def demo():
feature_parameter = selected_feature_parameter()
y_start_stop = [440, None] # Min and max in y to search in slide_window()
svc, X_scaler = read_classifier("classifier.p")
image = mpimg.imread('bbox-example-image.jpg')
draw_image = np.copy(image)
# Uncomment the following line if you extracted training
# data from .png images (scaled 0 to 1 by mpimg) and the
# image you are searching is a .jpg (scaled 0 to 255)
#image = image.astype(np.float32)/255
windows = slide_window(image,
x_start_stop=[None, None],
y_start_stop=y_start_stop,
xy_window=(96, 96),
xy_overlap=(0.5, 0.5))
hot_windows = search_windows(image,
windows,
svc,
X_scaler,
feature_parameter=feature_parameter)
window_img = draw_boxes(draw_image,
hot_windows,
color=(0, 0, 255),
thick=6)
plt.imshow(window_img)
plt.show()
def process_image(img, debug=False):
clf = classifier.get_classifier()
windows = search(img, clf)
heat_map = get_heat_map(img, windows)
heat_map = apply_threshold(heat_map, 1)
labeled_img = get_labeled_image(img, heat_map)
if debug:
import matplotlib.pyplot as plt
from sliding_window import draw_boxes
f, axarr = plt.subplots(2, 2, figsize=(15, 15))
axarr[0, 0].set_title('Original Image')
axarr[0, 0].imshow(img)
axarr[0, 1].set_title('Detected Windows')
axarr[0, 1].imshow(draw_boxes(img, windows))
axarr[1, 0].set_title('Heat Map')
axarr[1, 0].imshow(heat_map)
axarr[1, 1].set_title('Labeled Image')
axarr[1, 1].imshow(labeled_img)
plt.show()
return labeled_img
def find_cars(img, y_start_stop, scale, clf, X_scaler, feature_parameter):
hot_windows = subsample_search(img, y_start_stop, scale, clf, X_scaler,
feature_parameter)
draw_image = np.copy(img)
draw_image = draw_boxes(draw_image,
hot_windows,
color=(0, 0, 255),
thick=6)
return draw_image
def handle_image(self, img):
# if self.count < 170:
# self.count += 1
# return img
alpha = 0.3
#scipy.misc.imsave('input_images/' + str(self.count) + '.jpg', img)
if self.heat is None:
self.heat = np.zeros_like(img[:, :, 0]).astype(np.float)
else:
self.heat = self.heat * (1.0 - alpha)
heat = np.zeros_like(img[:, :, 0]).astype(np.float)
image = img.astype(np.float32) / 255.
boxes = sliding_window.search_classify(image, clf)
draw_image = np.copy(image)
window_img = sliding_window.draw_boxes(draw_image,
boxes,
color=(0, 0, 1.0),
thick=6)
scipy.misc.imsave('output_images/boxes_' + str(self.count) + '.jpg',
window_img)
heat = heatmap.add_heat(heat, boxes)
#self.heatmaps.append(heat)
self.heat += heat * alpha
# Apply threshold to help remove false positives
hm = heatmap.apply_threshold(np.copy(self.heat), 0.6)
# Visualize the heatmap when displaying
hm = np.clip(hm, 0, 255)
scipy.misc.imsave('output_images/map_' + str(self.count) + '.jpg',
self.heat)
# Find final boxes from heatmap using label function
labels = label(hm)
draw_img = heatmap.draw_labeled_bboxes(image, labels)
scipy.misc.imsave('output_images/' + str(self.count) + '.jpg',
draw_img)
self.count += 1
return draw_img * 255.0
def process_images(files):
imgcnt = 0
for filename in files:
image = mpimg.imread(filename)
#crop image to see the left half
height, width, depth = image.shape
image = image[:, 0:int(width / 2):, ]
windows = all_sliding_windows(image, max_image_y=320)
#We get 190 windows that are too many
#pick 20 windows randomly
windows = random.sample(windows, 20)
#call save
save_images(image, windows, imgcnt)
imgcnt += 1
#print(len(windows))
if (0):
window_img = draw_boxes(image,
windows,
color=(20, 200, 255),
thick=2)
plt.imshow(window_img)
plt.show()
def cnn_search_classify(image, windows, model):
on_windows = []
probs = []
if len(windows) == 0:
return on_windows, probs
images = []
size = (64, 64)
for window in windows:
#print("searching ",window)
img = cv2.resize(image[window[0][1]:window[1][1],
window[0][0]:window[1][0]],
size,
interpolation=cv2.INTER_AREA)
img = rescale_resize_image(img)
images.append(img)
X_input = np.array(images)
probabilities = model.predict_proba(X_input, batch_size=32, verbose=0)
#print("prob",probabilities)
cnt = 0
PROB_CAR = 0.8 #0.8
for pnocar, pcar in probabilities:
if pcar > PROB_CAR:
window = windows[cnt]
on_windows.append(window)
probs.append(pcar)
if (0):
window_img = draw_boxes(image,
on_windows,
color=(0, 0, 255),
thick=6)
plt.imshow(window_img)
plt.show()
cnt += 1
print("Found ", len(on_windows), "car windows out of ", len(windows))
return on_windows, probs
def apply_window_search(train_dir, test_dir):
# Uncomment the following line if you extracted training
# data from .png images (scaled 0 to 1 by mpimg) and the
# image you are searching is a .jpg (scaled 0 to 255)
#image = image.astype(np.float32)/255
dcspace = 'YCrCb' # Can be RGB, HSV, LUV, HLS, YUV, YCrCb
orient = 9 # HOG orientations
pix_per_cell = 8 # HOG pixels per cell
cell_per_block = 2 # HOG cells per block
hog_channel = '012' # Can be 0, 1, 2, or "012"
spatial_size = (32, 32) # Spatial binning dimensions
hist_bins = 32 # Number of histogram bins
spatial_feat = True # Spatial features on or off
hist_feat = True # Histogram features on or off
hog_feat = True # HOG features on or off
y_start_stop = [None, None] # Min and max in y to search in slide_window()
if have_classifier():
svc, scaler, dcspace, spatial_size, hist_bins, orient, \
pix_per_cell, cell_per_block, hog_channel, spatial_feat, hist_feat, hog_feat = load_classifier()
else:
svc, scaler = combo_feature_train(train_dir, scspace='BGR', dcspace=dcspace,
spatial_size=spatial_size, hist_bins=hist_bins,
orient=orient, pix_per_cell=pix_per_cell,
cell_per_block=cell_per_block,
hog_channel=hog_channel, spatial_feat=spatial_feat,
hist_feat=hist_feat, hog_feat=hog_feat)
fnames = load_images(test_dir)
for fname in fnames:
image = cv2.imread(fname)
draw_image = np.copy(image)
draw_image = color_convert_nocheck(image, 'BGR', 'RGB')
min_window = spatial_size[0]
max_window = 128
top_y = int(image.shape[0] / 2)
y_start_stop = [top_y, image.shape[0]]
valid_y = image.shape[0] - top_y
for window_size in range(min_window, max_window, spatial_size[0]):
#window_size = int(min_window * scale_factor)
#print("window size", window_size, "larger than", image.shape[0], "x", image.shape[1])
if window_size > valid_y or window_size > image.shape[1]:
print("window size", window_size, "larger than",
valid_y, "x", image.shape[1])
continue
windows = slide_window(image, x_start_stop=[None, None], y_start_stop=y_start_stop,
xy_window=(window_size, window_size), xy_overlap=(0.75, 0.75))
hot_windows = search_windows(image, windows, svc, scaler, scspace='BGR', dcspace=dcspace,
spatial_size=spatial_size, hist_bins=hist_bins,
orient=orient, pix_per_cell=pix_per_cell,
cell_per_block=cell_per_block,
hog_channel=hog_channel, spatial_feat=spatial_feat,
hist_feat=hist_feat, hog_feat=hog_feat)
window_img = draw_boxes(draw_image, hot_windows,
color=(0, 0, 255), thick=5)
plt.imshow(window_img)
basename = os.path.basename(fname)
name, ext = os.path.splitext(basename)
savename = os.path.join(
'output_images', name + "_" + str(window_size) + "_" + ext)
fig = plt.figure()
plt.imshow(window_img)
plt.title('Searching window size' + str(window_size))
plt.xlabel(fname)
fig.savefig(savename)
plt.close()