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VehicleDetectTrack.py
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VehicleDetectTrack.py
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#=============================================================================
#=== Importing libraries =====================================================
#=============================================================================
import matplotlib.image as mpimg
import matplotlib.pyplot as plt
import numpy as np
import pickle
import cv2
from Functions import *
from scipy.ndimage.measurements import label
svc = pickle.load( open("svc_pickle.p", "rb" ) )
X_scaler = pickle.load( open("X_scaler_pickle.p", "rb" ) )
orient = 9
pix_per_cell = 8
cell_per_block = 2
spatial_size = (32, 32)
hist_bins = 32
# img = mpimg.imread('test_images/test6.jpg')
#=============================================================================
#=== Find Cars Function ======================================================
#=============================================================================
# Define a single function that can extract features using hog sub-sampling and make predictions
def find_cars(img, ystart, ystop, scale, svc, X_scaler, orient, pix_per_cell, cell_per_block, spatial_size, hist_bins):
boxes = []
draw_img = np.copy(img)
img = img.astype(np.float32)/255
img_tosearch = img[ystart:ystop,:,:]
ctrans_tosearch = convert_color(img_tosearch, conv='RGB2YCrCb')
if scale != 1:
imshape = ctrans_tosearch.shape
ctrans_tosearch = cv2.resize(ctrans_tosearch, (np.int(imshape[1]/scale), np.int(imshape[0]/scale)))
ch1 = ctrans_tosearch[:,:,0]
ch2 = ctrans_tosearch[:,:,1]
ch3 = ctrans_tosearch[:,:,2]
# Define blocks and steps as above
nxblocks = (ch1.shape[1] // pix_per_cell) - cell_per_block + 1
nyblocks = (ch1.shape[0] // pix_per_cell) - cell_per_block + 1
nfeat_per_block = orient*cell_per_block**2
# 64 was the orginal sampling rate, with 8 cells and 8 pix per cell
window = 64
nblocks_per_window = (window // pix_per_cell) - cell_per_block + 1
cells_per_step = 2 # Instead of overlap, define how many cells to step
nxsteps = (nxblocks - nblocks_per_window) // cells_per_step
nysteps = (nyblocks - nblocks_per_window) // cells_per_step
# Compute individual channel HOG features for the entire image
hog1 = get_hog_features(ch1, orient, pix_per_cell, cell_per_block, feature_vec=False)
hog2 = get_hog_features(ch2, orient, pix_per_cell, cell_per_block, feature_vec=False)
hog3 = get_hog_features(ch3, orient, pix_per_cell, cell_per_block, feature_vec=False)
for xb in range(nxsteps):
for yb in range(nysteps):
ypos = yb*cells_per_step
xpos = xb*cells_per_step
# Extract HOG for this patch
hog_feat1 = hog1[ypos:ypos+nblocks_per_window, xpos:xpos+nblocks_per_window].ravel()
hog_feat2 = hog2[ypos:ypos+nblocks_per_window, xpos:xpos+nblocks_per_window].ravel()
hog_feat3 = hog3[ypos:ypos+nblocks_per_window, xpos:xpos+nblocks_per_window].ravel()
hog_features = np.hstack((hog_feat1, hog_feat2, hog_feat3))
xleft = xpos*pix_per_cell
ytop = ypos*pix_per_cell
# Extract the image patch
subimg = cv2.resize(ctrans_tosearch[ytop:ytop+window, xleft:xleft+window], (64,64))
# Get color features
spatial_features = bin_spatial(subimg, size=spatial_size)
hist_features = color_hist(subimg, nbins=hist_bins)
# Scale features and make a prediction
# test_features = X_scaler.transform(np.hstack((spatial_features, hist_features, hog_features)).reshape(1, -1))
# test_features = X_scaler.transform(np.hstack((shape_feat, hist_feat)).reshape(1, -1))
test_features = X_scaler.transform((hog_features).reshape(1, -1))
test_prediction = svc.predict(test_features)
# test_prediction = 1
if test_prediction == 1:
xbox_left = np.int(xleft*scale)
ytop_draw = np.int(ytop*scale)
win_draw = np.int(window*scale)
cv2.rectangle(draw_img,(xbox_left, ytop_draw+ystart),(xbox_left+win_draw,ytop_draw+win_draw+ystart),(0,0,255),6)
boxes.append(((xbox_left, ytop_draw+ystart),(xbox_left+win_draw,ytop_draw+win_draw+ystart)))
# print(spatial_features.shape)
# print(hist_features.shape)
# print(hog_features.shape)
# print(test_features.shape)
return draw_img, boxes
#=============================================================================
#=== Heat, Threshold and Label Functions =====================================
#=============================================================================
def add_heat(heatmap, bbox_list):
# 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]] += 1
# Return updated heatmap
return heatmap# Iterate through list of bboxes
def apply_threshold(heatmap, threshold):
# Zero out pixels below the threshold
heatmap[heatmap <= threshold] = 0
# Return thresholded map
return heatmap
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
ystart = 400
ystop = 656
scale = 1.5
#=============================================================================
#=== Process Image, Video Pipeline ===========================================
#=============================================================================
def process_image(img):
out_img, boxes = find_cars(img, ystart, ystop, scale, svc, X_scaler, orient, pix_per_cell, cell_per_block, spatial_size, hist_bins)
# plt.imshow(out_img)
# plt.show()
box_list = boxes
# print(box_list)
# Read in image similar to one shown above
# image = mpimg.imread('test_image.jpg')
heat = np.zeros_like(img[:,:,0]).astype(np.float)
# Add heat to each box in box list
heat = add_heat(heat,box_list)
# Apply threshold to help remove false positives
heat = apply_threshold(heat,1)
# Visualize the heatmap when displaying
heatmap = np.clip(heat, 0, 255)
# Find final boxes from heatmap using label function
labels = label(heatmap)
draw_img = draw_labeled_bboxes(np.copy(img), labels)
# fig = plt.figure()
# plt.subplot(121)
# plt.imshow(draw_img)
# plt.title('Car Positions')
# plt.subplot(122)
# plt.imshow(heatmap, cmap='hot')
# plt.title('Heat Map')
# fig.tight_layout()
# plt.show()
return draw_img
from moviepy.editor import VideoFileClip
from IPython.display import HTML
import moviepy as mve
#clip1 = VideoFileClip("project_video.mp4")
input_video = VideoFileClip("project_video.mp4")#.subclip(40,44)
output_video = input_video.fl_image(process_image) #NOTE: this function expects color images!!
output_video.write_videofile('project_video_output.mp4', audio=False)