import glob
import matplotlib.image as mpimg
import matplotlib.pyplot as plt
# NomeroffNet path
NOMEROFF_NET_DIR = os.path.abspath('../../')
sys.path.append(NOMEROFF_NET_DIR)
# Import license plate recognition tools.
from NomeroffNet import Detector
from NomeroffNet import filters
# load model
nnet = Detector()
nnet.loadModel(NOMEROFF_NET_DIR)
# Walking through the ./examples/images/ directory and checking each of the images for license plates.
rootDir = '../images/*'
imgs = [mpimg.imread(img_path) for img_path in glob.glob(rootDir)]
cv_imgs_masks = nnet.detect_mask(imgs)
for img, cv_img_masks in zip(imgs, cv_imgs_masks):
# Generate splashs.
splashs = filters.color_splash(img, cv_img_masks)
for splash in splashs:
plt.imshow(splash)
plt.axis("off")
plt.show()
nnet = Detector()
nnet.loadModel(NOMEROFF_NET_DIR)
# Detect numberplate
# img_path = 'images/example2.jpeg'
# img_path = '/usr/src/app/src/nomeroff-net/examples/images/example2.jpeg'
# img_path = '/usr/src/app/src/nomeroff-net/examples/images/example3.jpg'
for filename in glob.glob('/usr/src/app/src/nomeroff-net/cars/*'):
print(filename)
img = mpimg.imread(filename)
# Generate image mask.
cv_imgs_masks = nnet.detect_mask([img])
for cv_img_masks in cv_imgs_masks:
# Detect points.
arrPoints = rectDetector.detect(cv_img_masks)
# cut zones
zones = rectDetector.get_cv_zonesBGR(img, arrPoints, 64, 295)
# find standart
regionIds, stateIds, countLines = optionsDetector.predict(zones)
regionNames = optionsDetector.getRegionLabels(regionIds)
# find text with postprocessing by standart
textArr = textDetector.predict(zones)
textArr = textPostprocessing(textArr, regionNames)