Using python opencv easily can crop image or center crop image. Object cropping is little bit complex. But it's possible using opencv. For single object image without noise, this technique is work better. It's more better for cutout or removed background imges.

Code:

First install python opencv using pip install opencv-python

# import opencv
import cv2
import numpy as np

# Read an image
# input_image = cv2.imread(path, cv2.IMREAD_UNCHANGED)
input_image = cv2.imread("image.jpeg", cv2.IMREAD_UNCHANGED)

height = input_image.shape[0]
width = input_image.shape[1]

# Checking image is grayscale or not. If image shape is 2 then gray scale otherwise not
if len(input_image.shape) == 2:
    gray_input_image = input_image.copy()
else:
    # Converting BGR image to grayscale image
    gray_input_image = cv2.cvtColor(input_image, cv2.COLOR_BGR2GRAY)

# To find upper threshold, we need to apply Otsu's thresholding
upper_threshold, thresh_input_image = cv2.threshold(
    gray_input_image, thresh=0, maxval=255, type=cv2.THRESH_BINARY + cv2.THRESH_OTSU
)
# Calculate lower threshold
lower_threshold = 0.5 * upper_threshold

# Apply canny edge detection
canny = cv2.Canny(input_image, lower_threshold, upper_threshold)
# Finding the non-zero points of canny
pts = np.argwhere(canny > 0)

# Finding the min and max points
y1, x1 = pts.min(axis=0)
y2, x2 = pts.max(axis=0)

# Crop ROI from the givn image
output_image = input_image[y1:y2, x1:x2]


# Save image
cv2.imwrite(output_path, output_image)

N.B.: It's properly working for noiseless and single background images. After background removal, for a clean background, it works better.

Input:

image.jpeg ( in same folder in python file)

Output: