Hello
My name is Felipe and I'm from Chile. I'm currently making a project with 2 points and measuring the dinstance between them in real time. I know it can be done with images, but I can't find a real solution with videos.
I made a mask and then erotionate to recognize the colours that I need. I think that the next step is labeling them, and then get the distance.
The two objects I will move them up and down, and I need to measure the distance between them in real time.
Here is an image to show how it shows until now.
-------------------------EDIT-----------------------------
Now i can recognize an measure "distance" beetween the 2 points, but the values that I get appears to be in pixel values not in cm or inches. I dont know if somebody con confirmate that.
Now I have to export that values to a txt or csv file, because I need to send it in real time trough google drive in a spreedsheet.
So my questions now if you can help me with export that values in a file, and if is possible to show the values more slow, i mean like one value per second, because it shows so fast, and propably it will be so fast to the spreadsheet. (I hope you can understand my english hahaha)
Thank you again to everyone who respond.
my code is currently this
import cv2
import numpy as np
#Captura de video a traves de la webcam
cap=cv2.VideoCapture(0)
while(1):
d=0.1
centers=[]
_, img = cap.read()
hsv=cv2.cvtColor(img,cv2.COLOR_BGR2HSV) #Se obtiene un histograma basada en las saturaciones de colores.
blue_lower=np.array([80,150,100],np.uint8)
blue_upper=np.array([150,255,255],np.uint8)
blue=cv2.inRange(hsv,blue_lower,blue_upper) #Se crea una mascara utilizando intervalos de color azul.
kernal = np.ones((5 ,5), "uint8") #Crea una matriz de 5x5 la cual recorrera el video,
blue=cv2.erode(blue,kernal, iterations=1) #Se erosiona utilizando el kernel sobre la mascara.
res1=cv2.bitwise_and(img, img, mask = blue) #La nueva imagen reemplazara a blue.
(_,contours,hierarchy)=cv2.findContours(blue,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE) #Encuentra los contornos de los objetos que se ven en el filtro
for pic, contour in enumerate(contours):
area = cv2.contourArea(contour) #funcion de opencv que obtiene los contornos
if(area>300):
x,y,w,h = cv2.boundingRect(contour) #Encuentra coordenadas de los contornos.
img = cv2.rectangle(img,(x,y),(x+w,y+h),(255,0,0),2)
cv2.putText(img,"Marcador",(x,y),cv2.FONT_HERSHEY_SIMPLEX, 0.7, (255,0,0))
M = cv2.moments(contour) #Se obtiene el centro de masa de los marcadores enconrados.
cx = int(M['m10'] /M['m00'])
cy = int(M['m01'] /M['m00'])
centers.append([cx,cy])
cv2.circle(img, (cx, cy), 7, (255, 255, 255), -1)
if len(centers)==2:
D = np.linalg.norm(cx-cy) #Se aplica distancia euclidiana para encontrar la distancia entre los centros de masa.
print(D)
cv2.imshow("Color Tracking",img)
if cv2.waitKey(10) & 0xFF == ord('q'):
cap.release()
cv2.destroyAllWindows()
break
