[Python] smooth edge of piece

This is the code where I perform thresholding on an image to detect the piece and I would like to be able to smooth the edge even more.

I've never worked with Python, but I'd be interested in doing this project to find a solution to a puzzle. Therefore, I need the edges of the piece to be as smooth as possible.

from PIL import Image, ExifTags, ImageFilter

EXPECTED_PHOTO_ORIENTATION = 1 # Horizontal (normal)

def get_photo_orientation(img):
exif = img._getexif()
if exif:
for tag, value in exif.items():
if tag in ExifTags.TAGS:
if ExifTags.TAGS[tag] == 'Orientation':
return value
return None


def binary_pixel_data_for_photo(path, threshold, max_width=None, crop=None):
"""
Given a bitmap image path, returns a 2D array of 1s and 0s
crop is either None or (top, right, bottom, left) in pixels
"""
with Image.open(path) as img:
if (orientation := get_photo_orientation(img)) is not None and orientation != EXPECTED_PHOTO_ORIENTATION:
raise Exception(f"Image {path} is not oriented correctly: {orientation}")

w, h = img.size

if max_width is not None and img.size[0] > max_width:
scale_factor = max_width / img.size[0]
try:
img = img.resize((max_width, int(img.size[1] * scale_factor)), resample=Image.NEAREST)
except Exception as e:
print(f"Error resizing {path}")
raise e
else:
scale_factor = 1.0

if crop:
w, h = img.size
img = img.crop((crop[3], crop[0], w - crop[1], h - crop[2]))


data, out_w, out_h = threshold_pixels(img, threshold)
return data, out_w, out_h, scale_factor


def threshold_pixels(img, threshold):
# Convert image to grayscale numpy array
grayscale = img.convert('L')
data = np.array(grayscale)

# Aplicar convolución
filtered = cv2.GaussianBlur(data, (9, 9), sigmaX=0)

# Apply threshold to get binary representation
binary_data = np.where(filtered <= threshold, 0, 1).astype(np.int8)
return binary_data, binary_data.shape[1], binary_data.shape[0]


binary_data, out_w, out_h, scale_factor = binary_pixel_data_for_photo("ejemplo.jpeg", 100)

binary_uint8 = binary_data.astype(np.uint8) * 255

contours, _ = cv2.findContours(binary_uint8, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)

if contours:
largest = max(contours, key=cv2.contourArea)
contour_img = np.zeros_like(binary_uint8)
cv2.drawContours(contour_img, [largest], 0, 255, -1)

# Mostrar resultado
plt.imshow(cv2.imread("ejemplo.jpeg"))
plt.title("Imagen original")
plt.axis("off")
plt.show()

plt.figure(figsize=(6, 8))
plt.imshow(contour_img)
plt.title("Contorno de la pieza")
plt.axis("off")
plt.show()








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