Extract all bounding boxes using OpenCV Python

there you go:

import cv2

im = cv2.imread('c:/data/ph.jpg')
gray=cv2.cvtColor(im,cv2.COLOR_BGR2GRAY)
contours, hierarchy = cv2.findContours(gray,cv2.RETR_LIST,cv2.CHAIN_APPROX_SIMPLE)[-2:]
idx =0 
for cnt in contours:
    idx += 1
    x,y,w,h = cv2.boundingRect(cnt)
    roi=im[y:y+h,x:x+w]
    cv2.imwrite(str(idx) + '.jpg', roi)
    #cv2.rectangle(im,(x,y),(x+w,y+h),(200,0,0),2)
cv2.imshow('img',im)
cv2.waitKey(0)    

A simple approach is to find contours, obtain the bounding rectangle coordinates using cv2.boundingRect() then extract the ROI using Numpy slicing. We can keep a counter to save each ROI then save it with cv2.imwrite(). Here's a working example:

Input image:

Detected ROIs to extract highlighted in green

Saved ROIs

Code

import cv2
import numpy as np

# Load image, grayscale, Otsu's threshold 
image = cv2.imread('1.png')
original = image.copy()
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
thresh = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU)[1]

# Find contours, obtain bounding box, extract and save ROI
ROI_number = 0
cnts = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
cnts = cnts[0] if len(cnts) == 2 else cnts[1]
for c in cnts:
    x,y,w,h = cv2.boundingRect(c)
    cv2.rectangle(image, (x, y), (x + w, y + h), (36,255,12), 2)
    ROI = original[y:y+h, x:x+w]
    cv2.imwrite('ROI_{}.png'.format(ROI_number), ROI)
    ROI_number += 1

cv2.imshow('image', image)
cv2.waitKey()