def __init__(self):
self.shape = (224, 320, 3)
# model sess for being loaded just one time
model_name = "models/model_2.ckpt"
self.sess = tf.Session()
tf.saved_model.loader.load(self.sess, ['train'], model_name)
detector_stop = dlib.fhog_object_detector('data/detector_stop.svm')
detector_sem = dlib.fhog_object_detector('data/detector_sem.svm')
self.detectors = [detector_stop, detector_sem]
self.classes = ['stop', 'sem']
def GetMultiBuildmodel():
model = []
model.append(dlib.fhog_object_detector('ML_model/CE.svm'))
model.append(dlib.fhog_object_detector('ML_model/WEEE.svm'))
model.append(dlib.fhog_object_detector('ML_model/ROHS.svm'))
model.append(dlib.fhog_object_detector('ML_model/ULCSA.svm'))
model.append(dlib.fhog_object_detector('ML_model/Fortinet.svm'))
model.append(dlib.fhog_object_detector('ML_model/ICES_A.svm'))
# model.append(dlib.fhog_object_detector('ML_model/ICES_B.svm'))
model.append(dlib.fhog_object_detector('ML_model/RCM.svm'))
model.append(dlib.fhog_object_detector('ML_model/Address.svm'))
modelname = []
modelname.append(os.path.splitext(os.path.basename('ML_model/CE.svm'))[0])
modelname.append(
os.path.splitext(os.path.basename('ML_model/WEEE.svm'))[0])
modelname.append(
os.path.splitext(os.path.basename('ML_model/ROHS.svm'))[0])
modelname.append(
os.path.splitext(os.path.basename('ML_model/ULCSA.svm'))[0])
modelname.append(
os.path.splitext(os.path.basename('ML_model/Fortinet.svm'))[0])
modelname.append(
os.path.splitext(os.path.basename('ML_model/ICES_A.svm'))[0])
# modelname.append( os.path.splitext(os.path.basename('ML_model/ICES_B.svm'))[0] )
modelname.append(os.path.splitext(os.path.basename('ML_model/RCM.svm'))[0])
modelname.append(
os.path.splitext(os.path.basename('ML_model/Address.svm'))[0])
global COLORS
COLORS = np.random.uniform(0, 255, size=(len(model), 3))
return model, modelname
def trainObjects(images_folder):
folders = os.listdir(images_folder)
folders.remove('extras')
detectors = []
for folder in folders:
xml = os.path.join(images_folder, "{0}\{0}.xml".format(folder))
#print("{0}.svm".format(folder))
dlib.train_simple_object_detector(xml, "{0}.svm".format(folder),
options)
# save all detectors as list
detectors.append("{0}.svm".format(folder))
# Next, suppose you have trained multiple detectors and you want to run them
# efficiently as a group. You can do this as follows
detectorsModels = list()
for detector in detectors:
detectorsModels.append(dlib.fhog_object_detector(detector))
# testing multiple detectors with image
image = dlib.load_rgb_image(
images_folder +
'/head n shoulder/head-and-shoulder-best-oily-hair-shampoo.jpg')
[boxes, confidences, detector_idxs
] = dlib.fhog_object_detector.run_multiple(detectorsModels,
image,
upsample_num_times=1,
adjust_threshold=0.5)
for i in range(len(boxes)):
print("detector {} found box {} with confidence {}.".format(
detector_idxs[i], boxes[i], confidences[i]))
return detectorsModels, detectors
def detect_plate(self, cropped_image):
detector = dlib.fhog_object_detector("plate_detector_100.svm")
gray = cv2.cvtColor(cropped_image, cv2.COLOR_BGR2GRAY)
cars = detector(gray)
a = 1
b = 1
h = 1
w = 1
for i in cars:
cv2.rectangle(cropped_image, (i.left(), i.top()), (i.right(), i.bottom()), (0, 0, 255), 2)
a = i.left()
b = i.top()
h = i.bottom() - i.top()
w = i.right() - i.left()
cropped_plate = cropped_image[b:b+h, a:a+w]
if len(cropped_plate) <= 1:
if self.no_plate_counter == 1:
return self.same_frame, self.no_color
else:
self.no_plate_counter = 1
return self.no_plate, self.get_car_color(cropped_image)
else:
self.no_plate_counter = 0
return self.main(cropped_plate, cropped_image)
def detect_car(self, original_image):
detector = dlib.fhog_object_detector("car_detector_100.svm")
gray = cv2.cvtColor(original_image, cv2.COLOR_BGR2GRAY)
cars = detector(gray)
a = 1
b = 1
h = 1
w = 1
for i in cars:
cv2.rectangle(original_image, (i.left(), i.top()), (i.right(), i.bottom()), (0, 0, 255), 2)
a = i.left()
b = i.top()
h = i.bottom() - i.top()
w = i.right() - i.left()
cropped = original_image[b:b+h, a:a+w]
if len(cropped) <= 1:
return self.no_car, self.no_color
else:
return self.detect_plate(cropped)
def main(args):
'''Initializes and cleanup ros node'''
try:
rospy.init_node('image_feature', anonymous=True);
except:
pass;
ic = image_feature();
count = 0;
# create detector object
#
detector = dlib.fhog_object_detector("/home/roboboat/dlib/tools/imglab/detector2.svm");
# Unpersists graph from file
#
with tf.gfile.FastGFile("/home/roboboat/tf_files_01/retrained_graph.pb", 'rb') as f:
graph_def = tf.GraphDef()
graph_def.ParseFromString(f.read())
_ = tf.import_graph_def(graph_def, name='')
try:
while True:
ic.image_process(detector, count)
count = count + 1;
except KeyboardInterrupt:
print "shutting down ROS Image buoy detector module"
cv2.destroyAllWindows()
def __init__(self, xsize, ysize, path_or_io):
"""
Image is numpy array
"""
assert path_or_io, "Please pass an image path"
self.image = io.imread(path_or_io)
self.detector = dlib.fhog_object_detector()
self.objects = self.detector.run(self.image)
def __init__(self, sensor_name):
self.sensor_name = sensor_name
self.cfg_server = Server(FaceDetectionConfig, self.cfg_callback)
self.cv_bridge = cv_bridge.CvBridge()
self.detector = dlib.fhog_object_detector(self.face_detector_path)
self.pool = multiprocessing.Pool(3)
# get transformation between world, color, and depth images
now = rospy.Time(0)
tf_listener = tf.TransformListener()
print self.sensor_name
self.ir2rgb = recutils.lookupTransform(
tf_listener, self.sensor_name + '_ir_optical_frame',
self.sensor_name + '_rgb_optical_frame', 10.0, now)
# self.ir2rgb = numpy.eye(4, 4).astype(numpy.float64)
print '--- ir2rgb ---\n', self.ir2rgb
self.world2rgb = recutils.lookupTransform(
tf_listener, '/world', self.sensor_name + '_rgb_optical_frame',
10.0, now)
print '--- world2rgb ---\n', self.world2rgb
self.pub = rospy.Publisher('/face_detector/detections',
DetectionArray,
queue_size=10)
self.pub_local = rospy.Publisher(self.sensor_name +
'/face_detector/detections',
DetectionArray,
queue_size=10)
try:
print 'tryingnsecs_round to listen raw rgb image topic...'
rospy.client.wait_for_message(self.sensor_name + '/hd/image_color',
Image, 1.0)
img_subscriber = message_filters.Subscriber(
self.sensor_name + '/hd/image_color', Image)
except rospy.ROSException:
print 'failed, listen compressed rgb image topic'
img_subscriber = message_filters.Subscriber(
self.sensor_name + '/hd/image_color/compressed',
CompressedImage)
self.subscribers = [
img_subscriber,
message_filters.Subscriber(self.sensor_name + '/hd/camera_info',
CameraInfo),
message_filters.Subscriber('/detector/detections', DetectionArray)
]
# TypeSynchronizer doesn't work, the image time and the detection time are slightly different?
# self.ts = message_filters.TimeSynchronizer(self.subscribers, 5)
# self.ts = message_filters.ApproximateTimeSynchronizer(self.subscribers, 5, 0.0001)
self.ts = recutils.TimeSynchronizer(self.subscribers, 60, 1000)
self.ts.registerCallback(self.callback)
self.reset_time_sub = rospy.Subscriber('/reset_time', Empty,
self.reset_time)
print("init complete")
def __init__(self):
self.ratio_threshold = 0.25
self.min_frames_below_threshold = 20
self.frame_counter = 0
self.face_detector = dlib.get_frontal_face_detector()
self.face_predictor = dlib.shape_predictor('models/shape_predictor_68_face_landmarks.dat')
self.hand_detector = dlib.fhog_object_detector('models/HandDetector.svm')
self.blink_counter = 0
self.hand_counter = 0
self.min_hand_frames = 5
self.pause_flag = False
self.mp = MusicPlayer()
def __init__(self, makeup_artist):
self.to_process = []
self.to_output = []
self.makeup_artist = makeup_artist
self.face_detector = dlib.get_frontal_face_detector()
self.landmark_detector = dlib.shape_predictor(
"./resources/shape_68_dots.dat")
self.handetector = dlib.fhog_object_detector(
'./resources/HandDetector.svm')
self.pts = deque(maxlen=4)
thread = threading.Thread(target=self.keep_processing, args=())
thread.daemon = True
thread.start()
def __init__(self, model):
if isinstance(model, STRING_TYPES) or isinstance(model, Path):
m_path = Path(model)
if not Path(m_path).exists():
raise ValueError('Model {} does not exist.'.format(m_path))
# There are two different kinds of object detector, the
# simple_object_detector and the fhog_object_detector, but we
# can't tell which is which from the file name. Therefore, try one
# and then the other. Unfortunately, it throws a runtime error,
# which we have to catch.
try:
model = dlib.simple_object_detector(str(m_path))
except RuntimeError:
model = dlib.fhog_object_detector(str(m_path))
self._dlib_model = model
def runProject():
fist_cascade = cv2.CascadeClassifier()
fist_cascade.load('./fist.xml')
# <-- hand detector with dlib -->
detector = dlib.fhog_object_detector("./HandDetector.svm")
speech_recognizer = speechInit()
try:
win = dlib.image_window()
driver = None
cap = cv2.VideoCapture(0)
fistCount = 0
while True:
win.clear_overlay()
ret, image = cap.read()
#image = imutils.resize(image, width=800)
win.set_image(image)
if isFist(image, fist_cascade):
print("«-- Fist Found --»")
if fistCount < 5:
fistCount += 1
elif fistCount == 5 and driver == None:
fistCount = 0
print("«-- Can't close something that is not open --»")
elif fistCount == 5 and not (driver == None):
fistCount = 0
driver = fistDecisions(speech_recognizer, driver)
continue
else:
fistCount = 0
rects = detector(image)
win.add_overlay(rects)
if len(rects) == 0:
count = 0
print("NOT detected")
time.sleep(0.4)
else:
count += 1
print("detected")
if count == 8:
driver = searchMySpeech(driver, speech_recognizer)
count = 0
except KeyboardInterrupt:
cap.release()
driver.quit() if not (driver == None) else None
def Test_model(listNameModel, scaleim, cam):
detectors = []
cap = cv2.VideoCapture(cam)
for model in listNameModel:
detectors.append(dlib.fhog_object_detector(model))
while (1):
start = time.time()
ok, img = cap.read()
image = pyramid(img, scaleim)
image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
[boxes, confidences, detector_idxs
] = dlib.fhog_object_detector.run_multiple(detectors,
image,
upsample_num_times=0,
adjust_threshold=0.0)
for i in range(len(boxes)):
if (confidences[i] > 1):
confidences[i] = 1
elif (confidences[i] < 0.1):
continue
face = boxes[i]
x = int(face.left() * scaleim)
y = int(face.top() * scaleim)
w = int((face.right() - face.left()) * scaleim)
h = int((face.bottom() - face.top()) * scaleim)
cv2.rectangle(img, (x, y), (x + w, y + h), (255, 0, 0), 2)
cv2.putText(img,
str(detector_idxs[i]) + ' ' +
str(np.round(confidences[i] * 100, 2)) + '%', (x, y),
cv2.FONT_HERSHEY_SIMPLEX,
0.5, (0, 250, 0),
1,
lineType=cv2.LINE_AA)
k = cv2.waitKey(1) & 0xff
if k == 27:
break
end = time.time()
cv2.putText(img,
'FPS:' + str(np.round(1 / (end - start))), (20, 20),
cv2.FONT_HERSHEY_SIMPLEX,
0.5, (100, 50, 150),
1,
lineType=cv2.LINE_AA)
cv2.imshow("image", img)
cap.release()
cv2.destroyAllWindows()
def detect_car(frame):
#load car detector
detector = dlib.fhog_object_detector("../DATA/SVM/car_detector.svm")
#win = dlib.image_window()
try:
dets = detector(frame)
for d in dets:
frame = frame[int(d.top()):int(d.bottom() + 20),
int(d.left()):int(d.right() + 20)]
# Display the resulting frame
cv2.imshow("frame", frame)
return True, frame, (int(d.left()), int(d.top()),
int(d.right() + 20 - d.left()),
int(d.bottom() + 20 - d.top()))
except:
#print "HOG detector failed"
return False, None, None
def __init__(self, makeup_artist):
self.to_process = deque(maxlen=2)
self.to_output = deque(maxlen=2)
self.makeup_artist = makeup_artist
self.face_detector = dlib.get_frontal_face_detector()
self.landmark_detector = dlib.shape_predictor(
"../resources/shape_68_dots.dat")
self.handetector = dlib.fhog_object_detector(
'../resources/HandDetector.svm')
self.count = 0
self.max = 0
self.charge = False
self.user = "******"
self.background = cv2.imread("../media/home1.png")
self.r1 = 0
self.r2 = 0
thread = threading.Thread(target=self.keep_processing, args=())
thread.daemon = True
thread.start()
def __init__(self,
ert_model_path="",
auxiliary_model_path="",
face_detection_model_path="",
facial_landmark_localiser=None,
auxiliary_utility=None,
face_detector=None):
if len(ert_model_path) > 0:
facial_landmark_localiser = FacialLandmarkLocaliser(ert_model_path)
if len(auxiliary_model_path) > 0:
auxiliary_utility = AuxiliaryUtility(auxiliary_model_path)
super(ZenoFaceTracker, self).__init__(facial_landmark_localiser,
auxiliary_utility)
if len(face_detection_model_path) > 0:
self._face_detector = dlib.fhog_object_detector(
face_detection_model_path)
elif face_detector is not None:
self._face_detector = face_detector
else:
self._face_detector = dlib.get_frontal_face_detector()
self._face_detection_gap = 0xFFFFFFFF
def __init__(self, node_name):
super(GoodFeatures, self).__init__(node_name)
# Do we show text on the display?
self.show_text = rospy.get_param("~show_text", True)
# How big should the feature points be (in pixels)?
self.feature_size = rospy.get_param("~feature_size", 1)
# Good features parameters
self.gf_maxCorners = rospy.get_param("~gf_maxCorners", 200)
self.gf_qualityLevel = rospy.get_param("~gf_qualityLevel", 0.02)
self.gf_minDistance = rospy.get_param("~gf_minDistance", 7)
self.gf_blockSize = rospy.get_param("~gf_blockSize", 10)
self.gf_useHarrisDetector = rospy.get_param("~gf_useHarrisDetector", True)
self.gf_k = rospy.get_param("~gf_k", 0.04)
# Store all parameters together for passing to the detector
self.gf_params = dict(maxCorners = self.gf_maxCorners,
qualityLevel = self.gf_qualityLevel,
minDistance = self.gf_minDistance,
blockSize = self.gf_blockSize,
useHarrisDetector = self.gf_useHarrisDetector,
k = self.gf_k)
# Initialize key variables
self.keypoints = list()
self.detect_box = None
self.mask = None
self.boolFlag = Bool()
#self.detector1 = dlib.fhog_object_detector("Pedestriandetector.svm")
#self.detector2 = dlib.fhog_object_detector("Pedestriandetector2.svm")
self.detector3 = dlib.fhog_object_detector("Pedestriandetector33.svm")
self.detectors = [self.detector3]
pygame.init()
self.s = pygame.mixer.Sound('Dong.WAV')
DETECTOR = 'data/cat_face_detector.svm'
# Pre-trained shape predictor from iBUG 300-W dataset for human facial landmarks
SHAPE_PREDICTOR = 'data/cat_landmark_predictor.dat'
# finds landmarks in the form (from viewer perspective):
# index - (x,y)
MOUTH_INDEX = 0
LEFT_EYE_INDEX = 1
LEFT_EAR_LEFT_INDEX = 2
RIGHT_EAR_LEFT_INDEX = 3
NOSE_INDEX = 4
RIGHT_EYE_INDEX = 5
LEFT_EAR_RIGHT_INDEX = 6
RIGHT_EAR_RIGHT_INDEX = 7
detector = dlib.fhog_object_detector(DETECTOR)
haar_detector = dlib.fhog_object_detector(DETECTOR)
landmarks_predictor = dlib.shape_predictor(SHAPE_PREDICTOR)
# convenience function from imutils
def dlib_to_cv_bounding_box(box):
# convert dlib bounding box for OpenCV display
x = box.left()
y = box.top()
w = box.right() - x
h = box.bottom() - y
return x, y, w, h
print("Processing file: {}".format(f))
img = io.imread(f)
dets = detector(img)
print("Number of faces detected: {}".format(len(dets)))
for k, d in enumerate(dets):
print("Detection {}: Left: {} Top: {} Right: {} Bottom: {}".format(
k, d.left(), d.top(), d.right(), d.bottom()))
win.clear_overlay()
win.set_image(img)
win.add_overlay(dets)
dlib.hit_enter_to_continue()
# Next, suppose you have trained multiple detectors and you want to run them
# efficiently as a group. You can do this as follows:
detector1 = dlib.fhog_object_detector("dog_detector.svm")
# In this example we load detector.svm again since it's the only one we have on
# hand. But in general it would be a different detector.
detector2 = dlib.fhog_object_detector("dog_detector.svm")
# make a list of all the detectors you wan to run. Here we have 2, but you
# could have any number.
detectors = [detector1, detector2]
image = io.imread(faces_folder + '/2008_002506.jpg')
[boxes, confidences, detector_idxs] = dlib.fhog_object_detector.run_multiple(detectors, image, upsample_num_times=1, adjust_threshold=0.0)
for i in range(len(boxes)):
print("detector {} found box {} with confidence {}.".format(detector_idxs[i], boxes[i], confidences[i]))
# Finally, note that you don't have to use the XML based input to
# train_simple_object_detector(). If you have already loaded your training
# images and bounding boxes for the objects then you can call it as shown
# below.
print("Processing file: {}".format(f))
img = dlib.load_rgb_image(f)
dets = detector(img)
print("Number of faces detected: {}".format(len(dets)))
for k, d in enumerate(dets):
print("Detection {}: Left: {} Top: {} Right: {} Bottom: {}".format(
k, d.left(), d.top(), d.right(), d.bottom()))
win.clear_overlay()
win.set_image(img)
win.add_overlay(dets)
dlib.hit_enter_to_continue()
# Next, suppose you have trained multiple detectors and you want to run them
# efficiently as a group. You can do this as follows:
detector1 = dlib.fhog_object_detector("../biblioteca/detector_focinhos.svm")
# In this example we load detector.svm again since it's the only one we have on
# hand. But in general it would be a different detector.
detector2 = dlib.fhog_object_detector("../biblioteca/detector_focinhos.svm")
# make a list of all the detectors you want to run. Here we have 2, but you
# could have any number.
detectors = [detector1, detector2]
image = dlib.load_rgb_image(faces_folder + '/20201104_131526 (2).jpg')
[boxes, confidences,
detector_idxs] = dlib.fhog_object_detector.run_multiple(detectors,
image,
upsample_num_times=1,
adjust_threshold=0.0)
for i in range(len(boxes)):
print("detector {} found box {} with confidence {}.".format(
detector_idxs[i], boxes[i], confidences[i]))
from keras.models import load_model
from keras.preprocessing.image import img_to_array
import glob
import os
from matplotlib import pyplot as plt
from difflib import SequenceMatcher
DIR = 'D:\\UFPR-ALPR dataset\\testing_seg\\'
digit_model_path = 'classification\\digit_otsu.26-0.08.hdf5'
letter_model_path = 'classification\\letter_otsu.49-0.83.hdf5'
W, H = 300, 100
print("loading detector model...")
detector = dlib.fhog_object_detector("plate_detector.svm")
print("done!")
print("loading digit model...")
digit_model = load_model(digit_model_path)
print("done!")
print("loading letter model...")
letter_model = load_model(letter_model_path)
print("done!")
all_correct = 0
six_correct = 0
five_correct = 0
total = len(glob.glob(os.path.join(DIR,'**','*.png'), recursive=True))
formatter_class=argparse.RawDescriptionHelpFormatter)
parser.add_argument('ip', help='ip for raspberry pi car')
args = parser.parse_args()
ip = args.ip # 10.42.0.235
port = '8000'
base_url = 'http://' + ip + ':' + port + '/'
actions = [
'stop', 'forward_left', 'forward', 'forward_right', 'backward_left',
'backward', 'backward_right', 'fwstraight'
]
#if(not connection_ok(base_url)):
# raise Exception('Connection failed')
detector1 = dlib.fhog_object_detector("./../detector_stop.svm")
detector2 = dlib.fhog_object_detector("./../detector_sem.svm")
detector3 = dlib.fhog_object_detector("./../detector_der.svm")
#detector4 = dlib.fhog_object_detector("./../detector_izq.svm")
detectors = [detector1, detector2]
detectors2 = [detector3]
#cap = cv2.VideoCapture(base_url + '?action=stream')
#cap = cv2.VideoCapture('http://10.42.0.235:8080/?action=stream')
#cap = cv2.VideoCapture(0)
cap = cv2.VideoCapture('./../linesData2.mp4')
ret, frame = cap.read()
if (not ret):
raise Exception('no image read')
model = load_model("./lineModels/anonymous_modelv7.h5")
#匯入必要的library
from __future__ import print_function
from imutils import paths
from scipy.io import loadmat
from skimage import io
import argparse
import dlib
import cv2
import re
import os
cup = dlib.fhog_object_detector("output/cup")
forestPark = dlib.fhog_object_detector("output/forestPark")
waterNang = dlib.fhog_object_detector("output/waterNang")
yehliu = dlib.fhog_object_detector("output/yehliu")
turtleIsland = dlib.fhog_object_detector("output/turtleIsland")
detectors = [cup, forestPark, waterNang, yehliu, turtleIsland]
detectorList = ["cup", "forestPark", "waterNang", "yehliu", "turtleIsland"]
#define user
num= 0
num = num + 1
user1 = 1
usr = "******".format(num)
print(usr)
for imgPath in paths.list_images("spot/test1"):
#load image
import os
import sys
import glob
import matplotlib.pyplot as plt
import cv2
import dlib
dataset_folder_path = "./dataset/2019-06-26/lowsize/"
svn_path = os.path.join(dataset_folder_path, "detector.svm")
landmarks_path = os.path.join(dataset_folder_path, "landmarks.dat")
detector = dlib.fhog_object_detector(svn_path)
landmarks_detector = dlib.shape_predictor(landmarks_path)
image_name = "robot_1.png"
image_path = os.path.join(dataset_folder_path, image_name)
image = dlib.load_rgb_image(image_path)
[boxes, confidences, detector_idxs] = dlib.fhog_object_detector.run(detector,
image,
upsample_num_times=1,
adjust_threshold=0.0)
for i in range(len(boxes)):
print("detector {} found box {} with confidence {}.".format(detector_idxs[i],
boxes[i],
confidences[i]))
def printLandmark(image, landmarks, color):
for p in landmarks.parts():
cv2.circle(image, (p.x, p.y), 20, color, 2)
def __init__(self, input_image):
self.input_image = input_image
self.image_data = io.imread(input_image)
self.detector = dlib.fhog_object_detector(DETECTOR_SVM)
self.predictor = dlib.shape_predictor(PREDICTOR_DAT)
self.result = DetectorResult()
print("Processing file: {}".format(f))
img = dlib.load_rgb_image(f)
dets = detector(img)
print("Number of faces detected: {}".format(len(dets)))
for k, d in enumerate(dets):
print("Detection {}: Left: {} Top: {} Right: {} Bottom: {}".format(
k, d.left(), d.top(), d.right(), d.bottom()))
win.clear_overlay()
win.set_image(img)
win.add_overlay(dets)
dlib.hit_enter_to_continue()
# Next, suppose you have trained multiple detectors and you want to run them
# efficiently as a group. You can do this as follows:
detector1 = dlib.fhog_object_detector("detector.svm")
# In this example we load detector.svm again since it's the only one we have on
# hand. But in general it would be a different detector.
detector2 = dlib.fhog_object_detector("detector.svm")
# make a list of all the detectors you wan to run. Here we have 2, but you
# could have any number.
detectors = [detector1, detector2]
image = dlib.load_rgb_image(faces_folder + '/2008_002506.jpg')
[boxes, confidences, detector_idxs] = dlib.fhog_object_detector.run_multiple(detectors, image, upsample_num_times=1, adjust_threshold=0.0)
for i in range(len(boxes)):
print("detector {} found box {} with confidence {}.".format(detector_idxs[i], boxes[i], confidences[i]))
# Finally, note that you don't have to use the XML based input to
# train_simple_object_detector(). If you have already loaded your training
# images and bounding boxes for the objects then you can call it as shown
# below.
print("Processing file: {}".format(f))
img = io.imread(f)
dets = detector(img)
print("Number of faces detected: {}".format(len(dets)))
for k, d in enumerate(dets):
print("Detection {}: Left: {} Top: {} Right: {} Bottom: {}".format(
k, d.left(), d.top(), d.right(), d.bottom()))
win.clear_overlay()
win.set_image(img)
win.add_overlay(dets)
dlib.hit_enter_to_continue()
# Next, suppose you have trained multiple detectors and you want to run them
# efficiently as a group. You can do this as follows:
detector1 = dlib.fhog_object_detector("detector_0.svm")
# In this example we load detector.svm again since it's the only one we have on
# hand. But in general it would be a different detector.
detector2 = dlib.fhog_object_detector("detector_0.svm")
# make a list of all the detectors you wan to run. Here we have 2, but you
# could have any number.
detectors = [detector1, detector2]
image = io.imread(faces_folder + '/2008_002506.jpg')
[boxes, confidences,
detector_idxs] = dlib.fhog_object_detector.run_multiple(detectors,
image,
upsample_num_times=1,
adjust_threshold=0.0)
for i in range(len(boxes)):
print("detector {} found box {} with confidence {}.".format(
detector_idxs[i], boxes[i], confidences[i]))
import numpy as np
test_folder = "img/"
df = pd.DataFrame(
100 * np.ones((6, 6)),
columns=["name", "x", "y", "w", "h", "confidences"],
)
dict = {0.0: "sz", 1.0: "cz", 2.0: "dp", 3.0: "pd", 4.0: "zb", 5.0: "mf"}
# 定义编解码器并创建 VideoWriter 对象
fourcc = cv2.VideoWriter_fourcc(*"XVID")
out = cv2.VideoWriter("predict.avi", fourcc, 20.0, (1280, 480))
detector1 = dlib.fhog_object_detector("sz.svm")
detector2 = dlib.fhog_object_detector("cz.svm")
detector3 = dlib.fhog_object_detector("dp.svm")
detector4 = dlib.fhog_object_detector("pb.svm")
detector5 = dlib.fhog_object_detector("zb.svm")
detector6 = dlib.fhog_object_detector("mf.svm")
detectors = [detector1, detector2, detector3, detector4, detector5, detector6]
def draw_circle(frame, df):
global dict
for i in range(6):
if df.iloc[i, 0] != 100:
cv2.putText(
frame,
import imutils
import dlib
import cv2
# Now let's use the detector as you would in a normal application. First we
# will load it from disk.
detector = dlib.fhog_object_detector(
"/home/vsmart/Works/trafcamanprengine/data/model/car_175x75.svm")
# Video capture source
cap = cv2.VideoCapture(
"/home/vsmart/Works/Dlib_SVM/testmodle/Oto_in_020719_13h.mp4")
# We can look at the HOG filter we learned. It should look like a face. Neat!
win_det = dlib.image_window()
win_det.set_image(detector)
win = dlib.image_window()
writer = None
while True:
ret, image = cap.read()
image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
#image = imutils.resize(image, width=800)
rects = detector(image)
for k, d in enumerate(rects):
print("Detection {}: Left: {} Top: {} Right: {} Bottom: {}".format(
k, d.left(), d.top(), d.right(), d.bottom()))
from openalpr import Alpr
import sys
import cv2
import argparse
import imutils
from camera import VideoCamera
from hog_detector import detect_car
import dlib
from carDetector import *
car_detect = carDetector()
#load car detector
detector = dlib.fhog_object_detector("../DATA/SVM/car_detector.svm")
win = dlib.image_window()
# construct the argument parse and parse the arguments
ap = argparse.ArgumentParser()
ap.add_argument("-v", "--video", help="path to the (optional) video file")
ap.add_argument("-b", "--buffer", type=int, default=64, help="max buffer size")
ap.add_argument("-m", "--model", help="path to trained model")
args = vars(ap.parse_args())
if not args.get("video", False):
video_camera = VideoCamera(0)
# otherwise, grab a reference to the video file
else:
video_camera = VideoCamera(args["video"])
def read_number_plate(im):
import os,sys,glob,dlib,cv2
from skimage import io
cord_pts=[]
detector = dlib.fhog_object_detector("anpr_detector.svm")
img = io.imread(sys.argv[1])
dets = detector(img)
for k, d in enumerate(dets):
cord_pts.append(d.left())
cord_pts.append(d.top())
cord_pts.append(d.right())
cord_pts.append(d.bottom())
print cord_pts
anpr_part = img[cord_pts[1]:cord_pts[3],max(0,cord_pts[0]):min(cord_pts[2],cord_pts[2])]
cv2.imwrite("anpr3.jpg",anpr_part)
def load_dlib_detector(self):
print("Loading detector...")
self.detectors = [dlib.fhog_object_detector("../data/models/detector_mtb.svm"), dlib.fhog_object_detector("../data/models/detector_ped.svm")]
def add_detector(self, det):
self._detectors.append(dlib.fhog_object_detector(det))
