Find Slider CAPTCHA Position with OpenCV Template Matching

Template Matching

Wiki: Template_matching

The technique we’ll use is called Template Matching. Basically, it slides a small image — the template — across a larger image, and measures how similar each region is.

OpenCV provides the cv.matchTemplate() method, which makes Template Matching easy, You can learn more in template matching tutorial.

Find Slider CAPTCHA Position

def find_gap_position(background_img, template_img):
    """
    Use Template Matching to find the slider gap position
    """
    # Convert to grayscale
    bg_gray = cv2.cvtColor(background_img, cv2. COLOR_BGR2GRAY)
    template_gray = cv2.cvtColor(template_img, cv2. COLOR_BGR2GRAY)

    # Template matching
    result = cv2.matchTemplate(bg_gray, template_gray, cv2. TM_CCOEFF_NORMED)

    # Find the best match position
    _, max_val, _, max_loc = cv2.minMaxLoc(result)
    return max_loc[0], max_val  # Return x coordinate and match confidence

Using this pair of sample images as an example (sample1:bg&template):

background = cv2.imread("sample1-bg.png")
template = cv2.imread("sample1-template.png")

gap_x, confidence = find_gap_position(background, template)
print(
    f"Template matching result - X coordinate: {gap_x}, Confidence: {confidence:.4f}")
## Output
# Template matching result - X coordinate: 169, Confidence: 0.3312

Use the verify_gap_position function to check whether the template match position is correct:

def verify_gap_position(background, template, gap_x, save_path):
    """
    Create verification image showing the found gap position
    """
    # Create verification image: overlay template onto background image
    result_img = background.copy()
    template_height, template_width = template.shape[:2]

    # Add red border to template
    template_with_border = template.copy()
    cv2.rectangle(template_with_border, (0, 0),
                  (template_width-1, template_height-1), (0, 0, 255), 1)

    # Overlay bordered template onto background at found position
    # Use weighted blending to make template semi-transparent
    if gap_x + template_width <= background.shape[1] and template_height <= background.shape[0]:
        roi = result_img[0:template_height, gap_x:gap_x+template_width]
        # 0.85 background transparency, 0.15 template transparency
        blended = cv2.addWeighted(roi, 0.3, template_with_border, 0.7, 0)
        result_img[0:template_height, gap_x:gap_x+template_width] = blended

    # Save verification image
    cv2.imwrite(save_path, result_img)
    print(f"Verification image saved as {save_path}")

Verify sample1 result：

Test more samples

The recognition is very accurate!!!