I’m new to CV, and I’m trying to detect an image in a computer screen. My approach is using Hough line transform to get possible edges that form the image. I was wondering why the standard cv2.HoughLines() doesn’t work as well. Any insight helps. Thank you!

Probabilistic Hough Line Transform code:

lines = cv2.HoughLinesP(
                edges, # Input edge image
                1, # Distance resolution in pixels
                np.pi/180, # Angle resolution in radians
                threshold=300, # Min number of votes for valid line
                minLineLength=300, # Min allowed length of line
                maxLineGap=10 # Max allowed gap between line for joining them
                )

edges_color = cv2.cvtColor(edges, cv2.COLOR_GRAY2BGR)

for points in lines:
    x1,y1,x2,y2=points[0]
    # Draw the lines joing the points
    # On the original image
    cv2.line(edges_color,(x1,y1),(x2,y2),(0,255,0),3)

Probabilistic Hough Line Transform result:
enter image description here

Standard Hough Line Transform code:

    lines = cv2.HoughLines(
                edges, # Input edge image
                1, # Distance resolution in pixels
                np.pi/180, # Angle resolution in radians
                threshold=300, # Min number of votes for valid line
                )
    
    edges_color = cv2.cvtColor(edges, cv2.COLOR_GRAY2BGR)
    for line in lines:
        for rho,theta in line:
            a=np.cos(theta)
            b=np.sin(theta)
            x0=a*rho
            y0=b*rho
            x1=int(x0+1000*(-b))
            y1=int(y0+1000*(a))
            x2=int(x0-1000*(-b))
            y2=int(y0-1000*(a))
            cv2.line(edges_color,(x1,y1),(x2,y2),(255,0,0),2)

Standard Hough Line Transform result:
enter image description here