Не получается пережать скриншот jpeg'ом

В коде ниже нужно уменьшить количество оригинальных цветов на изображении перед их кластеризацией с помошью k-means и последующим сокращением оригинальных цветов до k. Без пережатия кластеризация даже на малом изображении будет чудовещно долгая, так что я решил пережать png в jpeg с 30% качеством. Вроде все сделал по мануалу на сайте opencv, но строчка с декодированием почему-то выдает ошибку. Подскажите почему?

Код:

from google.colab.patches import cv2_imshow
from google.colab import drive
drive.mount('/content/gdrive')
import cv2
import numpy as np
def prepare_screenshot(img, quality, crop_y, crop_x, crop_h, crop_w):
    img = img[crop_y:crop_h, crop_x:crop_w]
    encodedImg = cv2.imencode('.jpg', img, params=[int(cv2.IMWRITE_JPEG_QUALITY), quality])
    decodedImg = cv2.imdecode(encodedImg, cv2.IMREAD_COLOR)
    decodedImg = cv2.resize(img, None, fx=0.125, fy=0.125)
    return img

def initialize_means(img, clusters):
    points = img.reshape((-1, img.shape[2]))
    m, n = points.shape
    means = np.zeros((clusters, n))
    for i in range(clusters):
        rand_indieces = np.random.choice(m, size=10, replace=False)
        means[i] = np.mean(points[rand_indieces], axis=0)
    return points, means

def distance(x1, y1, x2, y2):
    dist = np.square(x1 - x2) + np.square(y1 - y2)
    dist = np.sqrt(dist)
    return dist

def k_means(points, means, clusters):
    iterations = 5
    m, n = points.shape
    index = np.zeros(m)

    while iterations > 0:
        for j in range(m):
            min_dist = float('inf')
            temp = None

            for k in range(clusters):
                x1, y1 = points[j, 0], points[j, 1]
                x2, y2 = means[k, 0], means[k, 1]

                if distance(x1, y1, x2, y2)<= min_dist:
                    min_dist = distance(x1, y1, x2, y2)
                    temp = k
                    index[j] = k
        for k in range(clusters):
            cluster_points = points[index==k]
            if len(cluster_points) > 0:
                means[k] = np.mean(cluster_points, axis=0)
        iterations -= 1
    return means, index

def compress_image(means, index, img):
    centroid = np.array(means)
    recovered = centroid[index.astype(int), :]
    recovered = recovered.reshape(img.shape)
    return recovered

screenShot = cv2.imread('/content/gdrive/MyDrive/Screenshot.png')
resizedImage = prepare_screenshot(screenShot, 30, 156, 320, 924, 1600)
resizedImage = cv2.resize(resizedImage, None, fx=4.0, fy=4.0,interpolation=cv2.INTER_NEAREST)
cv2_imshow(resizedImage)

points, means = initialize_means(resizedImage, 32)
means, index = k_means(points, means, 32)
pastirizedImg = compress_image(means, index, resizedImage)

pastirizedImg = cv2.resize(pastirizedImg, None, fx=4.0, fy=4.0,interpolation=cv2.INTER_NEAREST)
cv2_imshow(pastirizedImg)

Ошибка:

error                                     Traceback (most recent call last)
/tmp/ipython-input-1103729291.py in <cell line: 0>()
57
58 screenShot = cv2.imread('/content/gdrive/MyDrive/Screenshot.png')
---> 59 resizedImage = prepare_screenshot(screenShot, 30, 156, 320, 924, 1600)
60 resizedImage = cv2.resize(resizedImage, None, fx=4.0, fy=4.0,interpolation=cv2.INTER_NEAREST)
61 cv2_imshow(resizedImage)

/tmp/ipython-input-1103729291.py in prepare_screenshot(img, quality, crop_y, crop_x, crop_h, crop_w)
7   img = img[crop_y:crop_h, crop_x:crop_w]
8   encodedImg = cv2.imencode('.jpg', img, params=[int(cv2.IMWRITE_JPEG_QUALITY), quality])
----> 9   decodedImg = cv2.imdecode(encodedImg, cv2.IMREAD_COLOR)
10   decodedImg = cv2.resize(img, None, fx=0.125, fy=0.125)
11   return img

error: OpenCV(4.12.0) :-1: error: (-5:Bad argument) in function 'imdecode'
> Overload resolution failed:
>  - buf is not a numerical tuple
>  - Expected Ptr<cv::UMat> for argument 'buf'