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runDetection.py
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runDetection.py
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import glob
from object_functions import *
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from sklearn.svm import LinearSVC
import time
import pickle
import os
from frame import *
generate_new = False
if __name__ == '__main__':
if (generate_new):
# Load the data
images_notcar = glob.glob('data_large/non_vehicles/*/*.png')
images_notcar_negmining = glob.glob('neg_mining/neg_examples/*.png')
images_car = glob.glob('data_large/vehicles/*/*.png')
images_car_posmining = glob.glob('neg_mining/pos_examples/*.png')
cars = []
notcars = []
for image in images_notcar:
notcars.append(image)
for image in images_notcar_negmining:
notcars.append(image)
for image in images_car:
cars.append(image)
# Randomly pick half of car examples
cars_chosen = random.sample(cars,len(notcars)/2)
for image in images_car_posmining:
cars_chosen.append(image)
cars = cars_chosen
print("Loaded data...")
print("Found {} car images".format(len(cars)))
print("Found {} non-car images".format(len(notcars)))
# Extract features
# Parameters
param = {}
param["color_space"] = 'YCrCb' # Can be RGB, HSV, LUV, HLS, YUV, YCrCb
param["orient"] = 9 # HOG orientations
param["pix_per_cell"] = 8 # HOG pixels per cell
param["cell_per_block"] = 2 # HOG cells per block
param["hog_channel"] = "ALL" # Can be 0, 1, 2, or "ALL"
param["spatial_size"] = (4, 4) # Spatial binning dimensions
param["hist_bins"] = 32 # Number of histogram bins
param["spatial_feat"] = True # Spatial features on or off
param["hist_feat"] = True # Histogram features on or off
param["hog_feat"] = True # HOG features on or off
start_time = time.time()
car_features = Get_Features.extract_features(False, cars, cspace=param["color_space"],
spatial_size=param["spatial_size"], hist_bins=param["hist_bins"],
orient=param["orient"], pix_per_cell=param["pix_per_cell"],
cell_per_block=param["cell_per_block"],
hog_channel=param["hog_channel"], spat_feat=param["spatial_feat"],
hist_feat=param["hist_feat"], hog_feat=param["hog_feat"])
notcar_features = Get_Features.extract_features(False, notcars, cspace=param["color_space"],
spatial_size=param["spatial_size"], hist_bins=param["hist_bins"],
orient=param["orient"], pix_per_cell=param["pix_per_cell"],
cell_per_block=param["cell_per_block"],
hog_channel=param["hog_channel"], spat_feat=param["spatial_feat"],
hist_feat=param["hist_feat"], hog_feat=param["hog_feat"])
print("Computed",len(car_features)," car features of size ",len(car_features[0]))
print("Computed",len(notcar_features)," not car features of size ",len(notcar_features[0]))
print("Feature extraction took {} seconds".format(round(time.time() - start_time,2)))
print()
X = np.vstack((car_features, notcar_features)).astype(np.float64)
# Fit a per-column scaler
X_scaler = StandardScaler().fit(X)
# Apply the scaler to X
scaled_X = X_scaler.transform(X)
# Define the labels vector
y = np.hstack((np.ones(len(car_features)), np.zeros(len(notcar_features))))
# Split up data into randomized training and test sets
rand_state = np.random.randint(0, 100)
X_train, X_test, y_train, y_test = train_test_split(
scaled_X, y, test_size=0.2, random_state=rand_state)
print('Using:',param["orient"],'orientations',param["pix_per_cell"],
'pixels per cell and', param["cell_per_block"],'cells per block')
# Use a linear SVC
svc = LinearSVC()
# Check the training time for the SVC
t=time.time()
svc.fit(X_train, y_train)
t2 = time.time()
print(round(t2-t, 2), 'Seconds to train SVC...')
# Check the score of the SVC
print('Test Accuracy of SVC = ', round(svc.score(X_test, y_test), 4))
# Save params and other stuff to file
saved_dict = {}
saved_dict["X_scaler"] = X_scaler
saved_dict["classifier"] = svc
saved_dict["param"] = param
pickle.dump(saved_dict,open("classifier_params_hardnegposmine_size.p","wb"))
exit()
else:
saved_dict = pickle.load( open( "classifier_params_hardnegposmine.p", "rb" ) )
param = saved_dict["param"]
frame = Frame(10)
## Use these when running in parallel
# search_window_sizes_d = {'0': [192,128,96],
# '1': [64],
# '2': [32]}
# ylims_d = {'0': [[360,704],[376,704],[400,650]],
# '1': [[400,650]],
# '2': [[400,464]]}
# Use these when running in serial
search_window_sizes = [128,96,64,32]
ylims = [[376,704],[400,650],[400,650],[400,464]]
# Get individual frame from the project video
# Process frame and write to disk
vidcap = cv2.VideoCapture('project_video.mp4')
success,image = vidcap.read()
success = True
count = 1
while success:
success,image = vidcap.read()
draw_image = np.copy(image)
drawn_image = frame.find_labelled_cars_in_frame(image,\
saved_dict,\
search_window_sizes,\
ylims,
count,
debug=False,
num_process=1)
file = 'vid_images/frame{:04d}.jpg'.format(count)
cv2.imwrite(file,drawn_image)
print("File {} written to disk...".format(file))
count += 1
# Once the individual frames are available on disk
# create the video from the frames
images = []
for i in range(1,1253):
images.append("frame{:04d}.jpg".format(i))
# Determine the width and height from the first image
image_path = os.path.join('vid_images', images[0])
frame = cv2.imread(image_path)
height, width, channels = frame.shape
fourcc = cv2.VideoWriter_fourcc(*'mp4v')
out = cv2.VideoWriter('out5.mp4', fourcc, 15, (width, height))
for image in images:
image_path = os.path.join('vid_images', image)
frame = cv2.imread(image_path)
out.write(frame) # Write out frame to video
out.release()
exit()