create sliding windows to identify objects

sliding_window.py

import numpy as np
import cv2
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
% matplotlib inline

image = mpimg.imread('bbox-example-image.jpg')
def draw_boxes(img, bboxes, color=(0, 0, 255), thick=6):
    imcopy = np.copy(img)
    for bbox in bboxes:
        cv2.rectangle(imcopy, bbox[0], bbox[1], color, thick)
    return imcopy
    
    
def slide_window(img, x_start,x_stop, y_start,y_stop, 
                    xy_window=(64, 64), xy_overlap=(0.5, 0.5)):

    # Overlap pixels, which slows down the window moving
    x_overlap = np.int(xy_window[0]*(xy_overlap[0]))
    y_overlap = np.int(xy_window[1]*(xy_overlap[1]))    
    # Compute the effective moving pixels per step
    nx_pix_per_step = xy_window[0]- x_overlap
    ny_pix_per_step = xy_window[1]- y_overlap
    # Compute the number of windows in x/y
    nx_windows = np.int((x_stop - x_start-x_overlap)/nx_pix_per_step) 
    ny_windows = np.int((y_stop - y_start-y_overlap)/ny_pix_per_step) 

    window_list = []
    for y in range(ny_windows):
        for x in range(nx_windows):       
            startx = x * nx_pix_per_step + x_start
            endx = startx + xy_window[0]
            starty = y * ny_pix_per_step + y_start
            endy = starty + xy_window[1]      
            window_list.append(((startx, starty), (endx, endy)))
    return window_list

y_height, x_width, channel = image.shape
windows = slide_window(image, 0,x_width, 0, y_height, 
                    xy_window=(128, 128), xy_overlap=(0.5, 0.5))
                       
window_img = draw_boxes(image, windows, color=(0, 0, 255), thick=6)                    
plt.imshow(window_img)
plt.show()