class MainLoop(Thread):
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 run(self):
if Values.PRINT_FPS:
last_time = time.time()
ind = 0
while True:
try:
if self.stop_loop:
break
#self.frame = cv2.imread("images/pole_new.jpg")
self.frame = self.camera.get_frame()
#self.frame = cv2.resize(self.frame, (Values.CAMERA_WIDTH, Values.CAMERA_HEIGHT))
if self.frame is None:
continue
detections = self.detector.detect(self.frame)
self.telemetry.update_telemetry()
self.position_calculator.update_meters_per_pixel(
self.telemetry.altitude)
self.position_calculator.calculate_max_meters_area()
for d in detections:
lat, lon = self.position_calculator.calculate_point_lat_long(
d.middle_point, self.telemetry.latitude,
self.telemetry.longitude, self.telemetry.azimuth)
d.update_lat_lon(lat, lon)
d.area_m = self.position_calculator.calculate_area_in_meters_2(
d.area)
d.draw_detection(self.frame)
cv2.imshow("frame", self.frame)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
if Values.PRINT_FPS:
ind += 1
if time.time() - last_time > 1:
print("FPS:", ind)
ind = 0
last_time = time.time()
except Exception as e:
print("Some error accrued: ", str(e))
self.close()
def close(self):
self.camera.close()
self.stop_loop = True
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 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
if bot > height:
y_offset -= 5
bot = height
if top < 0:
y_offset += 5
top = 0
if right > width:
x_offset -= 5
right = width
if left < 0:
x_offset += 5
left = 0
prepared_frame = frame[top:bot, left:right]
print(left, right, top, bot, altitude)
detections = detector.detect(prepared_frame)
for d in detections:
d.draw_detection(prepared_frame)
cv2.imshow("frame", prepared_frame)
key = cv2.waitKey(0)
print(key)
if key == 97:
x_offset -= 5
elif key == 100:
x_offset += 5
elif key == 119:
y_offset -= 5
elif key == 115: