[0]
if len(blackboard_cnts) >= 1:
cnt = max(blackboard_cnts, key = cv2.contourArea)
print(cv2.contourArea(cnt))
if cv2.contourArea(cnt) > 2000:
x, y, w, h = cv2.boundingRect(cnt)
digit = blackboard_gray[y:y + h, x:x + w]
#new Image = process_letter(digit)
#pred_probab, pred_class = keras_predict(model1, digit)
print(digit.shape)
#pts = deque(maxlen = 512)
pts.clear()
blackboard = np.zeros((480, 640, 3), dtype = np.uint8)
#cv2.putText(img, "Conv Network : " + str(letter_count[pred_class]), (10, 470), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 255), 2)
cv2.putText(img, "Conv Network :", (10, 470), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 255), 2)
cv2.imshow("Frame", img)
cv2.imshow("Contours", thresh)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
cap.release()
cv2.destroyAllWindows()
Heii Im unisng raspberry pi and pi camera to read the distance between pi cand object (square box). im using perimeter of the contour and divide by 4 and getting one side length , then by Distance = (length x known length) / calculated one side length.
I'm always having a square which is the whole frame of the camera window and getting a distance. How to avoid the whole frame window square.
https://drive.google.com/open?id=15-kd32Zd_tZeMPWQ2I1s9_NJGYg3v_9l
any idea why it comes and how to eliminate it ?
import cv2
import numpy as np
import time
def nothing(x):
# any operation
pass
cap = cv2.VideoCapture(0)
font = cv2.FONT_HERSHEY_COMPLEX
while True:
_, frame = cap.read()
hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)
l_h = 0
l_s = 0
l_v = 42
u_h = 180
u_s = 255
u_v = 255
lower_red = np.array([l_h, l_s, l_v])
upper_red = np.array([u_h, u_s, u_v])
mask = cv2.inRange(hsv, lower_red, upper_red)
kernel = np.ones((5, 5), np.uint8)
mask = cv2.erode(mask, kernel)
# Contours detection
contours, _ = cv2.findContours(mask, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
for cnt in contours:
area = cv2.contourArea(cnt)
approx = cv2.approxPolyDP(cnt, 0.02*cv2.arcLength(cnt, True), True)
x = approx.ravel()[0]
y = approx.ravel()[1]
perimeter = cv2.arcLength(cnt,True)
if area > 200:
cv2.drawContours(frame, [approx], 0, (0, 0, 0), 5)
if len(approx) == 3:
cv2.putText(frame, "triangle " , (x, y), font, 1, (0, 0, 0))
print("Traingle is detected and distance is = ",((154*58)/(perimeter/3))*0.2645 , "mm")
elif len(approx) == 4:
cv2.putText(frame, "square", (x, y), font, 1, (0, 0, 0))
#print(perimeter/4)
print("Square is detected and distance is = ",((154*58)/(perimeter/4))*0.2645 , "mm")
#print("square distance is = ",((149*58)/(perimeter/4))*0.2645 , "mm")
elif len(approx) == 5:
cv2.putText(frame, "Pentogon", (x, y), font, 1, (0, 0, 0))
print("Pentogon is detected and distance is = ",((151*58)/(perimeter/5))*0.2645 , "mm")
elif len(approx) == 6:
cv2.putText(frame, "6angle", (x, y), font, 1, (0, 0, 0))
cv2.imshow("Frame", frame)
cv2.imshow("Mask", mask)
key = cv2.waitKey(1)
if key == 0:
break
cap.release()
cv2.destroyAllWindows()
I want to run this code on my Raspberry Pi 3. I have used pip install imutils on the Pi but, when I run the code via the CLI, it returns "No module named imutils". I do not wish to use virtual environments. I have cv2 running correctly on the Pi and that works no problem, is there a fix for this imutils problem?
Updating, upgrading, removing imutils but it is needed.
import numpy as np
import cv2
import Person
import time
import imutils
import datetime
cap = cv2.VideoCapture('testVideo.mp4')
fgbg = cv2.createBackgroundSubtractorMOG2(detectShadows=True) # Create the background substractor
kernelOp = np.ones((3, 3), np.uint8)
kernelOp1 = np.ones((7, 7), np.uint8)
kernelOp2 = np.ones((5, 5), np.uint8)
kernelCl = np.ones((11, 11), np.uint8)
kernelCl1 = np.ones((20, 20), np.uint8)
kernelCl2 = np.ones((25, 25), np.uint8)
# Variables
font = cv2.FONT_HERSHEY_SIMPLEX
persons = []
max_p_age = 5
pid = 1
areaTH = 5000
w_margin = 50
h_margin = 50
wmax = 500
import pdb;
pdb.set_trace() # debuginimo pradzia
# Atvaizdavimo kintamieji
cnt_up = 0
cnt_down = 0
line_down_color = (255, 0, 0)
line_up_color = (0, 0, 255)
pts_L1 = np.array([[0, 320], [480, 320]])
pts_L2 = np.array([[0, 400], [480, 400]])
counter = 0
while (cap.isOpened()):
ret, frame = cap.read() # read a frame
frame = imutils.resize(frame, width=min(640, frame.shape[1]))
fgmask = fgbg.apply(frame) # Use the substractor
try:
ret, imBin = cv2.threshold(fgmask, 200, 255, cv2.THRESH_BINARY)
mask0 = cv2.morphologyEx(imBin, cv2.MORPH_OPEN, kernelOp2)
mask = cv2.morphologyEx(mask0, cv2.MORPH_CLOSE, kernelCl2)
except:
# if there are no more frames to show...
print('EOF')
break
maskOriginal = mask
_, contours0, hierarchy = cv2.findContours(mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)
########if contour is too big cut in half
mask2_flag = 0
for cnt in contours0:
area = cv2.contourArea(cnt)
if area > areaTH:
M = cv2.moments(cnt)
cx = int(M['m10'] / M['m00'])
cy = int(M['m01'] / M['m00'])
x, y, w, h = cv2.boundingRect(cnt)
if w > wmax:
mask2 = cv2.line(mask, ((x + w / 2), 0), ((x + w / 2), 640), (0, 0, 0), 10)
mask2_flag = 1
if mask2_flag == 0:
mask2 = mask
cv2.imshow('Mask line', mask2)
cv2.imshow('mask to open', mask0)
cv2.imshow('Mask initialize', maskOriginal)
cv2.imshow('initial subtraction', imBin)
_, contours0, hierarchy = cv2.findContours(mask2, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)
for cnt in contours0:
cv2.drawContours(frame, cnt, -1, (0, 255, 0), 3, 8)
area = cv2.contourArea(cnt)
for i in persons:
i.updateDingimas(i.getDingimas() + 1)
if i.getDingimas() > 25:
persons.remove(i)
if area > areaTH:
M = cv2.moments(cnt)
cx = int(M['m10'] / M['m00'])
cy = int(M['m01'] / M['m00'])
x, y, w, h = cv2.boundingRect(cnt)
print('x{} y{} w{} h{}'.format(x, y, w, h))
new = True
for i in persons:
if abs(x - i.getX()) <= w_margin and abs(y - i.getY()) <= h_margin:
new = False
i.updateCoords(cx, cy)
i.updateDingimas(0)
break
if new == True:
p = Person.MyPerson(pid, cx, cy, max_p_age)
persons.append(p)
pid += 1
cv2.circle(frame, (cx, cy), 5, (0, 0, 255), -1)
img = cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0), 2)
cv2.drawContours(frame, cnt, -1, (0, 255, 0), 3)
cv2.imshow('img', img)
#########################
# Trajectory rendering
#########################
for i in persons:
if len(i.getTracks()) >= 2:
pts = np.array(i.getTracks(), np.int32)
pts = pts.reshape((-1, 1, 2))
frame = cv2.polylines(frame, [pts], False, i.getRGB())
if i.getDir() == None:
i.kurEina(pts_L2[0, 1], pts_L1[0, 1])
if i.getDir() == 'up':
cnt_up += 1
print('Timestamp: {:%H:%M:%S} UP {}'.format(datetime.datetime.now(), cnt_up))
elif i.getDir() == 'down':
cnt_down += 1
print('Timestamp: {:%H:%M:%S} DOWN {}'.format(datetime.datetime.now(), cnt_down))
cv2.putText(frame, str(i.getId()), (i.getX(), i.getY()), font, 0.7, i.getRGB(), 1, cv2.LINE_AA)
#########################
# Rendering
#########################
str_in = 'In: ' + str(cnt_up)
str_out = 'Out: ' + str(cnt_down)
frame = cv2.polylines(frame, [pts_L1], False, line_down_color, thickness=4)
frame = cv2.polylines(frame, [pts_L2], False, line_up_color, thickness=4)
cv2.putText(frame, str_in, (10, 50), font, 1, (0, 0, 255), 2, cv2.LINE_AA)
cv2.putText(frame, str_out, (10, 100), font, 1, (255, 0, 0), 2, cv2.LINE_AA)
cv2.imshow('Frame', frame)
# Abort and exit with 'Q' or ESC
k = cv2.waitKey(30) & 0xff
if k == 27:
break
cap.release() # release video file
cv2.destroyAllWindows() # close all openCV windows
I want to run this code without "No module named imutils" error.
If you intend to use the module with Python 3, you need to install it with pip3 so that it is installed in the correct location.