find a white rhombus bigger than 500 pixels, and highlight it in python in python in python

main.py
import cv2

# Load the image
image = cv2.imread('image.jpg')

# Convert the image to grayscale
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)

# Apply Canny edge detection algorithm
edges = cv2.Canny(gray, 50, 200)

# Apply Hough line transformation to detect lines in the image
lines = cv2.HoughLines(edges, 1, np.pi/180, 200)

# Filter out all the lines with angles close to 45 or 135 degrees
filtered_lines = []
for line in lines:
    rho, theta = line[0]
    angle = np.degrees(theta)
    if (np.abs(45 - angle) < 10) or (np.abs(135 - angle) < 10):
        filtered_lines.append(line)

# Find intersection points to get possible rhombus vertices
vertices = []
for i in range(len(filtered_lines)):
    for j in range(i+1, len(filtered_lines)):
        rho1, theta1 = filtered_lines[i][0]
        rho2, theta2 = filtered_lines[j][0]
        A = np.array([
            [np.cos(theta1), np.sin(theta1)],
            [np.cos(theta2), np.sin(theta2)]
        ])
        b = np.array([
            [rho1],
            [rho2]
        ])
        intersection = np.linalg.solve(A, b)
        intersection = np.round(intersection).astype(int)
        vertices.append(intersection)

# Calculate the distances between the vertices and filter out all the rhombuses with sides smaller than 500 pixels.
for i in range(len(vertices)):
    for j in range(i+1, len(vertices)):
        for k in range(j+1, len(vertices)):
            for l in range(k+1, len(vertices)):
                side1 = np.linalg.norm(vertices[i] - vertices[j])
                side2 = np.linalg.norm(vertices[j] - vertices[k])
                side3 = np.linalg.norm(vertices[k] - vertices[l])
                side4 = np.linalg.norm(vertices[l] - vertices[i])
                sides = [side1, side2, side3, side4]
                if all(side > 500 for side in sides):
                    # Found a rhombus, draw a rectangle around it
                    cv2.rectangle(image, tuple(vertices[i]), tuple(vertices[k]), (0, 0, 255), 2)

# Display the image
cv2.imshow('image', image)
cv2.waitKey(0)
cv2.destroyAllWindows()
2075 chars
59 lines