def __init__(self):
self.list_out = []
self.list_pic = []
try:
self.input_dir = sys.argv[1]
self.output_dir = sys.argv[2]
self.vehicle_threshold = .5
self.vehicle_weights = 'data/vehicle-detector/yolo-voc.weights'
self.vehicle_netcfg = 'data/vehicle-detector/yolo-voc.cfg'
self.vehicle_dataset = 'data/vehicle-detector/voc.data'
self.vehicle_net = dn.load_net(self.vehicle_netcfg,
self.vehicle_weights, 0)
self.vehicle_meta = dn.load_meta(self.vehicle_dataset)
self.imgs_paths = image_files_from_folder(self.input_dir)
self.imgs_paths.sort()
#if not isdir(self.output_dir):
# makedirs(self.output_dir)
except:
traceback.print_exc()
sys.exit(1)
def main():
args = parse_args()
# Retrieve the names of the input images
imgs_paths = image_files_from_folder(args.input_folder)
for img_path in imgs_paths:
# Process each image individually
process_image(img_path, args)
def generate_outputs(input_dir, output_dir):
img_files = image_files_from_folder(input_dir)
for img_file in img_files:
bname = splitext(basename(img_file))[0]
I = cv2.imread(img_file)
detected_cars_labels = '%s/%s_cars.txt' % (output_dir, bname)
Lcar = lread(detected_cars_labels)
sys.stdout.write('%s' % bname)
if Lcar:
for i, lcar in enumerate(Lcar):
draw_label(I, lcar, color=YELLOW, thickness=3)
lp_label = '%s/%s_%dcar_lp.txt' % (output_dir, bname, i)
lp_label_str = '%s/%s_%dcar_lp_str.txt' % (output_dir, bname, i)
if isfile(lp_label):
Llp_shapes = readShapes(lp_label)
pts = Llp_shapes[0].pts * lcar.wh().reshape(2, 1) + lcar.tl().reshape(2, 1)
ptspx = pts * np.array(I.shape[1::-1], dtype=float).reshape(2, 1)
draw_losangle(I, ptspx, RED, 3)
if isfile(lp_label_str):
with open(lp_label_str, 'r') as f:
lp_str = f.read().strip()
llp = Label(0, tl=pts.min(1), br=pts.max(1))
write2img(I, llp, lp_str)
sys.stdout.write(',%s' % lp_str)
cv2.imwrite('%s/%s_output.png' % (output_dir, bname), I)
input_dir = sys.argv[1]
output_dir = sys.argv[2]
vehicle_threshold = .5
vehicle_weights = 'data/vehicle-detector/yolo-voc.weights' #.encode('ascii')
vehicle_netcfg = 'data/vehicle-detector/yolo-voc.cfg' #.encode('ascii').encode('ascii')
vehicle_dataset = 'data/vehicle-detector/voc.data' #.encode('ascii')
vehicle_weights = vehicle_weights.encode('ascii')
vehicle_netcfg = vehicle_netcfg.encode('ascii')
vehicle_dataset = vehicle_dataset.encode('ascii')
#print(vehicle_weights)
vehicle_net = dn.load_net(vehicle_netcfg, vehicle_weights, 0)
vehicle_meta = dn.load_meta(vehicle_dataset)
imgs_paths = image_files_from_folder(input_dir)
imgs_paths.sort()
if not isdir(output_dir):
makedirs(output_dir)
print('Searching for vehicles using YOLO...')
for i, img_path in enumerate(imgs_paths):
print('\tScanning %s' % img_path)
bname = basename(splitext(img_path)[0])
img_path2 = img_path.encode('ascii')
R, _ = detect(vehicle_net,
outdir = args.output_dir
iterations = args.iterations
batch_size = args.batch_size
dim = 208
if not isdir(outdir):
makedirs(outdir)
model, model_stride, xshape, yshape = load_network(args.model, dim)
opt = getattr(keras.optimizers, args.optimizer)(lr=args.learning_rate)
model.compile(loss=loss, optimizer=opt)
print('Checking input directory...')
Files = image_files_from_folder(train_dir)
Data = []
for file in Files:
labfile = splitext(file)[0] + '.txt'
if isfile(labfile):
L = readShapes(labfile)
I = cv2.imread(file)
Data.append([I, L[0]])
print('%d images with labels found' % len(Data))
dg = DataGenerator( data=Data, \
process_data_item_func=lambda x: process_data_item(x,dim,model_stride),\
xshape=xshape, \
yshape=(yshape[0],yshape[1],yshape[2]+1), \
sys.stdout = io.TextIOWrapper(sys.stdout.buffer, encoding='utf8')
import cv2
import numpy as np
from os.path import splitext, basename, isfile
from src.utils import image_files_from_folder
from src.drawing_utils import draw_label, draw_losangle, write2img
from src.label import lread, Label, readShapes
YELLOW = (0, 255, 255)
RED = (0, 0, 255)
input_dir = sys.argv[1]
output_dir = sys.argv[2]
img_files = image_files_from_folder(input_dir)
for img_file in img_files:
bname = splitext(basename(img_file))[0]
I = cv2.imread(img_file)
detected_cars_labels = '%s/%s_cars.txt' % (output_dir, bname)
Lcar = lread(detected_cars_labels)
sys.stdout.write('%s' % bname)
if Lcar:
input_dir = sys.argv[1]
output_dir = sys.argv[2]
vehicle_threshold = .5
vehicle_weights = 'data/vehicle-detector/yolo-voc.weights'.encode(
'utf-8')
vehicle_netcfg = 'data/vehicle-detector/yolo-voc.cfg'.encode('utf-8')
vehicle_dataset = 'data/vehicle-detector/voc.data'.encode('utf-8')
vehicle_net = dn.load_net(vehicle_netcfg, vehicle_weights, 0)
vehicle_meta = dn.load_meta(vehicle_dataset)
imgs_paths = [path.encode('utf-8')
for path in image_files_from_folder(input_dir)]
imgs_paths.sort()
if not isdir(output_dir):
makedirs(output_dir)
print('Searching for vehicles using YOLO...')
for i, img_path in enumerate(imgs_paths):
print('\tScanning %s' % img_path)
bname = basename(splitext(img_path)[0])
R, _ = detect(vehicle_net, vehicle_meta,
img_path, thresh=vehicle_threshold)
def __init__(self, path, img_size=208):
self.img_files = image_files_from_folder(path)
n = len(self.img_files)
assert n > 0, 'No images found in %s' % path
self.img_size = img_size
