def main(image_file):
detector = Detector(weight_dir=MODEL_WEIGHT_SAVE_DIR,
mode=3,
min_face_size=24)
input_img_full_path = TEST_INPUT_IMG_DIR + '/' + image_file
output_img_full_path = TEST_OUTPUT_IMG_DIR + '/' + image_file
image = cv2.imread(input_img_full_path)
bbox, bboxes, landmarks = detector.predict(image)
print('bboxes-shape---:', bboxes.shape)
print('landmarks-shape---:', landmarks.shape)
for bbox in bboxes:
#print('bbox score--:',bbox[4])
#cv2.putText(image,str(np.round(bbox[4],2)),(int(bbox[0]),int(bbox[1])),cv2.FONT_HERSHEY_TRIPLEX,1,color=(255,0,255))
cv2.rectangle(image, (int(bbox[0]), int(bbox[1])),
(int(bbox[2]), int(bbox[3])), (0, 0, 255))
for landmark in landmarks:
#print('landmark-shape---:',landmark.shape)
#print('landmark----:',landmark)
for i in range(0, 5):
cv2.circle(image,
(int(landmark[2 * i]), int(int(landmark[2 * i + 1]))),
3, (0, 0, 255))
#break
cv2.imwrite(output_img_full_path, image)
cv2.imshow('yy', image)
cv2.waitKey(0)
def main():
print("Running Task")
camera = Camera()
d = Detector()
rs = d.detect_image(camera.capture())
print(rs)
for obj, confidence in rs.items():
print('Object {} : Confidence {}'.format(obj, confidence))
def __init__(self):
Thread.__init__(self)
self.camera = Camera()
self.detector = Detector()
self.stop_loop = False
self.telemetry = Telemetry()
self.position_calculator = PositionCalculator()
self.frame = None
def detection(d):
det = Detector(detector_name='mobilenet_ssd', config_path='./detectors.cfg')
while True:
if(d['tracker_ready'] == True):
results = det.detect_image_frame(d['%d_frame'%d['index']], to_xywh=True)
d['%d_boxes'%d['index']] = np.array([result[1:5] for result in results])
d['%d_scores'%d['index']] = np.array([result[5] for result in results])
d['tracker_ready'] = False
d['detector_ready'] = True
def detect(cluster, id):
img_catalog = make_camera_img_catalog(cluster, id)
predictor = Detector(config_path='detector/configs/faster_rcnn_R_101_FPN_3x.yaml',
weight_path='detector/configs/faster_rcnn_R_101.pkl')
img_catalog = add_static_to_catalog(img_catalog)
bboxes_catalog = 'static/img/cameras/cluster_' + str(cluster) + '/camera_' + str(id) + '/detected'
clean_directory(bboxes_catalog)
camera_cluster_id = str(cluster) + str(id)
predictor.save_bboxes(img_catalog, camera_cluster_id)
bboxes = make_camera_detected_img_catalog(cluster, id)
return render_template('detected_images.html', image_catalog=bboxes, cluster=cluster, id=id)
def load_model():
#
cropper = Cropper()
#
detector = Detector(config.DETECTOR_CFG, config.DETECTOR_WEIGHT, config.DETECTOR_LABELS)
#
reader_config = Cfg.load_config_from_file(config.READER_CFG)
reader_config['weights'] = config.READER_WEIGHT
reader_config['device'] = config.DEVICE
reader = Reader(reader_config)
#
return cropper, detector, reader
def test_detector_image(self):
# Create Localizer
cfg_path = './data/yolo/full/trafficsigns.cfg'
weights_path = './data/yolo/full/trafficsigns.weights'
threshold = 0.24
localizer = Localizer(cfg_path, weights_path, threshold, gpu=0.0)
# Create cropper
crop_percent = 0.25
force_square = True
cropper = Cropper(crop_percent, force_square)
# Create classifier
model_path = './data/classifier/trafficsigns.json'
weights_path = './data/classifier/trafficsigns.h5'
labels_path = './data/classifier/classes.txt'
threshold = 0.5
classifier = Classifier(model_path, weights_path, labels_path,
threshold)
# Create detection pipeline
detection_pipeline = DetectionPipeline(localizer, cropper, classifier)
# Create detector
images_path = './data/classifier/classes/'
sounds_path = './data/sounds/'
detector = Detector(detection_pipeline, images_path, sounds_path)
# Detect on image
img = cv2.imread('./tests/data/test.png')
image, detections = detector.detect_image(img,
show_confidence=True,
return_image=True)
true_detections = [{
'class_id': 15,
'coordinates': [1434, 456, 1590, 612],
'label': 'max-60',
'confidence': 1.0
}]
assert detections == true_detections
def test_detector_video(self):
# Create Localizer
cfg_path = './data/yolo/full/trafficsigns.cfg'
weights_path = './data/yolo/full/trafficsigns.weights'
threshold = 0.24
localizer = Localizer(cfg_path, weights_path, threshold, gpu=0.0)
# Create cropper
crop_percent = 0.25
force_square = True
cropper = Cropper(crop_percent, force_square)
# Create classifier
model_path = './data/classifier/trafficsigns.json'
weights_path = './data/classifier/trafficsigns.h5'
labels_path = './data/classifier/classes.txt'
threshold = 0.5
classifier = Classifier(model_path, weights_path, labels_path,
threshold)
# Create detection pipeline
detection_pipeline = DetectionPipeline(localizer, cropper, classifier)
# Create detector
images_path = './data/classifier/classes/'
sounds_path = './data/sounds/'
detector = Detector(detection_pipeline, images_path, sounds_path)
# Detect on video
video_feed = './tests/data/test.mp4'
output_mp4 = './tests/data/test_output.mp4'
output_csv = './tests/data/test_output.csv'
exit_code = detector.detect_video_feed(video_feed,
output=output_mp4,
output_csv=output_csv,
show_confidence=True,
sound_notifications=True)
assert exit_code == True
async def main():
log_levels = ['CRITICAL', 'ERROR', 'WARNING', 'INFO', 'DEBUG']
config_template = {
'devices': confuse.Sequence(BluetoothDeviceConfuseTemplate(), ),
'log_level': confuse.Choice(log_levels, default=DEFAULT_LOG_LEVEL),
'mqtt': {
'enabled':
confuse.Choice([True, False], default=DEFAULT_MQTT_ENABLED),
'host':
confuse.String(default=DEFAULT_MQTT_HOST),
'log_level':
confuse.Choice(['ERROR', 'WARNING', 'INFO', 'DEBUG'],
default=DEFAULT_MQTT_LOG_LEVEL),
'port':
confuse.Integer(default=DEFAULT_MQTT_PORT),
'protocol':
confuse.String(default=DEFAULT_MQTT_PROTOCOL),
},
'scheduler': {
'log_level':
confuse.Choice(log_levels, default=DEFAULT_SCHEDULER_LOG_LEVEL),
},
}
config = confuse.Configuration('bluetooth_tracker',
__name__).get(config_template)
kwargs = {}
kwargs['bluetooth_rssi_scanner'] = FakeBluetoothScanner
kwargs['bluetooth_lookup_name_func'] = lambda *_: 'test'
detector = Detector(
config,
mqtt.Client(),
AsyncIOScheduler(),
**kwargs,
)
detector.start_detecting()
killer = GracefulKiller()
while not killer.kill_now:
await asyncio.sleep(1)
logging.info('shutting down')
detector.stop_detecting()
def yolo():
image = cv2.imread(args["image"])
weightsPath = get_yolo_path("yolov3.weights")
configPath = get_yolo_path("yolov3.cfg")
labelsPath = get_yolo_path("coco.names")
LABELS = open(labelsPath).read().strip().split("\n")
np.random.seed(42)
COLORS = np.random.randint(0, 255, size=(len(LABELS), 3), dtype="uint8")
# load our YOLO object detector trained on COCO dataset (80 classes)
print("[INFO] loading YOLO from disk...")
net = cv2.dnn.readNetFromDarknet(configPath, weightsPath)
detector = Detector(net, image, args['confidence'], args['threshold'],
LABELS, COLORS)
img = detector.detect()
cv2.imshow("Image", img)
cv2.waitKey(0)
def callback(data):
bridge = CvBridge()
height, width = 1024, 1280
detector = Detector(height=height, width=width, is_live=False)
try:
image = bridge.imgmsg_to_cv2(data, "bgr8")
except CvBridgeError as e:
print(e)
data = detector.detection_by_frame(image)
print('parking center found = ', data['found'])
#cv2.imwrite('/home/artemon12/PycharmProjects/parking_diploma/pics/lot_res1.jpg', image)
out.write(image)
if data['found']:
quat = quaternion_from_euler(0, 0, np.pi / 2)
x_posititon = 0.7188 + data['parking_params']['x']
y_posititon = -1 * (0.7967 + data['parking_params']['y'])
position = PoseStamped()
position.header.seq = 0
position.header.stamp = rospy.Time.now()
position.header.frame_id = 'map'
position.pose.position = Point(x_posititon, y_posititon, 0)
position.pose.orientation = Quaternion(*quat)
if data['found'] and TRIGGER is not True:
global TIME
if TIME is 0:
TIME = data['time']
POINTS.append(data['parking_params']['center'])
if data['time'] - TIME > 1.5:
global TRIGGER
global POINTS
average = np.std(POINTS)
if average < 5:
parking_pub.publish(position)
TRIGGER = True
def test_detector_image_output_json(self):
# Create Localizer
cfg_path = './data/yolo/full/trafficsigns.cfg'
weights_path = './data/yolo/full/trafficsigns.weights'
threshold = 0.24
localizer = Localizer(cfg_path, weights_path, threshold, gpu=0.0)
# Create cropper
crop_percent = 0.25
force_square = True
cropper = Cropper(crop_percent, force_square)
# Create classifier
model_path = './data/classifier/trafficsigns.json'
weights_path = './data/classifier/trafficsigns.h5'
labels_path = './data/classifier/classes.txt'
threshold = 0.5
classifier = Classifier(model_path, weights_path, labels_path,
threshold)
# Create detection pipeline
detection_pipeline = DetectionPipeline(localizer, cropper, classifier)
# Create detector
images_path = './data/classifier/classes/'
sounds_path = './data/sounds/'
detector = Detector(detection_pipeline, images_path, sounds_path)
# Detect on image
img = cv2.imread('./tests/data/test.png')
output_filename = './tests/data/detector_image.json'
detections = detector.detect_image(img, output=output_filename)
assert os.path.exists(output_filename) == True
def main(input_net_name):
net_name = input_net_name
assert net_name in NET_NAMES
images_dir = WIDER_FACE_IMG_DIR
annotation_file = WIDER_FACE_ANNO_FILE
out_dir = GAN_DATA_ROOT_DIR
out_dir = '{}/{}'.format(out_dir, net_name)
if net_name == 'r_net':
mode = 1
elif net_name == 'o_net':
mode = 2
#images_path images_jpg bboxes
dataset = load_widerface_dataset(images_dir, annotation_file)
detector = Detector(weight_dir= MODEL_WEIGHT_SAVE_DIR, mode=mode)
bboxes_all = []
#p_net 一次测一张图片，注意，其返回可能会有多个，因为图片中可以包含多张面，而且还有图片金字塔
print('data img len --:',len(dataset['images_jpg']))
for img_jpg in dataset['images_jpg']:
_, bboxes, _ = detector.predict(img_jpg)
bboxes_all.append(bboxes)
bboxes_all = np.array(bboxes_all)
print('predict over---')
if not os.path.exists(out_dir):
os.mkdir(out_dir)
detections_path = os.path.join(out_dir, 'middle_wideface_data_det.pkl')
with open(detections_path, 'wb') as f:
pickle.dump({
'bboxes': bboxes_all,
}, f)
save_hard_examples(net_name, out_dir, dataset, detections_path)
def __init__(self, args):
self.args = args
self.num_classes = 4
self.device = 'cuda' if torch.cuda.is_available() else 'cpu'
assert self.device == 'cuda', 'CUDA is not available'
images_folder = os.path.join(args.images_root, args.videoname)
self.dataset = Vehicle(images_folder,
model=args.model_type,
image_size_effdet=args.image_size_effdet)
self.loader = DataLoader(self.dataset,
batch_size=args.batch_size,
shuffle=False,
sampler=SequentialSampler(self.dataset),
num_workers=args.num_workers,
pin_memory=False,
drop_last=False)
self.detector = Detector(args.weight, args.conf_thres, args.iou_thres,
args.model_type, args.image_size_effdet)
self.tracker = JDETracker(max_age=args.max_age,
buffer_size=args.buffer_size,
det_thresh=args.conf_thres,
thresh1=args.tracker_thresh1,
thresh2=args.tracker_thresh2,
thresh3=args.tracker_thresh3)
self.track_features_type = args.track_features_type
self.save_detects = args.save_detects
self.detect_outputs = args.detect_outputs
self.detect_results = []
self.track_outputs = args.track_outputs
self.track_results = []
from detector.detector import Detector
if __name__ == '__main__':
det = Detector(detector_name='yolo', config_path='config.cfg')
# detect_image_test
#det.detect_image('./_samples/MOT17-09-FRCNN/img1/000055.jpg')
# detect_video_test
#det.detect_video('./_samples/MOT17-09-FRCNN.mp4')
# detect_webcam_test
#det.detect_webcam()
#det = Detector(detector_name='mobilenet', config_path='detectors.cfg')
#det.detect_webcam()
# Detect image sequence
from rpi import rec_utils
from rpi import pi_utils
import utils.audio_utils as au
from utils.state_machine import StateMachine
from detector.detector import Detector
import pickle
DIRECTORY = "records_detector"
MODEL_DIR = "models/svc.pickle"
if __name__ == "__main__":
rec_saver = au.RecSaver(DIRECTORY)
with open(MODEL_DIR, "rb") as model_file:
detector = Detector(pickle.load(model_file))
audio_rec = rec_utils.AudioRecorder()
state_machine = StateMachine(audio_rec, detector, rec_saver)
button = pi_utils.Button(4)
while 1:
button_pressed = button.check_value()
if button_pressed:
state_machine.change_state()
state_machine.run()
from telemetry.telemetry import Telemetry
from positionCalculator.positionCalculator import PositionCalculator
import cv2
from settings.settings import Values
from detector.detector import Detector
import time
if __name__ == '__main__':
frame = cv2.imread("images/pole.png")
telemetry = Telemetry()
positionCalculator = PositionCalculator()
detector = Detector()
width, height = frame.shape[1::-1]
altitude = 10
x_offset = 0
y_offset = 0
azimuth = 0
telemetry.update_telemetry_manually(width / 2, height / 2, azimuth,
altitude)
try:
if Values.PRINT_FPS:
last_time = time.time()
ind = 0
while True:
frame = cv2.imread("images/pole.png")
left = x_offset + int(width / 2 - width * altitude / 30)
right = x_offset + int(width / 2 + width * altitude / 30)
top = y_offset + int(height / 2 - height * altitude / 30)
def predictcmnd(self, image):
# check cccd
"""
=========================================
===== Crop and align id card image
=========================================
"""
request = predict_pb2.PredictRequest()
# model_name
request.model_spec.name = "cropper_cmnd_model"
# signature name, default is 'serving_default'
request.model_spec.signature_name = "serving_default"
# preprocess image
img, original_image, original_width, original_height = preprocess_image(
image, Cropper.TARGET_SIZE)
if img.ndim == 3:
img = np.expand_dims(img, axis=0)
# request to cropper model
request.inputs["input_1"].CopyFrom(
tf.make_tensor_proto(img, dtype=np.float32, shape=img.shape))
try:
result = self.stub.Predict(request, 10.0)
result = result.outputs["tf_op_layer_concat_14"].float_val
result = np.array(result).reshape((-1, 9))
except Exception as e:
print("Cropper cmnd = ", e)
cropper = Cropper()
cropper.set_best_bboxes(result,
original_width=original_width,
original_height=original_height,
iou_threshold=0.5)
# respone to client if image is invalid
if cropper.respone_client(threshold_idcard=0.8) == -1:
# print(cropper.respone_client(threshold_idcard=0.8))
return
elif cropper.respone_client(threshold_idcard=0.8) == 0:
# print("no cropper")
# cv2.imwrite('app/static/aligned_images/' + filename, original_image)
aligned_image = cv2.cvtColor(original_image, cv2.COLOR_BGR2RGB)
else:
# print("cropper image")
cropper.set_image(original_image=original_image)
# print("Cropper cmnd end")
# output of cropper part
aligned_image = getattr(cropper, "image_output")
cv2.imwrite('storage/c.jpg', aligned_image)
aligned_image = cv2.cvtColor(aligned_image, cv2.COLOR_BGR2RGB)
"""
===========================================
==== Detect informations in aligned image
===========================================
"""
# preprocess aligned image
original_height, original_width, _ = aligned_image.shape
img = cv2.resize(aligned_image, Detector.TARGET_SIZE)
img = np.float32(img / 255.)
# model_name
request.model_spec.name = "detector_cmnd_model"
# signature name, default is 'serving_default'
request.model_spec.signature_name = "serving_default"
if img.ndim == 3:
img = np.expand_dims(img, axis=0)
# new request to detector model
request.inputs["input_1"].CopyFrom(
tf.make_tensor_proto(img, dtype=np.float32, shape=img.shape))
try:
# print("Detect cmnd = ok")
result = self.stub.Predict(request, 10.0)
result = result.outputs["tf_op_layer_concat_14"].float_val
result = np.array(result).reshape((-1, 13))
# print("Detect cmnd = end")
except Exception as e:
print("Detect cmnd = ", e)
detector = Detector()
detector.set_best_bboxes(result,
original_width=original_width,
original_height=original_height,
iou_threshold=0.5)
detector.set_info_images(original_image=aligned_image)
# output of detector part
info_images = getattr(detector, "info_images")
return info_images
import base64, cv2, random
import numpy as np
from flask import Flask, request, jsonify
from detector.detector import Detector, OP_RECT, OP_KEYPOINTS
app = Flask(__name__)
det = Detector()
@app.route('/detect', methods=['POST'])
def detect():
image_base64 = request.json['image']
padding = request.json.get('padding')
if padding is None:
padding = 0
keypoints = request.json.get('keypoints')
if keypoints is None:
keypoints = False
rect = request.json.get('rect')
if rect is None:
rect = False
crop = request.json.get('crop')
if crop is None:
crop = False
rgb = request.json.get('rgb')
# OpenCV works with BGR, so we reverse RGB
rgb = rgb[::-1] if rgb else None
print('rgb is ', rgb)
npimg = np.fromstring(base64.b64decode(image_base64), np.uint8)
img = cv2.imdecode(npimg, cv2.IMREAD_COLOR)
def __init__(self,
detector_name,
tracker_name,
config_path='./config.cfg'):
self.det = Detector(detector_name, config_path)
self.tra = Tracker_temp(tracker_name, config_path)
def run(opt):
# output dir
if os.path.exists(opt.save_dir):
shutil.rmtree(opt.save_dir)
os.makedirs(opt.save_dir)
# load dataset
dataset = Dataloader(source=opt.source, imgsz=opt.img_size).dataset
# load object detection model, and weights
detector = Detector(detector_type=opt.detector_type,
cfg_file=opt.detector_cfg_file)
detector.run_through_once(opt.img_size) # 空跑一次
# load object tracking model
tracker = Tracker(tracker_type=opt.tracker_type,
cfg_file=opt.tracker_cfg_file)
# load pose detection model
poser = Poser(poser_type=opt.poser_type, cfg_file=opt.poser_cfg_file)
# load classifier model
clssifier = Classifier(classifier_type=opt.classifier_type,
cfg_file=opt.classifier_cfg_file)
print(detector.device, detector.cfg)
filt_with_txt = False # 先分析一下status标注文件.txt，存在的才进行检测，这样能加快速度
if filt_with_txt:
from classifier.data_analyse import anaylise_label
label_ret = anaylise_label()
label_stems = [x[0] for x in label_ret]
for img_idx, (path, img, im0s, vid_cap) in enumerate(dataset):
# print(type(img), type(im0s))
# print(type(im0s), im0s.shape)
if dataset.is_camera:
im0s = im0s[0]
path = f'{path[0]}/{img_idx:0<6}.jpg'
if filt_with_txt:
fold_stem = path.split('/')[-2]
idx = label_stems.index(fold_stem)
# print(fold_stem, label_stems, idx)
img_stem = Path(path).stem
valid_stems = [Path(x).stem for x in label_ret[idx][-1]]
in_it = f'track_{img_stem}' in valid_stems
# print(path, in_it, label_ret[idx][-1][0])
if not in_it:
continue
# img: [3, w, h], preprocess, inference, NMS,
det_ret = detector.detect(
path, img,
im0s) # detect result: nparray, [num_obj, 6] 6: xyxy,conf,cls
# detector.imshow(im0s, det_ret)
# track
tra_ret = tracker.track(
det_ret,
im0s) # track result: list, [num_obj, 7], 7: xyxy, cls, tid, trace
# print(tra_ret[:, 5])
# tracker.imshow(im0s, tra_ret, path)
# pose detect
pose_ret = poser.detect_pose(tra_ret, im0s, path, return_type='zzd')
# zzd format: np.array(object): [num_obj, 10]，10: xyxy cls tid trace keypoints kp_score proposal_score
# print(pose_ret)
poser.imshow(im0s, pose_ret, path, resize=(1280, 720))
# classifier
if opt.feature_save_dir is not None: # 保存特征的
clssifier.build_and_save_feature(pose_ret,
path,
save_dir=opt.feature_save_dir)
print(f'\rsaving features: [{img_idx + 1:>3}/{len(dataset)}] ',
end='')
continue
# status_ret = clssifier.detect_status(pose_ret, path, is_camera=dataset.is_camera)
# zzd format: np.array(object): [num_obj, 12], 12: 比10多了status_idx和status
# clssifier.imshow(im0s, status_ret, show_name='x', resize=(1280, 720))
# print(status_ret)
if img_idx == 10:
if cv2.waitKeyEx(0) == ord('q'):
raise StopIteration
from camera.camera import VideoCamera
from detector.detector import Detector
import configparser
import sys
if len(sys.argv) < 2:
print('Usage:', sys.argv[0], '<config_file>')
print('Example:', sys.argv[0], 'config/config.cfg')
exit(0)
app = Flask(__name__)
config = configparser.ConfigParser()
config.read(sys.argv[1])
detector = Detector(config['tflite']['model'],
config['tflite'].getboolean('use_edgetpu'))
labels = config['tflite']['labels']
src0 = config['cameras'].getint('src0')
threshold = config['cameras'].getfloat('threshold')
@app.route('/')
def index():
return render_template('index.html')
def gen(camera):
while True:
frame = camera.get_frame()
yield (b'--frame\r\n'
def predict(filename):
channel = grpc.insecure_channel("localhost:8500")
stub = prediction_service_pb2_grpc.PredictionServiceStub(channel)
request = predict_pb2.PredictRequest()
# model_name
request.model_spec.name = "cropper_model"
# signature name, default is 'serving_default'
request.model_spec.signature_name = "serving_default"
start = time.time()
"""
=========================================
===== Crop and align id card image
=========================================
"""
filepath = app.config["IMAGE_UPLOADS"] + "/" + filename
# preprocess image
img, original_image, original_width, original_height = preprocess_image(
filepath, Cropper.TARGET_SIZE)
if img.ndim == 3:
img = np.expand_dims(img, axis=0)
# request to cropper model
request.inputs["input_1"].CopyFrom(
tf.make_tensor_proto(img, dtype=np.float32, shape=img.shape))
try:
result = stub.Predict(request, 10.0)
result = result.outputs["tf_op_layer_concat_14"].float_val
result = np.array(result).reshape((-1, 9))
except Exception as e:
print(e)
cropper = Cropper()
cropper.set_best_bboxes(result,
original_width=original_width,
original_height=original_height,
iou_threshold=0.5)
# respone to client if image is invalid
if not cropper.respone_client(threshold_idcard=0.8):
return render_template('upload_image_again.html')
cropper.set_image(original_image=original_image)
# output of cropper part
aligned_image = getattr(cropper, "image_output")
cv2.imwrite('app/static/aligned_images/' + filename, aligned_image)
aligned_image = cv2.cvtColor(aligned_image, cv2.COLOR_BGR2RGB)
"""
===========================================
==== Detect informations in aligned image
===========================================
"""
# preprocess aligned image
original_height, original_width, _ = aligned_image.shape
img = cv2.resize(aligned_image, Detector.TARGET_SIZE)
img = np.float32(img / 255.)
# model_name
request.model_spec.name = "detector_model"
# signature name, default is 'serving_default'
request.model_spec.signature_name = "serving_default"
if img.ndim == 3:
img = np.expand_dims(img, axis=0)
# new request to detector model
request.inputs["input_1"].CopyFrom(
tf.make_tensor_proto(img, dtype=np.float32, shape=img.shape))
try:
result = stub.Predict(request, 10.0)
result = result.outputs["tf_op_layer_concat_14"].float_val
result = np.array(result).reshape((-1, 13))
except Exception as e:
print(e)
detector = Detector()
detector.set_best_bboxes(result,
original_width=original_width,
original_height=original_height,
iou_threshold=0.5)
detector.set_info_images(original_image=aligned_image)
# output of detector part
info_images = getattr(detector, "info_images")
"""
=====================================
==== Reader infors from infors image
=====================================
"""
keys = list(info_images.keys())
keys.remove("thoi_han")
keys.remove("chan_dung")
infors = dict()
# init default value of quoc_tich, dan_toc
infors['quoc_tich'] = ""
infors['dan_toc'] = ""
if "quoc_tich" in keys:
infors['quoc_tich'] = ["Việt Nam"]
keys.remove("quoc_tich")
if "sex" in keys:
info_image = info_images["sex"]
infors["sex"] = list()
for i in range(len(info_image)):
img = info_image[i]['image']
s = reader.predict(img)
if "Na" in s:
infors["sex"].append("Nam")
else:
infors["sex"].append("Nữ")
keys.remove("sex")
if "dan_toc" in keys:
info_image = info_images["dan_toc"]
infors["dan_toc"] = list()
for i in range(len(info_image)):
img = info_image[i]['image']
s = reader.predict(img)
s = s.split(" ")[-1]
infors["dan_toc"].append(s)
keys.remove("dan_toc")
for key in keys:
infors[key] = list()
info_image = info_images[key]
for i in range(len(info_image)):
img = info_image[i]['image']
s = reader.predict(img)
infors[key].append(s)
que_quan_0 = infors['que_quan'][0]
que_quan_1 = ''
noi_thuong_tru_0 = infors['noi_thuong_tru'][0]
noi_thuong_tru_1 = ''
if len(infors['que_quan']) == 2:
que_quan_1 = infors['que_quan'][1]
if len(infors['noi_thuong_tru']) == 2:
noi_thuong_tru_1 = infors['noi_thuong_tru'][1]
print("total_time:{}".format(time.time() - start))
return render_template('predict.html',
id=infors['id'][0].replace(" ", ""),
full_name=infors['full_name'][0],
date_of_birth=infors['date_of_birth'][0],
sex=infors['sex'][0],
quoc_tich=infors['quoc_tich'],
dan_toc=infors['dan_toc'],
que_quan_0=que_quan_0,
que_quan_1=que_quan_1,
noi_thuong_tru_0=noi_thuong_tru_0,
noi_thuong_tru_1=noi_thuong_tru_1,
filename=str(filename))
from detector.detector import Detector import pandas as pd # input image input_path = '/media/herval/Save/School/THB/Master/Semestre3/Projekt/data/processed/test/test_sub_image_x1-2692_y1-2512_x2-3320_y2-3525.jpeg' # output folder output_path = '/media/herval/Save/School/THB/Master/Semestre3/Projekt/data/processed/test/outputs' # real centers real_centers_path = '/media/herval/Save/School/THB/Master/Semestre3/Projekt/data/processed/test/test_sub_image_x1-2692_y1-2512_x2-3320_y2-3525_marked.csv' real = pd.read_csv(real_centers_path, sep=',', header=0).values[:, 1:] # instantiate the detector detector = Detector(input_path, output_path) detector.recognize() detector.mapping(real)
from camera.threadcamera import ThreadCamera
from detector.detector import Detector
from detector.threaddetector import ThreadDetector
import signal
signal.signal(signal.SIGINT, signal.SIG_DFL)
if __name__ == '__main__':
camera = Camera()
print(camera)
app = QtGui.QApplication(sys.argv)
window = Gui(camera)
detector = Detector(camera, window)
print(detector)
t_cam = ThreadCamera(camera)
t_cam.start()
t_detector = ThreadDetector(detector)
t_detector.start()
window.setCamera(camera, t_cam)
window.show()
window.setDetector(detector, t_detector)
window.show()
t_gui = ThreadGui(window)
t_gui.start()