utilities.py

import cv2
import numpy as np
from glob import glob


def get_training_data():
    cars = glob('./training_data/vehicles/*/*.png')
    notcars = glob('./training_data/non-vehicles/*/*.png')
    return cars, notcars


# Define a function to compute binned color features
def bin_spatial(img, size=(32, 32)):
    # Use cv2.resize().ravel() to create the feature vector
    features = cv2.resize(img, size).ravel()
    # Return the feature vector
    return features

# def bin_spatial(img, size=(32, 32)):
#     color1 = cv2.resize(img[:,:,0], size).ravel()
#     color2 = cv2.resize(img[:,:,1], size).ravel()
#     color3 = cv2.resize(img[:,:,2], size).ravel()
#     return np.hstack((color1, color2, color3))


# Define a function to compute color histogram features
# NEED TO CHANGE bins_range if reading .png files with mpimg!
def color_hist(img, nbins=32, bins_range=(0, 256)):

    # Compute the histogram of the color channels separately
    channel1_hist = np.histogram(img[:, :, 0], bins=nbins, range=bins_range)
    channel2_hist = np.histogram(img[:, :, 1], bins=nbins, range=bins_range)
    channel3_hist = np.histogram(img[:, :, 2], bins=nbins, range=bins_range)

    # Concatenate the histograms into a single feature vector
    hist_features = np.concatenate((channel1_hist[0], channel2_hist[0], channel3_hist[0]))

    # Return the individual histograms, bin_centers and feature vector
    return hist_features


def convert_color(img, conv='RGB2YCrCb'):
    if conv == 'RGB2YCrCb':
        return cv2.cvtColor(img, cv2.COLOR_RGB2YCrCb)
    if conv == 'BGR2YCrCb':
        return cv2.cvtColor(img, cv2.COLOR_BGR2YCrCb)
    if conv == 'RGB2LUV':
        return cv2.cvtColor(img, cv2.COLOR_RGB2LUV)


def draw_boxes(img, bboxes, color=(0, 0, 255), thick=6):
    # make a copy of the image
    draw_img = np.copy(img)
    # Iterate through the bounding boxes
    for bbox in bboxes:
        # Draw a rectangle given bbox coordinates
        cv2.rectangle(draw_img, bbox[0], bbox[1], color, thick)
    # draw each bounding box on your image copy using cv2.rectangle()
    # return the image copy with boxes drawn
    return draw_img # Change this line to return image copy with boxes


from random import shuffle

if __name__ == "__main__":
    cars, notcars = get_training_data()
    shuffle(cars)
    for car in cars[0:10]:
        print(car)