Programmiererforum

import cv2

# Load the image
image = cv2.imread("image.png")  # Update with your image path
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
hsv = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)

# Define refined HSV ranges for sandy ground (light brown, beige, gray)
lower_sand = np.array([10, 20, 100])  # Lower bound for sand-like colors
upper_sand = np.array([30, 100, 255])  # Upper bound for sand-like colors

# Create a mask for the sandy competition ground
mask_sand = cv2.inRange(hsv, lower_sand, upper_sand)

# Apply morphological operations to reduce noise
kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (7, 7))
mask_sand = cv2.morphologyEx(mask_sand, cv2.MORPH_CLOSE, kernel)  # Close gaps
mask_sand = cv2.morphologyEx(mask_sand, cv2.MORPH_OPEN, kernel)   # Remove small noise

# Invert the mask (to highlight everything except the competition area)
mask = cv2.bitwise_not(mask_sand)

# Apply the inverted mask to the original image
output = cv2.bitwise_and(image, image, mask=mask)

# Ensure the mask is properly binarized for contour detection
ret, thresh = cv2.threshold(mask, 50, 255, cv2.THRESH_BINARY)

# Find contours
contours, hierarchy = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)

if contours:
# Find the largest contour by area
largest_contour = max(contours, key=cv2.contourArea)

# Approximate the contour to a quadrilateral
epsilon = 0.02 * cv2.arcLength(largest_contour, True)
approx = cv2.approxPolyDP(largest_contour, epsilon, True)

# If the approximation has 4 points, use it; otherwise, use convex hull
if len(approx) == 4:
quadrilateral = approx
else:
quadrilateral = cv2.convexHull(largest_contour)

# Draw the quadrilateral in green
cv2.polylines(output, [quadrilateral], isClosed=True, color=(0, 255, 0), thickness=3)

# Draw all detected contours in blue
cv2.drawContours(output, contours, -1, (255, 0, 0), 2)

# Show and save the output
cv2.imshow("Result", np.hstack([image, output]))
cv2.imwrite("output_image.png", output)

cv2.waitKey(0)
cv2.destroyAllWindows()```

Ich kämpfe darum, den Begrenzungsbox für einen identifizierten Bereich in einem Bild zu erhalten. Der folgende Code führt zu diesem Bild: https://i.static.net/6hx4dnrb.png. Wie aus dem Bild hervorgeht, möchte ich jedoch den (schwarzen) identifizierten großen Bereich einschließen. Irgendwelche Vorschläge, wie man das erreicht? /p>
[code]import cv2

# Load the image
image = cv2.imread("image.png") # Update with your image path
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
hsv = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)

# Define refined HSV ranges for sandy ground (light brown, beige, gray)
lower_sand = np.array([10, 20, 100]) # Lower bound for sand-like colors
upper_sand = np.array([30, 100, 255]) # Upper bound for sand-like colors

# Create a mask for the sandy competition ground
mask_sand = cv2.inRange(hsv, lower_sand, upper_sand)

# Apply morphological operations to reduce noise
kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (7, 7))
mask_sand = cv2.morphologyEx(mask_sand, cv2.MORPH_CLOSE, kernel) # Close gaps
mask_sand = cv2.morphologyEx(mask_sand, cv2.MORPH_OPEN, kernel) # Remove small noise

# Invert the mask (to highlight everything except the competition area)
mask = cv2.bitwise_not(mask_sand)

# Apply the inverted mask to the original image
output = cv2.bitwise_and(image, image, mask=mask)

# Ensure the mask is properly binarized for contour detection
ret, thresh = cv2.threshold(mask, 50, 255, cv2.THRESH_BINARY)

# Find contours
contours, hierarchy = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)

if contours:
# Find the largest contour by area
largest_contour = max(contours, key=cv2.contourArea)

# Approximate the contour to a quadrilateral
epsilon = 0.02 * cv2.arcLength(largest_contour, True)
approx = cv2.approxPolyDP(largest_contour, epsilon, True)

# If the approximation has 4 points, use it; otherwise, use convex hull
if len(approx) == 4:
quadrilateral = approx
else:
quadrilateral = cv2.convexHull(largest_contour)

# Draw the quadrilateral in green
cv2.polylines(output, [quadrilateral], isClosed=True, color=(0, 255, 0), thickness=3)

# Draw all detected contours in blue
cv2.drawContours(output, contours, -1, (255, 0, 0), 2)

# Show and save the output
cv2.imshow("Result", np.hstack([image, output]))
cv2.imwrite("output_image.png", output)

cv2.waitKey(0)
cv2.destroyAllWindows()```
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