# construct the argument parser and parse the arguments
ap = argparse.ArgumentParser()
ap.add_argument("-i",
"--images",
required=True,
help="path to the images to be classified")
args = vars(ap.parse_args())
# loop over the images
for imagePath in sorted(list(paths.list_images(args["images"]))):
# load the image
image = cv2.imread(imagePath)
print(imagePath)
# if the width is greater than 640 pixels, then resize the image
if image.shape[1] > 640:
image = imutils.resize(image, width=640)
# initialize the license plate detector and detect the license plates and charactors
lpd = LicensePlateDetector(image)
plates = lpd.detect()
# loop over the license plate regions and draw the bounding box surrounding the
# license plate
for lpBox in plates:
lpBox = np.array(lpBox).reshape((-1, 1, 2)).astype(np.int32)
cv2.drawContours(image, [lpBox], -1, (0, 255, 0), 2)
# display the output image
cv2.imshow("image", image)
cv2.waitKey(0)
# initialize the descriptor
blockSizes = ((5, 5), (5, 10), (10, 5), (10, 10))
desc = BlockBinaryPixelSum(targetSize=(30, 15), blockSizes=blockSizes)
# loop over the images
for imagePath in sorted(list(paths.list_images(args["images"]))):
# load the image
print(imagePath[imagePath.rfind("/") + 1:])
image = cv2.imread(imagePath)
# if the width is greater than 640 pixels, then resize the image
if image.shape[1] > 640:
image = imutils.resize(image, width=640)
# initialize the license plate detector and detect the license plates and characters
lpd = LicensePlateDetector(image, numChars=7)
plates = lpd.detect()
# loop over the detected plages
for (lpBox, chars) in plates:
# initialize the text containing the recognized characters
text = ""
# loop over each character
for (i, char) in enumerate(chars):
# preprocess the character and describe it
char = LicensePlateDetector.preprocessChar(char)
features = desc.describe(char).reshape(1, -1)
# if this is the first 3 characters, then use the character classifier
if i < 3:
alphabetLabels = []
digitsLabels = []
# loop over the sample character paths
for samplePath in sorted(glob.glob(args["samples"] + "/*")):
# extract the sample name, grab all images in the sample path, and sample them
sampleName = samplePath[samplePath.rfind("/") + 1:]
imagePaths = list(paths.list_images(samplePath))
imagePaths = random.sample(imagePaths, min(len(imagePaths), args["min_samples"]))
# loop over all images in the sample path
for imagePath in imagePaths:
# load the character, convert it to grayscale, preprocess it, and describe it
char = cv2.imread(imagePath)
char = cv2.cvtColor(char, cv2.COLOR_BGR2GRAY)
char = LicensePlateDetector.preprocessChar(char)
features = desc.describe(char)
# check to see if we are examining a digit
if sampleName.isdigit():
digitsData.append(features)
digitsLabels.append(sampleName)
# otherwise, we are examining an alphabetical character
else:
alphabetData.append(features)
alphabetLabels.append(sampleName)
# train the character classifier
print("[INFO] fitting character model...")
charModel = LinearSVC(C=1.0, random_state=42)
# loop over the images
for imagePath in imagePaths:
# show the image path
print("[EXAMINING] {}".format(imagePath))
try:
# load the image
image = cv2.imread(imagePath)
# if the width is greater than 640 pixels, then resize the image
if image.shape[1] > 640:
image = imutils.resize(image, width=640)
# initialize the license plate detector and detect characters on the license plate
lpd = LicensePlateDetector(image, numChars=7)
plates = lpd.detect()
# loop over the license plates
for (lpBox, chars) in plates:
# draw the bounding box surrounding the license plate and display it for
# reference purposes
plate = image.copy()
cv2.drawContours(plate, [lpBox], -1, (0, 255, 0), 2)
cv2.imshow("License Plate", plate)
# loop over the characters
for char in chars:
# display the character and wait for a keypress
cv2.imshow("Char", char)
key = cv2.waitKey(0)
# mask = np.zeros(image.shape[:2], dtype="uint8")
#x1,y1 x2,y2
# cv2.rectangle(mask, rect[0], rect[1], 255, -1)
# image = cv2.bitwise_and(image, image, mask=mask)
# crop it instead
image = image[rect[0][1]:rect[1][1], rect[0][0]:rect[1][0]]
print(imagePath)
# if the width is greater than 640 pixels, then resize the image
# if image.shape[1] > 640:
# image = imutils.resize(image, width=640)
# initialize the license plate detector and detect the license plates and charactors
lpd = LicensePlateDetector(image)
data = lpd.detect()
plates = data['regions']
if plates:
images = data['images']
# image = cv2.cvtColor(images[-1], cv2.COLOR_GRAY2BGR)
# montage.addResult(images[0])
print('found', len(plates))
# loop over the license plate regions and draw the bounding box surrounding the
# license plate
for lpBox in plates:
# cv2.drawContours(image, [lpBox], -1, (0, 255, 0), 2)