[CarND_classifiers] #CarND #classifiers

add_bounding_box.py

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

image = mpimg.imread('bbox-example-image.jpg')

# Define a function that takes an image, a list of bounding boxes, 
# and optional color tuple and line thickness as inputs
# then draws boxes in that color on the output

def draw_boxes(img, bboxes, color=(0, 0, 255), thick=6):
    # make a copy of the image
    draw_img = np.copy(img)
    # draw each bounding box on your image copy using cv2.rectangle()
    # return the image copy with boxes drawn
    for box in bboxes:
        (x1, y1), (x2, y2) = box
        cv2.rectangle(draw_img, (x1, y1), (x2, y2), color, thick)
    return draw_img # Change this line to return image copy with boxes
# Add bounding boxes in this format, these are just example coordinates.
bboxes = [((300, 500), (380, 560)), ((300, 300), (400, 400))]

result = draw_boxes(image, bboxes)
plt.imshow(result)

color_histogram.py

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

image = mpimg.imread('cutout1.jpg')


# Define a function to compute color histogram features  
def color_hist(img, nbins=32, bins_range=(0, 256)):
    # Compute the histogram of the RGB channels separately
    rhist = np.histogram(img[:,:,0], bins=32, range=(0, 256))
    ghist = np.histogram(img[:,:,1], bins=32, range=(0, 256))
    bhist = np.histogram(img[:,:,2], bins=32, range=(0, 256))
    # Generating bin centers
    bin_edges = rhist[1]
    bin_centers = (bin_edges[1:]  + bin_edges[0:len(bin_edges)-1])/2
    # Concatenate the histograms into a single feature vector
    hist_features = np.concatenate((rhist[0], ghist[0], bhist[0]))
    # Return the individual histograms, bin_centers and feature vector
    return rhist, ghist, bhist, bin_centers, hist_features
    
rh, gh, bh, bincen, feature_vec = color_hist(image, nbins=32, bins_range=(0, 256))

# Plot a figure with all three bar charts
if rh is not None:
    fig = plt.figure(figsize=(12,3))
    plt.subplot(131)
    plt.bar(bincen, rh[0])
    plt.xlim(0, 256)
    plt.title('R Histogram')
    plt.subplot(132)
    plt.bar(bincen, gh[0])
    plt.xlim(0, 256)
    plt.title('G Histogram')
    plt.subplot(133)
    plt.bar(bincen, bh[0])
    plt.xlim(0, 256)
    plt.title('B Histogram')
    fig.tight_layout()
else:
    print('Your function is returning None for at least one variable...')

naive_bayes_and_svm.py

from sklearn.svm import SVC

# gamma controls margin size (in loss function)
# C controls classfying error penalty

clf = SVC(C=1.0, gamma=1000)

from sklearn.naive_bayes import GaussianNB

clf = GaussianNB()

clf.fit(...)

pred = clf.predict(...)

resize_and_cvt.py

# Define a function to compute color histogram features  
# Pass the color_space flag as 3-letter all caps string
# like 'HSV' or 'LUV' etc.
def bin_spatial(img, color_space='RGB', size=(32, 32)):
    # Convert image to new color space (if specified)
    if color_space != 'RGB':
        if color_space == 'HSV':
            feature_image = cv2.cvtColor(img, cv2.COLOR_RGB2HSV)
        elif color_space == 'LUV':
            feature_image = cv2.cvtColor(img, cv2.COLOR_RGB2LUV)
        elif color_space == 'HLS':
            feature_image = cv2.cvtColor(img, cv2.COLOR_RGB2HLS)
        elif color_space == 'YUV':
            feature_image = cv2.cvtColor(img, cv2.COLOR_RGB2YUV)
        elif color_space == 'YCrCb':
            feature_image = cv2.cvtColor(img, cv2.COLOR_RGB2YCrCb)
    else: feature_image = np.copy(img)             
    # Use cv2.resize().ravel() to create the feature vector
    features = cv2.resize(feature_image, size).ravel() 
    # Return the feature vector
    return features

template_matching.py

# Define a function to search for template matches
# and return a list of bounding boxes
def find_matches(img, template_list):
    # Define an empty list to take bbox coords
    bbox_list = []
    # Define matching method
    # Other options include: cv2.TM_CCORR_NORMED', 'cv2.TM_CCOEFF', 'cv2.TM_CCORR',
    #         'cv2.TM_SQDIFF', 'cv2.TM_SQDIFF_NORMED'
    method = cv2.TM_CCOEFF_NORMED
    # Iterate through template list
    for temp in template_list:
        # Read in templates one by one
        tmp = mpimg.imread(temp)
        # Use cv2.matchTemplate() to search the image
        result = cv2.matchTemplate(img, tmp, method)
        # Use cv2.minMaxLoc() to extract the location of the best match
        min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(result)
        # Determine a bounding box for the match
        w, h = (tmp.shape[1], tmp.shape[0])
        if method in [cv2.TM_SQDIFF, cv2.TM_SQDIFF_NORMED]:
            top_left = min_loc
        else:
            top_left = max_loc
        bottom_right = (top_left[0] + w, top_left[1] + h)
        # Append bbox position to list
        bbox_list.append((top_left, bottom_right))
        # Return the list of bounding boxes
        
    return bbox_list