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As I learned from tutorial videos, I tried this code:
import numpy as np
import cv2
ARUCO_DICT = {
"DICT_4X4_50": cv2.aruco.DICT_4X4_50,
"DICT_4X4_100": cv2.aruco.DICT_4X4_100,
}
#In fact there are more dictionary keys than written above. I deleted them to shorten the question.
def aruco_display(corners, ids, rejected, image):
if len(corners) > 0:
ids = ids.flatten()
for (markerCorner, markerID) in zip(corners, ids):
corners = markerCorner.reshape((4, 2))
(topLeft, topRight, bottomRight, bottomLeft) = corners
topRight = (int(topRight[0]), int(topRight[1]))
bottomRight = (int(bottomRight[0]), int(bottomRight[1]))
bottomLeft = (int(bottomLeft[0]), int(bottomLeft[1]))
topLeft = (int(topLeft[0]), int(topLeft[1]))
cv2.line(image, topLeft, topRight, (0, 255, 0), 2)
cv2.line(image, topRight, bottomRight, (0, 255, 0), 2)
cv2.line(image, bottomRight, bottomLeft, (0, 255, 0), 2)
cv2.line(image, bottomLeft, topLeft, (0, 255, 0), 2)
cX = int((topLeft[0] + bottomRight[0]) / 2.0)
cY = int((topLeft[1] + bottomRight[1]) / 2.0)
cv2.circle(image, (cX, cY), 4, (0, 0, 255), -1)
cv2.putText(image, str(markerID),(topLeft[0], topLeft[1] - 10), cv2.FONT_HERSHEY_SIMPLEX,
0.5, (0, 255, 0), 2)
print("[Inference] ArUco marker ID: {}".format(markerID))
return image
img = cv2.imread('markers.jpg', 1)
#the first parameter will change according to the name of the photo
aruco_type = ["DICT_4X4_50",
"DICT_4X4_100",
"DICT_4X4_250",
]
for i in aruco_type:
arucoDict = cv2.aruco.getPredefinedDictionary(ARUCO_DICT[i])
arucoParams = cv2.aruco.DetectorParameters()
corners, ids, rejected = cv2.aruco.ArucoDetector(arucoDict, arucoParams).detectMarkers(img)
detected_markers = aruco_display(corners, ids, rejected, img)
cv2.imshow("Image", detected_markers)
cv2.waitKey(0)
cv2.destroyAllWindows()
This code can detect most of codes but there is still problem due to not detecting some ArUco's like that:
ArUco1
ArUco2
How can I solve this issue?
I think if it can detect some of them, I don't understand why it cannot detect ArUco's on same image.
I have tried to use cv2.putText and it appears to show the position based on the the top right of the window and not the actual center of the image. It will probably be an obvious fix since I just started using opencv
import os
import numpy as np
font = cv2.FONT_HERSHEY_SIMPLEX
org = (50, 50)
fontScale = 1
color = (255, 0, 0)
radius = 3
thickness = 2
cascPath=os.path.dirname(cv2.__file__)+"/data/haarcascade_frontalface_default.xml"
faceCascade = cv2.CascadeClassifier(cascPath)
video_capture = cv2.VideoCapture(0)
while (True):
ret, frames = video_capture.read()
gray = cv2.cvtColor(frames, cv2.COLOR_BGR2GRAY)
faces = faceCascade.detectMultiScale(
gray,
scaleFactor=1.1,
minNeighbors=5,
minSize=(30, 30),
flags=cv2.CASCADE_SCALE_IMAGE
)
for (x, y, w, h) in faces:
cv2.rectangle(frames, (x, y), (x+w, y+h), (0, 255, 0), 2)
text = (x+w//2), (y+h//2)
cv2.circle(frames, (cx, cy), radius, (255, 0, 0), -1)
cv2.putText(frames, str(text), org, font, fontScale, color, thickness)
cv2.imshow('Video', frames)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
video_capture.release()
cv2.destroyAllWindows()
With the cv2.getTextSize() function, you can calculate the pixel size of the text you will put and subtract it from the text's position. In this way, the text will be right on the center.
text = (x+w//2), (y+h//2)
text_size,t = cv2.getTextSize(text=str(text), fontFace=cv2.FONT_HERSHEY_DUPLEX, fontScale=1, thickness=1)
text_size_x,text_size_y = text_size
text_pos = (x+w//2)-(text_size_x//2), (y+h//2)+(text_size_y//2)
Here is a working code
import os
import numpy as np
import cv2
font = cv2.FONT_HERSHEY_SIMPLEX
org = (50, 50)
fontScale = 1
color = (255, 0, 0)
radius = 3
thickness = 2
cascPath=os.path.dirname(cv2.__file__)+"/data/haarcascade_frontalface_default.xml"
faceCascade = cv2.CascadeClassifier(cascPath)
video_capture = cv2.VideoCapture(0)
while (True):
ret, frames = video_capture.read()
gray = cv2.cvtColor(frames, cv2.COLOR_BGR2GRAY)
faces = faceCascade.detectMultiScale(
gray,
scaleFactor=1.1,
minNeighbors=5,
minSize=(30, 30),
flags=cv2.CASCADE_SCALE_IMAGE
)
for (x, y, w, h) in faces:
cv2.rectangle(frames, (x, y), (x+w, y+h), (0, 255, 0), 2)
text = (x+w//2), (y+h//2)
text_size,t = cv2.getTextSize(text=str(text), fontFace=cv2.FONT_HERSHEY_DUPLEX, fontScale=1, thickness=1)
text_size_x,text_size_y = text_size
text_pos = (x+w//2)-(text_size_x//2), (y+h//2)+(text_size_y//2)
#cv2.circle(frames, (cx, cy), radius, (255, 0, 0), -1)
cv2.putText(frames, str(text), text_pos, font, fontScale, color, thickness)
cv2.imshow('Video', frames)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
video_capture.release()
cv2.destroyAllWindows()
I am trying to do background subtraction using MOG2, It was working fine, but when there is deep shadow of a moving object then the shadow is considered as foreground object and I don't want that shadow as foreground object (I'm running MOG2 for 13 images). How can I remove these shadow so that it does not come in foreground?
Here is a sample image...
original img
image after applying MOG2
here is my sample code...
import os
import numpy as np
import cv2
import glob
import imutils
i=0
bg_flag = 0
image_list = []
bgs_list = []
#bgsfinal function
def detection(image_list):
global i
global bg_flag
bgs3_img = None
backsub = cv2.createBackgroundSubtractorMOG2(128, cv2.THRESH_BINARY, 1)
print("start2")
for k in range(len(image_list)):
frame = image_list[k]
frame = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
cv2.imwrite('./gray/'+str(k)+'.jpg', frame)
#blur = cv2.medianBlur(frame, 21)
blur = frame
bgs_list.append(blur)
for bg in range(len(bgs_list)):
rects = []
#start_time = time.time()
frame_blur = bgs_list[bg]
img = image_list[bg].copy()
s_frame = image_list[bg]
new_frame = s_frame.copy()
fgmask = backsub.apply(frame_blur)
cv2.imwrite("./bgs/"+str(i)+".jpg", fgmask)
fgmask[fgmask==127] = 0
cv2.imwrite("./dilate/"+str(i)+".jpg", fgmask)
thresh = cv2.threshold(fgmask, 128, 255, cv2.THRESH_BINARY)[1]
thresh = cv2.erode(thresh, None, iterations = 1)
thresh = cv2.dilate(thresh, None, iterations=1)
cnts = cv2.findContours(thresh.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
cnts = imutils.grab_contours(cnts)
for c in cnts:
#M = cv2.moments(c)
A = cv2.contourArea(c)
(x, y, w, h) = cv2.boundingRect(c)
cv2.rectangle(new_frame, (x, y), (x + w, y + h), (0,0, 255), 1)
cv2.putText(new_frame, str(A), (x - 10, y - 10),
cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255), 1)
cv2.imwrite("./area/"+str(i)+".jpg", new_frame)
cv2.rectangle(thresh, (x, y), (x + w, y + h), (255,255, 255), 1)
cv2.putText(thresh, str(A), (x - 10, y - 10),
cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 255, 255), 1)
cv2.imwrite("./area_bgs/"+str(i)+".jpg", thresh)
i+=1
print("Done!")
#this folder contains 13 continuous images
images = glob.glob('./inci4/*.jpg')
for j in range(len(images)):
img = cv2.imread(images[j])
img = cv2.resize(img, (360, 640))
image_list.append(img)
detection(image_list)
Here is my code that I have used for object detection using raspberry pi and Android Ip Camera. Here I'm not getting any output and the code does not provide any errors. Can someone figure out what is the error?
import urllib.request
import cv2
import numpy as np
import datetime
import math
#global variables
width = 0
height = 0
EntranceCounter = 0
ExitCounter = 0
MinCountourArea = 3000 #Adjust ths value according to your usage
BinarizationThreshold = 70 #Adjust ths value according to your usage
OffsetRefLines = 150 #Adjust ths value according to your usage
#Check if an object in entering in monitored zone
def CheckEntranceLineCrossing(y, CoorYEntranceLine, CoorYExitLine):
AbsDistance = abs(y - CoorYEntranceLine)
if ((AbsDistance <= 2) and (y < CoorYExitLine)):
return 1
else:
return 0
#Check if an object in exitting from monitored zone
def CheckExitLineCrossing(y, CoorYEntranceLine, CoorYExitLine):
AbsDistance = abs(y - CoorYExitLine)
if ((AbsDistance <= 2) and (y > CoorYEntranceLine)):
return 1
else:
return 0
This is the code i have used to obtain the video stream from my IP camera
ReferenceFrame = None
while True:
camera=cv2.VideoCapture("http://192.168.1.6:8080/shot.jpg")
camera.set(3,640)
camera.set(4,480)
(ret,Frame)=camera.read()
height = np.size(Frame,0)
width = np.size(Frame,1)
#if cannot grab a frame, this program ends here.
if not ret:
break
This is the code part i have used to display the lines and frame for object detection and object counting
#gray-scale convertion and Gaussian blur filter applying
GrayFrame = cv2.cvtColor(Frame, cv2.COLOR_BGR2GRAY)
GrayFrame = cv2.GaussianBlur(GrayFrame, (21, 21), 0)
if ReferenceFrame is None:
ReferenceFrame = GrayFrame
continue
#Background subtraction and image binarization
FrameDelta = cv2.absdiff(ReferenceFrame, GrayFrame)
FrameThresh = cv2.threshold(FrameDelta, BinarizationThreshold, 255, cv2.THRESH_BINARY)[1]
#Dilate image and find all the contours
FrameThresh = cv2.dilate(FrameThresh, None, iterations=2)
_, cnts, _ = cv2.findContours(FrameThresh.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
QttyOfContours = 0
#plot reference lines (entrance and exit lines)
CoorYEntranceLine = (height / 2)-OffsetRefLines
CoorYExitLine = (height / 2)+OffsetRefLines
cv2.line(Frame, (0,CoorYEntranceLine), (width,CoorYEntranceLine), (255, 0, 0), 2)
cv2.line(Frame, (0,CoorYExitLine), (width,CoorYExitLine), (0, 0, 255), 2)
#check all found countours
for c in cnts:
#if a contour has small area, it'll be ignored
if cv2.contourArea(c) < MinCountourArea:
continue
QttyOfContours = QttyOfContours+1
#draw an rectangle "around" the object
(x, y, w, h) = cv2.boundingRect(c)
cv2.rectangle(Frame, (x, y), (x + w, y + h), (0, 255, 0), 2)
#find object's centroid
CoordXCentroid = (x+x+w)/2
CoordYCentroid = (y+y+h)/2
ObjectCentroid = (CoordXCentroid,CoordYCentroid)
cv2.circle(Frame, ObjectCentroid, 1, (0, 0, 0), 5)
if (CheckEntranceLineCrossing(CoordYCentroid,CoorYEntranceLine,CoorYExitLine)):
EntranceCounter += 1
if (CheckExitLineCrossing(CoordYCentroid,CoorYEntranceLine,CoorYExitLine)):
ExitCounter += 1
print ("Total countours found: "+str(QttyOfContours))
#Write entrance and exit counter values on frame and shows it
cv2.putText(Frame, "Entrances: {}".format(str(EntranceCounter)), (10, 50),
cv2.FONT_HERSHEY_SIMPLEX, 0.5, (250, 0, 1), 2)
cv2.putText(Frame, "Exits: {}".format(str(ExitCounter)), (10, 70),
cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255), 2)
cv2.imshow('Salida',Frame)
cv2.waitKey(1);
# When everything done, release the capture
cap.release()
cv2.destroyAllWindows()
The correct code
import numpy as np
import math
def nothing(x):
pass
width=0
height=0
EntranceCounter = 0
OffsetRefLines = 150
ExitCounter = 0
BinarizationThreshold = 70
MinCountourArea = 3000
cap = cv2.VideoCapture(0);
path="http://192.168.1.6:8080/video"
cap.open(path)
ReferenceFrame = None
#Check if an object in entering in monitored zone
def CheckEntranceLineCrossing(y, CoorYEntranceLine, CoorYExitLine):
AbsDistance = abs(y - CoorYEntranceLine)
if ((AbsDistance <= 2) and (y < CoorYExitLine)):
return 1
else:
return 0
#Check if an object in exitting from monitored zone
def CheckExitLineCrossing(y, CoorYEntranceLine, CoorYExitLine):
AbsDistance = abs(y - CoorYExitLine)
if ((AbsDistance <= 2) and (y > CoorYEntranceLine)):
return 1
else:
return 0
#cv2.namedWindow("Tracking")
cv2.createTrackbar("LH", "Tracking", 0, 255, nothing)
cv2.createTrackbar("LS", "Tracking", 0, 255, nothing)
cv2.createTrackbar("LV", "Tracking", 0, 255, nothing)
cv2.createTrackbar("UH", "Tracking", 255, 255, nothing)
cv2.createTrackbar("US", "Tracking", 255, 255, nothing)
cv2.createTrackbar("UV", "Tracking", 255, 255, nothing)
while True:
#frame = cv2.imread('smarties.png')
if cap.isOpened():
rval, frame = cap.read()
while rval:
rval,frame = cap.read()
hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
hsv = cv2.GaussianBlur(hsv, (21, 21), 0)
if ReferenceFrame is None:
ReferenceFrame = hsv
continue
#Background subtraction and image binarization
FrameDelta = cv2.absdiff(ReferenceFrame, hsv)
FrameThresh = cv2.threshold(FrameDelta, 25, 255, cv2.THRESH_BINARY)[1]
#Dilate image and find all the contours
FrameThresh = cv2.dilate(FrameThresh, None, iterations=2)
cnts, _ = cv2.findContours(FrameThresh, cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE)
QttyOfContours = 0
#plot reference lines (entrance and exit lines)
cv2.line(frame, (0,170), (2000,170), (255, 0, 0), 5)
cv2.line(frame, (0,470), (2000,470), (0, 0, 255), 5)
#check all found countours
for c in cnts:
#if a contour has small area, it'll be ignored
if cv2.contourArea(c) < MinCountourArea:
continue
QttyOfContours = QttyOfContours+1
#draw an rectangle "around" the object
(x, y, w, h) = cv2.boundingRect(c)
cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0), 2)
#find object's centroid
CoordXCentroid = int(x+x+w)/2
CoordYCentroid = int(y+y+h)/2
ObjectCentroid = (x,y)
cv2.circle(frame, ObjectCentroid, 2, (0, 255, 0), 5)
if (CheckEntranceLineCrossing(CoordYCentroid,170,470)):
EntranceCounter += 1
if (CheckExitLineCrossing(CoordYCentroid,170,470)):
ExitCounter += 1
print ("Total countours found: "+str(QttyOfContours))
#Write entrance and exit counter values on frame and shows it
cv2.putText(frame, "Entrances: {}".format(str(EntranceCounter)), (10, 50),
cv2.FONT_HERSHEY_SIMPLEX, 2, (250, 0, 1), 2)
cv2.putText(frame, "Exits: {}".format(str(ExitCounter)), (10, 110),
cv2.FONT_HERSHEY_SIMPLEX, 2, (0, 0, 255), 2)
imS = cv2.resize(frame, (400, 400)) # Resize image
#imSS = cv2.resize(mask, (200, 200))
#imSSS = cv2.resize(frame, (200, 200))
cv2.imshow("frame", imS)
#cv2.imshow("mask", imSS)
#cv2.imshow("res", imSSS)
key = cv2.waitKey(1)
if key == 27:
break
cap.release()
cv2.destroyAllWindows()
My goal is to check the black wire has some tape on them. (the tape with specific color Red, yellow, blue)
Now I can detect the tape on the wire.
But I have no idea how to detect the wire and check they have an intersection area.
This is my code :
import numpy as np
import cv2
def load_image(path_img):
return cv2.imread(path_img)
def bgr2hsv(img):
return cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
def setRangeColor(hsv, lower_color, upper_color):
return cv2.inRange(hsv, lower_color, upper_color)
def contours_img(mask):
contours, _ = cv2.findContours(mask, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
return contours
def filter_contours_img(contours, img_draw, color_bbox):
count = 0
for c in contours:
rect = cv2.boundingRect(c)
x,y,w,h = rect
area = w * h
if area > 1000:
count = count + 1
cv2.rectangle(img_draw, (x, y), (x+w, y+h), color_bbox, 5)
return img_draw, count
def draw_text_on_image(img_draw, count_yellow, count_red, count_blue):
cv2.rectangle(img_draw, (0, 0), (500, 170), (0,0,0), -1)
cv2.putText(img_draw,'Red Count : ' + str(count_red),
(10,50), # bottomLeftCornerOfText
cv2.FONT_HERSHEY_SIMPLEX, # font
1.5, # fontScale
(0,255,255), # fontColor
2) # lineType
cv2.putText(img_draw,'Yellow Count : ' + str(count_yellow),
(10,100), # bottomLeftCornerOfText
cv2.FONT_HERSHEY_SIMPLEX, # font
1.5, # fontScale
(0,255,255), # fontColor
2) # lineType
cv2.putText(img_draw,'blue Count : ' + str(count_blue),
(10,150), # bottomLeftCornerOfText
cv2.FONT_HERSHEY_SIMPLEX, # font
1.5, # fontScale
(0,255,255), # fontColor
2) # lineType
return img_draw
def main():
path_img = 'images/ph14.jpg'
img = load_image(path_img)
#cv2.imshow('img_title',img)
#img = cv2.resize(img, None,fx=0.5,fy=0.5)
hsv = bgr2hsv(img)
img_draw = img
# define range of Yellow color in HSV
lower_Yellow = np.array([20,100,100])
upper_Yellow = np.array([30,255,255])
mask = setRangeColor(hsv, lower_Yellow, upper_Yellow)
contours = contours_img(mask)
color_bbox = (0, 255, 0)
img_draw, count_yellow = filter_contours_img(contours, img_draw, color_bbox)
print('Yellow Count:', count_yellow)
# define range of Red color in HSV
lower_Red = np.array([0,125,125])
upper_Red = np.array([10,255,255])
mask0 = setRangeColor(hsv, lower_Red, upper_Red)
contours = contours_img(mask0)
color_bbox = (0, 125, 255)
img_draw, count_red0 = filter_contours_img(contours, img_draw, color_bbox)
#print('Red Count:', count_red)
# upper mask (170-180)
lower_red = np.array([170,100,100]) #170
upper_red = np.array([180,255,255]) #180
mask1 = cv2.inRange(hsv, lower_red, upper_red)
contours = contours_img(mask1)
color_bbox = (0, 125, 255)
img_draw, count_red1 = filter_contours_img(contours, img_draw, color_bbox)
count_red = count_red0 + count_red1
print('Red Count:', count_red)
# define range of Blue color in HSV
lower_Blue = np.array([100,125,125])
upper_Blue = np.array([125,255,255])
mask = setRangeColor(hsv, lower_Blue, upper_Blue)
contours = contours_img(mask)
color_bbox = (255, 0, 255)
img_draw, count_blue = filter_contours_img(contours, img_draw, color_bbox)
img_draw = draw_text_on_image(img_draw, count_yellow, count_red, count_blue)
print('Blue Count:', count_blue)
cv2.imwrite('output/output_IMG_ph14.png', img_draw)
if __name__ == '__main__':
main()
I think my final output can show the result like this below. (The Green color line is not necessary.)
This is over detected picture ** (red is detected more than 1)
So in this case, **I think if I can detect the black wire I will able to check
that detection is correct.
enter image description here