util.py

import numpy as np
import cv2

# Define a function that takes an image,
# start and stop positions in both x and y, 
# window size (x and y dimensions),  
# and overlap fraction (for both x and y)
def slide_window(img, x_start_stop=[None, None], y_start_stop=[None, None], 
                    xy_window=(64, 64), xy_overlap=(0.5, 0.5)):
    # If x and/or y start/stop positions not defined, set to image size
    # Compute the span of the region to be searched    
    # Compute the number of pixels per step in x/y
    # Compute the number of windows in x/y
    # Initialize a list to append window positions to
    if x_start_stop[0] == None:
        x_start_stop[0] = 0
    if x_start_stop[1] == None:
        x_start_stop[1] = img.shape[1]
    if y_start_stop[0] == None:
        y_start_stop[0] = 0
    if y_start_stop[1] == None:
        y_start_stop[1] = img.shape[0]
        
    window_list = []
    w, h = xy_window
    xstep, ystep = [int(window*overlap) for window,overlap in zip(xy_window, xy_overlap)]
    # Loop through finding x and y window positions
    for xpos in range(x_start_stop[0], x_start_stop[1]-w+1, xstep):
        for ypos in range(y_start_stop[0], y_start_stop[1]-h+1, ystep):
			# Append window position to list
            window_list.append(((xpos, ypos), (xpos + w, ypos + h)))       
    # Return the list of windows
    return window_list

# Define a function to draw bounding boxes
def draw_boxes(img, bboxes, color=(0, 0, 255), thick=6):
    # Make a copy of the image
    imcopy = np.copy(img)
    # Iterate through the bounding boxes
    for bbox in bboxes:
        # Draw a rectangle given bbox coordinates
        cv2.rectangle(imcopy, bbox[0], bbox[1], color, thick)
    # Return the image copy with boxes drawn
    return imcopy