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helper_functions.py
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helper_functions.py
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import cv2
import numpy as np
from skimage.feature import hog
# Define a function to return HOG features and visualization
def get_hog_features(img, orient, pix_per_cell, cell_per_block,
vis=False, feature_vec=True):
# Call with two outputs if vis==True
if vis:
features, hog_image = hog(img, orientations=orient,
pixels_per_cell=(pix_per_cell, pix_per_cell),
cells_per_block=(cell_per_block, cell_per_block),
transform_sqrt=True,
visualise=vis, feature_vector=feature_vec)
return features, hog_image
# Otherwise call with one output
else:
features = hog(img, orientations=orient,
pixels_per_cell=(pix_per_cell, pix_per_cell),
cells_per_block=(cell_per_block, cell_per_block),
transform_sqrt=True,
visualise=vis, feature_vector=feature_vec)
return features
# 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
# Define a function to compute color histogram features
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
# Define a function to extract features from a single image window
# This function is very similar to extract_features()
# just for a single image rather than list of images
def single_img_features(img, color_space='RGB', spatial_size=(32, 32),
hist_bins=32, orient=9,
pix_per_cell=8, cell_per_block=2, hog_channel=0,
spatial_feat=True, hist_feat=True, hog_feat=True):
# 1) Define an empty list to receive features
img_features = []
# 2) Apply color conversion if other than 'RGB'
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)
# 3) Compute spatial features if flag is set
if spatial_feat:
spatial_features = bin_spatial(feature_image, size=spatial_size)
# 4) Append features to list
img_features.append(spatial_features)
# 5) Compute histogram features if flag is set
if hist_feat:
hist_features = color_hist(feature_image, nbins=hist_bins)
# 6) Append features to list
img_features.append(hist_features)
# 7) Compute HOG features if flag is set
if hog_feat:
if hog_channel == 'ALL':
hog_features = []
for channel in range(feature_image.shape[2]):
hog_features.extend(get_hog_features(feature_image[:, :, channel],
orient, pix_per_cell, cell_per_block,
vis=False, feature_vec=True))
else:
hog_features = get_hog_features(feature_image[:, :, hog_channel], orient,
pix_per_cell, cell_per_block, vis=False, feature_vec=True)
# 8) Append features to list
img_features.append(hog_features)
# 9) Return concatenated array of features
return np.concatenate(img_features)
# 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
if x_start_stop[0] is None:
x_start_stop[0] = 0
if x_start_stop[1] is None:
x_start_stop[1] = img.shape[1]
if y_start_stop[0] is None:
y_start_stop[0] = 0
if y_start_stop[1] is None:
y_start_stop[1] = img.shape[0]
# Compute the span of the region to be searched
xspan = x_start_stop[1] - x_start_stop[0]
yspan = y_start_stop[1] - y_start_stop[0]
# Compute the number of pixels per step in x/y
nx_pix_per_step = np.int(xy_window[0] * (1 - xy_overlap[0]))
ny_pix_per_step = np.int(xy_window[1] * (1 - xy_overlap[1]))
# Compute the number of windows in x/y
nx_windows = np.int(xspan / nx_pix_per_step) - 1
ny_windows = np.int(yspan / ny_pix_per_step) - 1
# Initialize a list to append window positions to
window_list = []
# Loop through finding x and y window positions
# Note: you could vectorize this step, but in practice
# you'll be considering windows one by one with your
# classifier, so looping makes sense
for ys in range(ny_windows):
for xs in range(nx_windows):
# Calculate window position
startx = xs * nx_pix_per_step + x_start_stop[0]
endx = startx + xy_window[0]
starty = ys * ny_pix_per_step + y_start_stop[0]
endy = starty + xy_window[1]
# Append window position to list
window_list.append(((startx, starty), (endx, endy)))
# Return the list of windows
return window_list
# Adds heat to the heatmap, based on bounding boxes received
def add_heat(heatmap, bbox_list, amount=1):
# Iterate through list of bboxes
for box in bbox_list:
# Add += 1 for all pixels inside each bbox
# Assuming each "box" takes the form ((x1, y1), (x2, y2))
heatmap[box[0][1]:box[1][1], box[0][0]:box[1][0]] += amount
# Return updated heatmap
return heatmap
# Applies threshold to the heatmap, zeroing out anything below the threshold
def apply_threshold(heatmap, threshold):
new_heatmap = np.copy(heatmap)
# Zero out pixels below the threshold
new_heatmap[heatmap <= threshold] = 0
# Return thresholded map
return new_heatmap
# Draws bounding boxes over an image
def draw_labeled_bboxes(img, labels):
# Iterate through all detected cars
for car_number in range(1, labels[1] + 1):
# Find pixels with each car_number label value
nonzero = (labels[0] == car_number).nonzero()
# Identify x and y values of those pixels
nonzeroy = np.array(nonzero[0])
nonzerox = np.array(nonzero[1])
# Define a bounding box based on min/max x and y
bbox = ((np.min(nonzerox), np.min(nonzeroy)), (np.max(nonzerox), np.max(nonzeroy)))
# Draw the box on the image
cv2.rectangle(img, bbox[0], bbox[1], (0, 0, 255), 6)
# Return the image
return img