def Init_tracker(firstframe, position, session, network):
frame = firstframe
height, width = frame.shape[:2]
if len(frame.shape) == 3:
is_color = True
# 彩色图每张（frame）为[h,w,3]的形式
else:
is_color = False
frame = frame[:, :, np.newaxis]
# 灰度图每张转化为[h,w,1]的形式
# 目标位置信息posision的格式为(xmin,ymin,w,h)
tracker = ECOTracker(is_color, session, network)
# 初始化追踪器（类）
# 第一帧操作
bbox = position
# 目标初始位置（第一帧位置）
tracker.init(frame, bbox)
bbox = (bbox[0] - 1, bbox[1] - 1, bbox[0] + bbox[2] - 1,
bbox[1] + bbox[3] - 1)
# bbox改为左上角和右下角的位置
frame = cv2.rectangle(frame, (int(bbox[0]), int(bbox[1])),
(int(bbox[2]), int(bbox[3])), (0, 255, 0), 2)
frame = frame.squeeze()
# 真实位置画框
if len(frame.shape) == 3:
frame = cv2.cvtColor(frame, cv2.COLOR_RGB2BGR)
# 由于cv2的imshow图像格式为BGR，而Image读取的图像格式为RGB，转换
frame = cv2.putText(frame, str('start'), (5, 20),
cv2.FONT_HERSHEY_COMPLEX_SMALL, 1, (0, 255, 0), 1)
# 在图像上显示帧数
cv2.imshow('track', frame)
cv2.waitKey(30)
return tracker
def init_data(self):
self.src_video_path = None
self.video_len = None
self.cur_frame_id = 0
self.tmr = QTimer()
self.time = QTime()
self.proc_time = 40
self.tmr.setInterval(self.proc_time)
self.tmr.timeout.connect(self.timeout_handler)
self.tmr.start()
self.time.start()
self.fps_period = None
self.last_timestamp = self.time.elapsed()
self.video_capturer = None
self.frame = None
self.prev_frame = None
self.frame_draw = None
self.frame_crop = None
self.proc_qimage = None
self.proc_qpixmap = None
self.amount_of_frames = None
self.feat_extr = Feature_Extractor()
self.bay_cl = BayesClassifier()
self.proceed_frame = Proceed_Frame()
self.tracker = Tracker(4, 15, 30, 31)
self.orb = orb()
self.dist_thresh = 4
self.max_frames_to_skip = 15
self.size_orb_area = 30
self.left_corner = None
self.right_corner = None
self.left_corner_real = None
self.right_corner_real = None
self.kp_select = None
self.nkeypoints = 300
self.brief_size = 31
self.begin = QtCore.QPoint()
self.end = QtCore.QPoint()
self.width = None
self.height = None
self.enable_playing = False
self.fern_det = False
self.orb_det = False
self.harris_det = False
self.enable_clust = False
self.enable_tracking_orb = False
self.enable_tracking_Hungarian = False
self.enable_tracking_hung_orb = False
self.enable_eco = False
self.camera_id = 0
self.camera_capturer = cv2.VideoCapture(self.camera_id)
self.camera_frame = None
self.video_frame = None
self.video_src = 'video'
self.clust_kps = []
self.en_roi = False
self.bbox_eco = ()
self.bbox_init_eco = ()
self.tracker_eco = ECOTracker(True)
pass
def run_ECO(seq, rp, saveimage):
x = seq.init_rect[0]
y = seq.init_rect[1]
w = seq.init_rect[2]
h = seq.init_rect[3]
frames = [np.array(Image.open(filename)) for filename in seq.s_frames]
# frames = [cv2.cvtColor(cv2.imread(filename), cv2.COLOR_BGR2RGB) for filename in seq.s_frames]
if len(frames[0].shape) == 3:
is_color = True
else:
is_color = False
frames = [frame[:, :, np.newaxis] for frame in frames]
tic = time.clock()
# starting tracking
tracker = ECOTracker(is_color)
res = []
for idx, frame in enumerate(frames):
if idx == 0:
bbox = (x, y, w, h)
tracker.init(frame, bbox)
bbox = (bbox[0], bbox[1], bbox[0] + bbox[2], bbox[1] + bbox[3])
elif idx < len(frames) - 1:
bbox = tracker.update(frame, True)
else: # last frame
bbox = tracker.update(frame, False)
res.append((bbox[0], bbox[1], bbox[2] - bbox[0], bbox[3] - bbox[1]))
duration = time.clock() - tic
result = {}
result['res'] = res
result['type'] = 'rect'
result['fps'] = round(seq.len / duration, 3)
return result
def main(video_dir):
# load videos
filenames = sorted(glob.glob(os.path.join(video_dir, "*.jpg")),
key=lambda x: int(os.path.basename(x).split('.')[0]))
# frames = [cv2.cvtColor(cv2.imread(filename), cv2.COLOR_BGR2RGB) for filename in filenames]
frames = [np.array(Image.open(filename)) for filename in filenames]
height, width = frames[0].shape[:2]
if len(frames[0].shape) == 3:
is_color = True
else:
is_color = False
frames = [frame[:, :, np.newaxis] for frame in frames]
gt_bboxes = pd.read_csv(
os.path.join(video_dir, "groundtruth.txt"),
sep='\t|,| ',
header=None,
names=['x1', 'y1', 'x2', 'y2', 'x3', 'y3', 'x4', 'y4'],
engine='python')
title = video_dir.split('/')[-1]
# starting tracking
tracker = ECOTracker(is_color)
for idx, frame in enumerate(frames):
if idx == 0:
bbox = gt_bboxes.iloc[0].values
xmin = np.min(bbox[::2])
xmax = np.max(bbox[::2])
ymin = np.min(bbox[1::2])
ymax = np.max(bbox[1::2])
bbox = (xmin, ymin, xmax - xmin + 1, ymax - ymin + 1)
tracker.init(frame, bbox)
bbox = (xmin, ymin, xmax, ymax)
elif idx < len(frames) - 1:
bbox = tracker.update(frame, True)
else: # last frame
bbox = tracker.update(frame, False)
# bbox xmin ymin xmax ymax
frame = cv2.rectangle(frame, (int(bbox[0] - 1), int(bbox[1] - 1)),
(int(bbox[2] - 1), int(bbox[3] - 1)),
(0, 255, 0), 2)
gt_bbox = gt_bboxes.iloc[idx].values
xmin = np.min(gt_bbox[::2])
xmax = np.max(gt_bbox[::2])
ymin = np.min(gt_bbox[1::2])
ymax = np.max(gt_bbox[1::2])
gt_bbox = (xmin, ymin, xmax, ymax)
frame = frame.squeeze()
frame = cv2.rectangle(
frame,
(int(gt_bbox[0] - 1), int(gt_bbox[1] - 1)), # 0-index
(int(gt_bbox[2] - 1), int(gt_bbox[3] - 1)),
(255, 0, 0),
1)
if len(frame.shape) == 3:
frame = cv2.cvtColor(frame, cv2.COLOR_RGB2BGR)
frame = cv2.putText(frame, str(idx), (5, 20),
cv2.FONT_HERSHEY_COMPLEX_SMALL, 1, (0, 255, 0), 1)
cv2.imshow(title, frame)
cv2.waitKey(30)
def main():
# load videos
filenames = sorted(glob.glob("../../test/*.jpg"),
key=lambda x: int(os.path.basename(x).split('.')[0]))
frames = [np.array(Image.open(filename)) for filename in filenames]
height, width = frames[0].shape[:2]
# starting tracking
tracker = ECOTracker(True)
vis = True
videoWriter = cv2.VideoWriter('0011.mp4', cv2.VideoWriter_fourcc(*'mp4v'),
20, (224, 224))
for idx, frame in enumerate(frames):
if idx == 0:
bbox = [
245, 512, 587, 587
] #[dets[0].left()+1,dets[0].top()+1,dets[0].right()-dets[0].left(),dets[0].bottom()-dets[0].top()]
tracker.init(frame, bbox)
bbox = (bbox[0] - 1, bbox[1] - 1, bbox[0] + bbox[2] - 1,
bbox[1] + bbox[3] - 1)
elif idx < len(frames) - 1:
bbox = tracker.update(frame, True, vis)
else: # last frame
bbox = tracker.update(frame, False, vis)
# bbox xmin ymin xmax ymax
frame = cv2.cvtColor(frame, cv2.COLOR_RGB2BGR)
if len(bbox) == 4:
print("write " + str(idx))
actualbbox = [
max(0, int(bbox[1])),
min(int(bbox[3]), frame.shape[0]),
max(0, int(bbox[0])),
min(int(bbox[2]), frame.shape[1])
]
saveframe = frame[actualbbox[0]:actualbbox[1],
actualbbox[2]:actualbbox[3]]
longerside = max(actualbbox[1] - actualbbox[0],
actualbbox[3] - actualbbox[2])
dstsize = (224 * (actualbbox[1] - actualbbox[0]) / longerside,
224 * (actualbbox[3] - actualbbox[2]) / longerside)
dstsize = (int(dstsize[1]), int(dstsize[0]))
resizedimg = cv2.resize(saveframe,
dstsize,
interpolation=cv2.INTER_AREA)
canvas = 255 * np.ones((224, 224, 3), np.uint8)
try:
canvas[max(0, -int(int(bbox[1]) * 224 / longerside)):(
max(0, -int(int(bbox[1]) * 224 / longerside)) +
dstsize[1]),
max(0, -int(int(bbox[0]) * 224 / longerside)):(
max(0, -int(int(bbox[0]) * 224 / longerside)) +
dstsize[0])] = resizedimg
videoWriter.write(canvas)
except:
continue
videoWriter.release()
def main():
frames = []
# load videos
# filenames = sorted(glob.glob(os.path.join(video_dir, "img/*.jpg")),
# key=lambda x: int(os.path.basename(x).split('.')[0]))
# # frames = [cv2.cvtColor(cv2.imread(filename), cv2.COLOR_BGR2RGB) for filename in filenames]
# frames = [np.array(Image.open(filename)) for filename in filenames]
cap = cv2.VideoCapture('/media/aigul/Tom/Aigul/dim_small_target_tracking/Sword/Corsair_LWIR_20.02.19_15.02.11.avi')
i = 0
while cap.isOpened():
ret, frame = cap.read()
frames.append(frame)
i += 1
if i == 654:
break
height, width = frames[0].shape[:2]
if len(frames[0].shape) == 3:
is_color = True
else:
is_color = False
frames = [frame[:, :, np.newaxis] for frame in frames]
# gt_bboxes = pd.read_csv(os.path.join(video_dir, "groundtruth_rect.txt"), sep='\t|,| ',
# header=None, names=['xmin', 'ymin', 'width', 'height'],
# engine='python')
title = 'demo'
# fourcc = cv2.VideoWriter_fourcc(*'XVID')
# img_writer = cv2.VideoWriter(os.path.join('./videos', title+'.avi'),
# fourcc, 25, (width, height))
# starting tracking
tracker = ECOTracker(is_color)
vis = True
for idx, frame in enumerate(frames):
if idx == 0:
# bbox = gt_bboxes.iloc[0].values
bbox = np.array([329, 357, 22, 49])
tracker.init(frame, bbox)
bbox = (bbox[0]-1, bbox[1]-1,
bbox[0]+bbox[2]-1, bbox[1]+bbox[3]-1)
elif idx < len(frames) - 1:
bbox = tracker.update(frame, True, vis)
else: # last frame
bbox = tracker.update(frame, False, vis)
# bbox xmin ymin xmax ymax
frame = frame.squeeze()
if len(frame.shape) == 3:
frame = cv2.cvtColor(frame, cv2.COLOR_RGB2BGR)
else:
frame = cv2.cvtColor(frame, cv2.COLOR_GRAY2BGR)
frame = cv2.rectangle(frame,
(int(bbox[0]), int(bbox[1])),
(int(bbox[2]), int(bbox[3])),
(0, 255, 255),
1)
# gt_bbox = gt_bboxes.iloc[idx].values
# gt_bbox = (gt_bbox[0], gt_bbox[1],
# gt_bbox[0]+gt_bbox[2], gt_bbox[1]+gt_bbox[3])
# frame = frame.squeeze()
# frame = cv2.rectangle(frame,
# (int(gt_bbox[0]-1), int(gt_bbox[1]-1)), # 0-index
# (int(gt_bbox[2]-1), int(gt_bbox[3]-1)),
# (0, 255, 0),
# 1)
# if vis and idx > 0:
# score = tracker.score
# size = tuple(tracker.crop_size.astype(np.int32))
# score = cv2.resize(score, size)
# score -= score.min()
# score /= score.max()
# score = (score * 255).astype(np.uint8)
# # score = 255 - score
# score = cv2.applyColorMap(score, cv2.COLORMAP_JET)
# pos = tracker._pos
# pos = (int(pos[0]), int(pos[1]))
# xmin = pos[1] - size[1]//2
# xmax = pos[1] + size[1]//2 + size[1] % 2
# ymin = pos[0] - size[0] // 2
# ymax = pos[0] + size[0] // 2 + size[0] % 2
# left = abs(xmin) if xmin < 0 else 0
# xmin = 0 if xmin < 0 else xmin
# right = width - xmax
# xmax = width if right < 0 else xmax
# right = size[1] + right if right < 0 else size[1]
# top = abs(ymin) if ymin < 0 else 0
# ymin = 0 if ymin < 0 else ymin
# down = height - ymax
# ymax = height if down < 0 else ymax
# down = size[0] + down if down < 0 else size[0]
# score = score[top:down, left:right]
# crop_img = frame[ymin:ymax, xmin:xmax]
# # if crop_img.shape != score.shape:
# # print(left, right, top, down)
# # print(xmin, ymin, xmax, ymax)
# score_map = cv2.addWeighted(crop_img, 0.6, score, 0.4, 0)
# frame[ymin:ymax, xmin:xmax] = score_map
#
frame = cv2.putText(frame, str(idx), (5, 20), cv2.FONT_HERSHEY_COMPLEX_SMALL, 1, (0, 255, 0), 1)
# # img_writer.write(frame)
cv2.imshow(title, frame)
cv2.waitKey(1)
def main(video_dir):
# load videos
filenames = sorted(glob.glob(os.path.join(video_dir, "*.jpg")),
key=lambda x: int(os.path.basename(x).split('.')[0]))
frames = [np.array(Image.open(filename)) for filename in filenames]
height, width = frames[0].shape[:2]
# starting tracking
tracker = ECOTracker(True)
vis = True
videoname = video_dir.split("/")[2]
print(videoname)
videoWriter = cv2.VideoWriter(videoname + '.mp4',
cv2.VideoWriter_fourcc(*'mp4v'), 20,
(224, 224))
for idx, frame in enumerate(frames):
if idx == 0:
detector = dlib.get_frontal_face_detector()
filename = os.path.join(video_dir, "0001.jpg")
img = dlib.load_rgb_image(filename)
dets = detector(img, 0)
print(len(dets))
bbox = [
dets[0].left() + 1, dets[0].top() + 1,
dets[0].right() - dets[0].left(),
dets[0].bottom() - dets[0].top()
]
tracker.init(frame, bbox)
bbox = (bbox[0] - 1, bbox[1] - 1, bbox[0] + bbox[2] - 1,
bbox[1] + bbox[3] - 1)
elif idx < len(frames) - 1:
bbox = tracker.update(frame, True, vis)
else: # last frame
bbox = tracker.update(frame, False, vis)
# bbox xmin ymin xmax ymax
frame = cv2.cvtColor(frame, cv2.COLOR_RGB2BGR)
if len(bbox) == 4:
print("write " + str(idx))
actualbbox = [
max(0, int(bbox[1])),
min(int(bbox[3]), frame.shape[0]),
max(0, int(bbox[0])),
min(int(bbox[2]), frame.shape[1])
]
saveframe = frame[actualbbox[0]:actualbbox[1],
actualbbox[2]:actualbbox[3]]
longerside = max(actualbbox[1] - actualbbox[0],
actualbbox[3] - actualbbox[2])
dstsize = (224 * (actualbbox[1] - actualbbox[0]) / longerside,
224 * (actualbbox[3] - actualbbox[2]) / longerside)
dstsize = (int(dstsize[1]), int(dstsize[0]))
resizedimg = cv2.resize(saveframe,
dstsize,
interpolation=cv2.INTER_AREA)
canvas = 255 * np.ones((224, 224, 3), np.uint8)
canvas[max(0, -int(int(bbox[1]) * 224 / longerside)):(
max(0, -int(int(bbox[1]) * 224 / longerside)) + dstsize[1]),
max(0, -int(int(bbox[0]) * 224 / longerside)):(
max(0, -int(int(bbox[0]) * 224 / longerside)) +
dstsize[0])] = resizedimg
videoWriter.write(canvas)
videoWriter.release()
if w > 0:
ix, iy = int(x - w / 2), int(y - h / 2)
initializing = True
if __name__ == '__main__':
cap = cv2.VideoCapture(0)
# 获取摄像头的宽高，如果修改的话摄像头参数会被永久修改
width = int(cap.get(3)) # CV_CAP_PROP_FRAME_WIDTH
height = int(cap.get(4)) # CV_CAP_PROP_FRAME_HEIGHT
cv2.namedWindow('tracking')
cv2.setMouseCallback('tracking', draw_bbox)
# init tracker
tracker = ECOTracker()
# visualise score(will run slower)
vis = False
while cap.isOpened():
ret, frame = cap.read()
if not ret:
break
if selecting:
cv2.rectangle(frame, (ix, iy), (lx, ly), (0, 255, 255), 1)
elif initializing:
cv2.rectangle(frame, (ix, iy), (ix + w, iy + h), (0, 255, 255), 2)
bbox = [ix, iy, w, h]
tracker.init(frame, bbox)
def main(video_dir):
# load videos
filenames = sorted(glob.glob(os.path.join(video_dir, "img/*.jpg")),
key=lambda x: int(os.path.basename(x).split('.')[0]))
# frames = [cv2.cvtColor(cv2.imread(filename), cv2.COLOR_BGR2RGB) for filename in filenames]
frames = [np.array(Image.open(filename)) for filename in filenames]
height, width = frames[0].shape[:2]
if len(frames[0].shape) == 3:
is_color = True
# 彩色图每张（frame）为[h,w,3]的形式
else:
is_color = False
frames = [frame[:, :, np.newaxis] for frame in frames]
# 灰度图每张转化为[h,w,1]的形式
gt_bboxes = pd.read_csv(os.path.join(video_dir, "groundtruth_rect.txt"),
sep='\t|,| ',
header=None,
names=['xmin', 'ymin', 'width', 'height'],
engine='python')
# 读取目标位置信息（全部）
title = video_dir.split('/')[-1]
# starting tracking
tracker = ECOTracker(is_color)
# 初始化追踪器（类）
for idx, frame in enumerate(frames):
if idx == 0:
# 第一帧
bbox = gt_bboxes.iloc[0].values
# 目标初始位置（第一帧位置）
tracker.init(frame, bbox)
bbox = (bbox[0] - 1, bbox[1] - 1, bbox[0] + bbox[2] - 1,
bbox[1] + bbox[3] - 1)
# bbox改为左上角和右下角的位置
elif idx < len(frames) - 1:
bbox = tracker.update(frame, True)
# 追踪每一帧的目标位置
else: # last frame
bbox = tracker.update(frame, False)
# 追踪最后一帧的目标位置
# bbox xmin ymin xmax ymax
frame = cv2.rectangle(frame, (int(bbox[0]), int(bbox[1])),
(int(bbox[2]), int(bbox[3])), (0, 255, 0), 2)
# 预测位置画框
gt_bbox = gt_bboxes.iloc[idx].values
gt_bbox = (gt_bbox[0], gt_bbox[1], gt_bbox[0] + gt_bbox[2],
gt_bbox[1] + gt_bbox[3])
frame = frame.squeeze()
frame = cv2.rectangle(
frame,
(int(gt_bbox[0] - 1), int(gt_bbox[1] - 1)), # 0-index
(int(gt_bbox[2] - 1), int(gt_bbox[3] - 1)),
(255, 0, 0),
1)
# 真实位置画框
if len(frame.shape) == 3:
frame = cv2.cvtColor(frame, cv2.COLOR_RGB2BGR)
# 由于cv2的imshow图像格式为BGR，而Image读取的图像格式为RGB，转换
frame = cv2.putText(frame, str(idx), (5, 20),
cv2.FONT_HERSHEY_COMPLEX_SMALL, 1, (0, 255, 0), 1)
# 在图像上显示帧数
cv2.imshow(title, frame)
cv2.waitKey(30)
def main(video_dir):
# load videos
filenames = sorted(glob.glob(os.path.join(video_dir, "*.jpg")),
key=lambda x: int(os.path.basename(x).split('.')[0]))
frames = [np.array(Image.open(filename)) for filename in filenames]
height, width = frames[0].shape[:2]
if len(frames[0].shape) == 3:
is_color = True
else:
is_color = False
frames = [frame[:, :, np.newaxis] for frame in frames]
# starting tracking
tracker = ECOTracker(is_color)
vis = True
for idx, frame in enumerate(frames):
print(idx)
if idx == 0:
cnn_face_detector = dlib.cnn_face_detection_model_v1(
"bin/mmod_human_face_detector.dat")
filename = os.path.join(video_dir, "0001.jpg")
img = dlib.load_rgb_image(filename)
dets = cnn_face_detector(img, 1)
bbox = [
dets[0].rect.left() + 1, dets[0].rect.top() + 1,
dets[0].rect.right() - dets[0].rect.left(),
dets[0].rect.bottom() - dets[0].rect.top()
]
tracker.init(frame, bbox)
bbox = (bbox[0] - 1, bbox[1] - 1, bbox[0] + bbox[2] - 1,
bbox[1] + bbox[3] - 1)
elif idx < len(frames) - 1:
bbox = tracker.update(frame, True, vis)
else: # last frame
bbox = tracker.update(frame, False, vis)
# bbox xmin ymin xmax ymax
frame = frame.squeeze()
if len(frame.shape) == 3:
frame = cv2.cvtColor(frame, cv2.COLOR_RGB2BGR)
else:
frame = cv2.cvtColor(frame, cv2.COLOR_GRAY2BGR)
frame = cv2.rectangle(frame, (int(bbox[0]), int(bbox[1])),
(int(bbox[2]), int(bbox[3])), (0, 255, 255), 1)
frame = frame.squeeze()
if vis and idx > 0:
score = tracker.score
size = tuple(tracker.crop_size.astype(np.int32))
score = cv2.resize(score, size)
score -= score.min()
score /= score.max()
score = (score * 255).astype(np.uint8)
# score = 255 - score
score = cv2.applyColorMap(score, cv2.COLORMAP_JET)
pos = tracker._pos
pos = (int(pos[0]), int(pos[1]))
xmin = pos[1] - size[1] // 2
xmax = pos[1] + size[1] // 2 + size[1] % 2
ymin = pos[0] - size[0] // 2
ymax = pos[0] + size[0] // 2 + size[0] % 2
left = abs(xmin) if xmin < 0 else 0
xmin = 0 if xmin < 0 else xmin
right = width - xmax
xmax = width if right < 0 else xmax
right = size[1] + right if right < 0 else size[1]
top = abs(ymin) if ymin < 0 else 0
ymin = 0 if ymin < 0 else ymin
down = height - ymax
ymax = height if down < 0 else ymax
down = size[0] + down if down < 0 else size[0]
score = score[top:down, left:right]
crop_img = frame[ymin:ymax, xmin:xmax]
score_map = cv2.addWeighted(crop_img, 0.6, score, 0.4, 0)
frame[ymin:ymax, xmin:xmax] = score_map
saveFileName = "new%04d" % idx
cv2.imwrite(saveFileName + ".jpg", frame)
def main(video_dir):
# load videos
filenames = sorted(glob.glob(os.path.join(video_dir, "img/*.jpg")),
key=lambda x: int(os.path.basename(x).split('.')[0]))
# frames = [cv2.cvtColor(cv2.imread(filename), cv2.COLOR_BGR2RGB) for filename in filenames]
frames = [np.array(Image.open(filename)) for filename in filenames]
height, width = frames[0].shape[:2]
if len(frames[0].shape) == 3:
is_color = True
else:
is_color = False
frames = [frame[:, :, np.newaxis] for frame in frames]
gt_bboxes = pd.read_csv(os.path.join(video_dir, "groundtruth_rect.txt"),
sep='\t|,| ',
header=None,
names=['xmin', 'ymin', 'width', 'height'],
engine='python')
title = video_dir.split('/')[-1]
# fourcc = cv2.VideoWriter_fourcc(*'XVID')
# img_writer = cv2.VideoWriter(os.path.join('./videos', title+'.avi'),
# fourcc, 25, (width, height))
# starting tracking
tracker = ECOTracker(is_color)
vis = True
for idx, frame in enumerate(frames):
if idx == 0:
bbox = gt_bboxes.iloc[0].values
tracker.init(frame, bbox)
bbox = (bbox[0] - 1, bbox[1] - 1, bbox[0] + bbox[2] - 1,
bbox[1] + bbox[3] - 1)
elif idx < len(frames) - 1:
bbox = tracker.update(frame, True, vis)
else: # last frame
bbox = tracker.update(frame, False, vis)
# bbox xmin ymin xmax ymax
frame = frame.squeeze()
if len(frame.shape) == 3:
frame = cv2.cvtColor(frame, cv2.COLOR_RGB2BGR)
else:
frame = cv2.cvtColor(frame, cv2.COLOR_GRAY2BGR)
frame = cv2.rectangle(frame, (int(bbox[0]), int(bbox[1])),
(int(bbox[2]), int(bbox[3])), (0, 255, 255), 1)
gt_bbox = gt_bboxes.iloc[idx].values
gt_bbox = (gt_bbox[0], gt_bbox[1], gt_bbox[0] + gt_bbox[2],
gt_bbox[1] + gt_bbox[3])
frame = frame.squeeze()
frame = cv2.rectangle(
frame,
(int(gt_bbox[0] - 1), int(gt_bbox[1] - 1)), # 0-index
(int(gt_bbox[2] - 1), int(gt_bbox[3] - 1)),
(0, 255, 0),
1)
if vis and idx > 0:
score = tracker.score
size = tuple(tracker.crop_size.astype(np.int32))
score = cv2.resize(score, size)
score -= score.min()
score /= score.max()
score = (score * 255).astype(np.uint8)
# score = 255 - score
score = cv2.applyColorMap(score, cv2.COLORMAP_JET)
pos = tracker._pos
pos = (int(pos[0]), int(pos[1]))
xmin = pos[1] - size[1] // 2
xmax = pos[1] + size[1] // 2 + size[1] % 2
ymin = pos[0] - size[0] // 2
ymax = pos[0] + size[0] // 2 + size[0] % 2
left = abs(xmin) if xmin < 0 else 0
xmin = 0 if xmin < 0 else xmin
right = width - xmax
xmax = width if right < 0 else xmax
right = size[1] + right if right < 0 else size[1]
top = abs(ymin) if ymin < 0 else 0
ymin = 0 if ymin < 0 else ymin
down = height - ymax
ymax = height if down < 0 else ymax
down = size[0] + down if down < 0 else size[0]
score = score[top:down, left:right]
crop_img = frame[ymin:ymax, xmin:xmax]
# if crop_img.shape != score.shape:
# print(left, right, top, down)
# print(xmin, ymin, xmax, ymax)
score_map = cv2.addWeighted(crop_img, 0.6, score, 0.4, 0)
frame[ymin:ymax, xmin:xmax] = score_map
frame = cv2.putText(frame, str(idx), (5, 20),
cv2.FONT_HERSHEY_COMPLEX_SMALL, 1, (0, 255, 0), 1)
# img_writer.write(frame)
cv2.imshow(title, frame)
cv2.waitKey(1)
def main():
logger = logger_init()
# load videos
camUse = False
bbox = []
if camUse:
video = cv2.VideoCapture(0)
else:
video = cv2.VideoCapture("/home/wnj/projects/videos/people.mp4")
ok, frame = video.read()
height, width = frame.shape[:2]
center = width
if len(frame.shape) == 3:
is_color = True
else:
is_color = False
frame = frame[:, :, np.newaxis]
# starting tracking
tracker = ECOTracker(is_color)
#
while (True):
if bbox:
# initialize the tracker with frame[0] and bbox
tracker.init(frame, bbox)
bbox = (bbox[0] - 1, bbox[1] - 1,
bbox[0] + bbox[2] - 1, bbox[1] + bbox[3] - 1)
cv2.rectangle(frame, (bbox[0], bbox[1]), (bbox[2], bbox[3]), (0, 255, 0), thickness=2)
break
else:
pick = detector.detector(frame, None, 0, logger)
# check the first frame, if no object, do the next frame
for (x, y, w, h) in pick:
if abs(x + w / 2 - width / 2) + abs(y + h / 2 - height / 2) < center:
center = abs(x + w / 2 - width / 2) + abs(y + h / 2 - height / 2)
bbox = (x, y, w, h)
title = "ECO"
vis = True
idx = 0
while True:
# 读新的帧
ok, frame = video.read()
if not ok:
break
idx += 1
# start timer
timer = cv2.getTickCount()
# 用后续帧更新tracker
bbox = tracker.track(frame, True, vis)
# bbox xmin ymin xmax ymax
frame = frame.squeeze()
if len(frame.shape) == 3:
frame = cv2.cvtColor(frame, cv2.COLOR_RGB2BGR)
else:
frame = cv2.cvtColor(frame, cv2.COLOR_GRAY2BGR)
frame = cv2.rectangle(frame,
(int(bbox[0]), int(bbox[1])),
(int(bbox[2]), int(bbox[3])),
(0, 255, 255),
1)
if vis:
score = tracker.score
size = tuple(tracker.crop_size.astype(np.int32))
score = cv2.resize(score, size)
score -= score.min()
score /= score.max()
score = (score * 255).astype(np.uint8)
# score = 255 - score
score = cv2.applyColorMap(score, cv2.COLORMAP_JET)
pos = tracker._pos
pos = (int(pos[0]), int(pos[1]))
xmin = pos[1] - size[1] // 2
xmax = pos[1] + size[1] // 2 + size[1] % 2
ymin = pos[0] - size[0] // 2
ymax = pos[0] + size[0] // 2 + size[0] % 2
left = abs(xmin) if xmin < 0 else 0
xmin = 0 if xmin < 0 else xmin
right = width - xmax
xmax = width if right < 0 else xmax
right = size[1] + right if right < 0 else size[1]
top = abs(ymin) if ymin < 0 else 0
ymin = 0 if ymin < 0 else ymin
down = height - ymax
ymax = height if down < 0 else ymax
down = size[0] + down if down < 0 else size[0]
score = score[top:down, left:right]
crop_img = frame[ymin:ymax, xmin:xmax]
# if crop_img.shape != score.shape:
# print(xmin, ymin, xmax, ymax)
score_map = cv2.addWeighted(crop_img, 0.6, score, 0.4, 0)
frame[ymin:ymax, xmin:xmax] = score_map
# counting FPS
fps = cv2.getTickFrequency() / (cv2.getTickCount() - timer)
# print idx
frame = cv2.putText(frame, str(idx), (5, 20), cv2.FONT_HERSHEY_COMPLEX_SMALL, 1, (0, 255, 0), 1)
# print("FPS:", str(int(fps)))
cv2.putText(frame, "FPS : " + str(int(fps)), (100, 50), cv2.FONT_HERSHEY_SIMPLEX, 0.75, (50, 170, 50), 2)
# img_writer.write(frame)
cv2.imshow(title, frame)
k = cv2.waitKey(1)&0xff
if k == 27:
break
if k == ord(' '):
cv2.waitKey(0)
tracker.quit()
std=[0.229, 0.224, 0.225],
inplace=False)
])
t = transforms.Resize(size=(256, 256))
predictor = dlib.shape_predictor(
"../shape_predictor_68_face_landmarks.dat")
detector = dlib.get_frontal_face_detector()
fvs = FileVideoStream(file_path).start()
stabilizer = VidStab()
prev_anchor = None
prev_ROTATION = None
MOTION_DIFF = 0
frame_count = 0
refreshed = False
# ROTATION_ANGLE = 75
tracker = ECOTracker(True)
prevTrackPts = []
nextTrackPts = []
last_object = []
kalman_points = []
predict_points = []
for i in range(68):
predict_points.append((0.0, 0.0))
last_object.append((0.0, 0.0))
kalman_points.append((0.0, 0.0))
prevTrackPts.append((0.0, 0.0))
nextTrackPts.append((0.0, 0.0))
scaling = 0.5
flag = -1
def trackeco(video_dir, firstbbox, resultpath):
# # 读入连续帧
# # filenames为图片名
filenames = sorted(glob.glob(os.path.join(video_dir, "*.jpg")),
key=lambda x: int(os.path.basename(x).split('.')[0].split('_')[-1]))
# frames包含连续帧数据
# frames = [cv2.cvtColor(cv2.imread(filename), cv2.COLOR_BGR2RGB) for filename in filenames]
frames = [np.array(Image.open(filename)) for filename in filenames]
height, width = frames[0].shape[:2]
if len(frames[0].shape) == 3:
is_color = True
else:
is_color = False
frames = [frame[:, :, np.newaxis] for frame in frames]
#读取bbox
bbox = firstbbox
#输出文件，此处需要修改
title = video_dir.split('/')[-1]
txtname = os.path.join(resultpath, 'ecoresult.txt')
print(txtname)
# fourcc = cv2.VideoWriter_fourcc(*'XVID')
# img_writer = cv2.VideoWriter(os.path.join('./videos', title+'.avi'),
# fourcc, 25, (width, height))
# 开始跟踪
tracker = ECOTracker(is_color)
vis = True
for idx, frame in enumerate(frames):
if idx == 0:
#bbox = gt_bboxes.iloc[0].values
tracker.init(frame, bbox)
bbox = (bbox[0]-1, bbox[1]-1,
bbox[0]+bbox[2]-1, bbox[1]+bbox[3]-1)
with open(txtname,'w') as f:
content = str(np.round(bbox).astype(np.int16)).strip('[').strip(']')
f.write(content)
f.write('\n')
f.close()
start_time = time.time()
elif idx < len(frames) - 1:
bbox = tracker.update(frame, True, vis)
with open(txtname,'a') as f:
content = str(np.round(bbox).astype(np.int16)).strip('[').strip(']')
f.write(content)
f.write('\n')
f.close()
else: # last frame
bbox = tracker.update(frame, False, vis)
with open(txtname,'a') as f:
content = str(np.round(bbox).astype(np.int16)).strip('[').strip(']')
f.write(content)
f.write('\n')
f.close()
end_time = time.time()
# bbox xmin ymin xmax ymax
frame = frame.squeeze()
if len(frame.shape) == 3:
frame = cv2.cvtColor(frame, cv2.COLOR_RGB2BGR)
else:
frame = cv2.cvtColor(frame, cv2.COLOR_GRAY2BGR)
frame = cv2.rectangle(frame,
(int(bbox[0]), int(bbox[1])),
(int(bbox[2]), int(bbox[3])),
(0, 255, 255),
1)
#gt_bbox = gt_bboxes.iloc[idx].values
#gt_bbox = (gt_bbox[0], gt_bbox[1],
# gt_bbox[0]+gt_bbox[2], gt_bbox[1]+gt_bbox[3])
#frame = frame.squeeze()
#frame = cv2.rectangle(frame,
# (int(gt_bbox[0]-1), int(gt_bbox[1]-1)), # 0-index
# (int(gt_bbox[2]-1), int(gt_bbox[3]-1)),
# (0, 255, 0),
# 1)
if vis and idx > 0:
score = tracker.score
size = tuple(tracker.crop_size.astype(np.int32))
score = cv2.resize(score, size)
score -= score.min()
score /= score.max()
score = (score * 255).astype(np.uint8)
# score = 255 - score
score = cv2.applyColorMap(score, cv2.COLORMAP_JET)
pos = tracker._pos
pos = (int(pos[0]), int(pos[1]))
xmin = pos[1] - size[1]//2
xmax = pos[1] + size[1]//2 + size[1] % 2
ymin = pos[0] - size[0] // 2
ymax = pos[0] + size[0] // 2 + size[0] % 2
left = abs(xmin) if xmin < 0 else 0
xmin = 0 if xmin < 0 else xmin
right = width - xmax
xmax = width if right < 0 else xmax
right = size[1] + right if right < 0 else size[1]
top = abs(ymin) if ymin < 0 else 0
ymin = 0 if ymin < 0 else ymin
down = height - ymax
ymax = height if down < 0 else ymax
down = size[0] + down if down < 0 else size[0]
score = score[top:down, left:right]
crop_img = frame[ymin:ymax, xmin:xmax]
# if crop_img.shape != score.shape:
# print(left, right, top, down)
# print(xmin, ymin, xmax, ymax)
score_map = cv2.addWeighted(crop_img, 0.6, score, 0.4, 0)
frame[ymin:ymax, xmin:xmax] = score_map
frame = cv2.putText(frame, str(idx), (5, 20), cv2.FONT_HERSHEY_COMPLEX_SMALL, 1, (0, 255, 0), 1)
# img_writer.write(frame)
# cv2.imshow(title, frame)
#jpgpath = os.path.join(resultpath, 'pic')
#if not os.path.exists(jpgpath):
# os.makedirs(jpgpath)
#cv2.imwrite(jpgpath + str(idx) + '.jpg', frame)
#cv2.imwrite(os.path.join(jpgpath, str(idx)+'.jpg'), frame)
#cv2.waitKey(1)
return (idx, end_time - start_time)
def main(video_dir):
# 读入连续帧
# filenames为图片名
filenames = sorted(glob.glob(os.path.join(video_dir, "img/*.jpg")),
key=lambda x: int(os.path.basename(x).split('.')[0]))
# frames包含连续帧数据
frames = [np.array(Image.open(filename)) for filename in filenames]
height, width = frames[0].shape[:2]
if len(frames[0].shape) == 3:
is_color = True
else:
is_color = False
frames = [frame[:, :, np.newaxis] for frame in frames]
gt_bboxes = pd.read_csv(os.path.join(video_dir, "groundtruth_rect.txt"), sep='\t|,| ',
header=None, names=['xmin', 'ymin', 'width', 'height'],
engine='python')
print(video_dir)
title = video_dir.split('/')[-1]
txtname = './results/' + video_dir[10:] + '.txt'
print(video_dir[10:-1])
print(txtname)
# 完成读取工作，开始跟踪
tracker = ECOTracker(is_color)
vis = True
for idx, frame in enumerate(frames):
if idx == 0:
bbox = gt_bboxes.iloc[0].values
tracker.init(frame, bbox)
bbox = (bbox[0]-1, bbox[1]-1,
bbox[0]+bbox[2]-1, bbox[1]+bbox[3]-1)
with open(txtname,'w') as f:
content = str(np.round(bbox).astype(np.int16)).strip('[').strip(']')
f.write(content)
f.write('\n')
f.close()
start_time = time.time()
elif idx < len(frames) - 1:
bbox = tracker.update(frame, True, vis)
with open(txtname,'a') as f:
content = str(np.round(bbox).astype(np.int16)).strip('[').strip(']')
f.write(content)
f.write('\n')
f.close()
else: # 最后一帧
bbox = tracker.update(frame, False, vis)
with open(txtname,'a') as f:
content = str(np.round(bbox).astype(np.int16)).strip('[').strip(']')
f.write(content)
f.write('\n')
f.close()
end_time = time.time()
# bbox xmin ymin xmax ymax
frame = frame.squeeze()
if len(frame.shape) == 3:
frame = cv2.cvtColor(frame, cv2.COLOR_RGB2BGR)
else:
frame = cv2.cvtColor(frame, cv2.COLOR_GRAY2BGR)
frame = cv2.rectangle(frame,
(int(bbox[0]), int(bbox[1])),
(int(bbox[2]), int(bbox[3])),
(0, 255, 255),
1)
gt_bbox = gt_bboxes.iloc[idx].values
gt_bbox = (gt_bbox[0], gt_bbox[1],
gt_bbox[0]+gt_bbox[2], gt_bbox[1]+gt_bbox[3])
frame = frame.squeeze()
frame = cv2.rectangle(frame,
(int(gt_bbox[0]-1), int(gt_bbox[1]-1)), # 0-index
(int(gt_bbox[2]-1), int(gt_bbox[3]-1)),
(0, 255, 0),
1)
if vis and idx > 0:
score = tracker.score
size = tuple(tracker.crop_size.astype(np.int32))
score = cv2.resize(score, size)
score -= score.min()
score /= score.max()
score = (score * 255).astype(np.uint8)
score = cv2.applyColorMap(score, cv2.COLORMAP_JET)
pos = tracker._pos
pos = (int(pos[0]), int(pos[1]))
xmin = pos[1] - size[1]//2
xmax = pos[1] + size[1]//2 + size[1] % 2
ymin = pos[0] - size[0] // 2
ymax = pos[0] + size[0] // 2 + size[0] % 2
left = abs(xmin) if xmin < 0 else 0
xmin = 0 if xmin < 0 else xmin
right = width - xmax
xmax = width if right < 0 else xmax
right = size[1] + right if right < 0 else size[1]
top = abs(ymin) if ymin < 0 else 0
ymin = 0 if ymin < 0 else ymin
down = height - ymax
ymax = height if down < 0 else ymax
down = size[0] + down if down < 0 else size[0]
score = score[top:down, left:right]
crop_img = frame[ymin:ymax, xmin:xmax]
score_map = cv2.addWeighted(crop_img, 0.6, score, 0.4, 0)
frame[ymin:ymax, xmin:xmax] = score_map
frame = cv2.putText(frame, str(idx), (5, 20), cv2.FONT_HERSHEY_COMPLEX_SMALL, 1, (0, 255, 0), 1)
cv2.imshow(title, frame)
cv2.waitKey(1)
FPS = (idx + 1) / (end_time - start_time)
class guiApp(QtWidgets.QMainWindow, mainwindow.Ui_MainWindow):
def __init__(self):
super().__init__()
self.setupUi(self)
self.init_data()
self.init_ui()
def init_ui(self):
self.setWindowTitle('sword v.1')
self.lineEdit_video_file_path.editingFinished.connect(
self.lineedit_video_file_path_editing_finished)
self.lineEdit_video_file_path.setText(
'Corsair_LWIR_20.02.19_15.02.11.avi')
self.lineedit_video_file_path_editing_finished()
self.lineEdit_descriptor.editingFinished.connect(
self.lineedit_descriptor_editing_finished)
self.lineEdit_descriptor.setText('fern_data_28_01_2019.mat')
self.lineedit_descriptor_editing_finished()
self.lineEdit_descriptor.setFocus()
self.pushButton_select_path_video.clicked.connect(
self.pushButton_select_path_video_clicked)
self.checkBox_camera.toggled.connect(self.checkBox_camera_toggled)
self.checkBox_fern.toggled.connect(self.checkBox_fern_toggled)
self.checkBox_orb.toggled.connect(self.checkBox_orb_toggled)
self.checkBox_clust.toggled.connect(self.checkBox_clust_toggled)
self.checkBox_harris_corn.toggled.connect(
self.checkBox_harris_corn_toggled)
self.checkBox_track_orb.toggled.connect(
self.checkBox_track_orb_toggled)
self.checkBox_Hungarian.toggled.connect(
self.checkBox_Hungarian_toggled)
self.checkBox_hung_orb.toggled.connect(self.checkBox_hung_orb_toggled)
self.checkBox_orb.setChecked(True)
self.checkBox_clust.setChecked(True)
self.checkBox_eco.toggled.connect(self.checkBox_eco_toggled)
self.lineEdit_max_frames_to_skip.editingFinished.connect(
self.lineEdit_max_frames_to_skip_editing_finished)
self.lineEdit_dist_tresh.editingFinished.connect(
self.lineEdit_dist_tresh_editing_finished)
self.lineEdit_nkeypoints.editingFinished.connect(
self.lineEdit_nkeypoints_editing_finished)
self.lineEdit_orb_area.editingFinished.connect(
self.lineEdit_orb_area_editing_finished)
self.lineEdit_proc_time.editingFinished.connect(
self.lineEdit_proc_time_editing_finished)
self.lineEdit_brief_size.editingFinished.connect(
self.lineEdit_brief_size_editing_finished)
self.lineEdit_max_frames_to_skip.setText(str(self.max_frames_to_skip))
self.lineEdit_dist_tresh.setText(str(self.dist_thresh))
self.lineEdit_orb_area.setText(str(self.size_orb_area))
self.lineEdit_nkeypoints.setText(str(self.nkeypoints))
self.lineEdit_proc_time.setText(str(self.proc_time))
self.lineEdit_brief_size.setText(str(self.brief_size))
self.horizontalSlider_video.sliderMoved.connect(
self.horizontalSlider_video_moved)
self.label_cur_frame.setText('00:00')
pass
def init_data(self):
self.src_video_path = None
self.video_len = None
self.cur_frame_id = 0
self.tmr = QTimer()
self.time = QTime()
self.proc_time = 40
self.tmr.setInterval(self.proc_time)
self.tmr.timeout.connect(self.timeout_handler)
self.tmr.start()
self.time.start()
self.fps_period = None
self.last_timestamp = self.time.elapsed()
self.video_capturer = None
self.frame = None
self.prev_frame = None
self.frame_draw = None
self.frame_crop = None
self.proc_qimage = None
self.proc_qpixmap = None
self.amount_of_frames = None
self.feat_extr = Feature_Extractor()
self.bay_cl = BayesClassifier()
self.proceed_frame = Proceed_Frame()
self.tracker = Tracker(4, 15, 30, 31)
self.orb = orb()
self.dist_thresh = 4
self.max_frames_to_skip = 15
self.size_orb_area = 30
self.left_corner = None
self.right_corner = None
self.left_corner_real = None
self.right_corner_real = None
self.kp_select = None
self.nkeypoints = 300
self.brief_size = 31
self.begin = QtCore.QPoint()
self.end = QtCore.QPoint()
self.width = None
self.height = None
self.enable_playing = False
self.fern_det = False
self.orb_det = False
self.harris_det = False
self.enable_clust = False
self.enable_tracking_orb = False
self.enable_tracking_Hungarian = False
self.enable_tracking_hung_orb = False
self.enable_eco = False
self.camera_id = 0
self.camera_capturer = cv2.VideoCapture(self.camera_id)
self.camera_frame = None
self.video_frame = None
self.video_src = 'video'
self.clust_kps = []
self.en_roi = False
self.bbox_eco = ()
self.bbox_init_eco = ()
self.tracker_eco = ECOTracker(True)
pass
def lineedit_video_file_path_editing_finished(self):
if os.path.isfile(self.lineEdit_video_file_path.text()):
self.src_video_path = os.path.basename(
self.lineEdit_video_file_path.text())
self.video_capturer = cv2.VideoCapture(
self.lineEdit_video_file_path.text())
self.video_capturer.set(cv2.CAP_PROP_POS_FRAMES, self.cur_frame_id)
self.amount_of_frames = self.video_capturer.get(
cv2.CAP_PROP_FRAME_COUNT)
self.enable_playing = False
self.refresh_image_tracker()
else:
print('Error: path to video file is invalid!')
pass
def horizontalSlider_video_moved(self):
self.cur_frame_id = int(self.amount_of_frames *
self.horizontalSlider_video.sliderPosition() /
100)
self.video_capturer.set(cv2.CAP_PROP_POS_FRAMES, self.cur_frame_id)
self.refresh_image_tracker()
self.horizontalSlider_time(self.cur_frame_id)
pass
def horizontalSlider_time(self, cur_frame_id):
if cur_frame_id > 1500:
minutes = int(cur_frame_id // 1500)
seconds = int((cur_frame_id % 1500) // 25)
else:
minutes = 0
seconds = int(cur_frame_id // 25)
str_seconds = str(seconds)
str_minutes = str(minutes)
if seconds < 10:
str_seconds = '0' + str(seconds)
if minutes < 10:
str_minutes = '0' + str(minutes)
self.label_cur_frame.setText(str_minutes + ':' + str_seconds)
pass
def lineEdit_max_frames_to_skip_editing_finished(self):
self.max_frames_to_skip = int(self.lineEdit_max_frames_to_skip.text())
self.tracker = Tracker(self.dist_thresh, self.max_frames_to_skip,
self.size_orb_area, self.brief_size)
pass
def lineEdit_dist_tresh_editing_finished(self):
self.dist_thresh = int(self.lineEdit_dist_tresh.text())
self.tracker = Tracker(self.dist_thresh, self.max_frames_to_skip,
self.size_orb_area, self.brief_size)
pass
def lineEdit_brief_size_editing_finished(self):
self.brief_size = int(self.lineEdit_brief_size.text())
self.tracker = Tracker(self.dist_thresh, self.max_frames_to_skip,
self.size_orb_area, self.brief_size)
pass
def lineEdit_nkeypoints_editing_finished(self):
self.nkeypoints = int(self.lineEdit_nkeypoints.text())
pass
def lineEdit_orb_area_editing_finished(self):
self.size_orb_area = int(self.lineEdit_orb_area.text())
self.tracker = Tracker(self.dist_thresh, self.max_frames_to_skip,
self.size_orb_area, self.brief_size)
pass
def lineEdit_proc_time_editing_finished(self):
self.proc_time = int(self.lineEdit_proc_time.text())
self.tracker = Tracker(self.dist_thresh, self.max_frames_to_skip,
self.size_orb_area, self.brief_size)
pass
def pushButton_select_path_video_clicked(self):
fname = QFileDialog.getOpenFileName(self, 'Select video file', '')[0]
if fname is not None:
self.src_video_path = os.path.basename(fname)
self.lineEdit_video_file_path.setText(fname)
self.lineedit_video_file_path_editing_finished()
pass
def lineedit_descriptor_editing_finished(self):
if os.path.isfile(self.lineEdit_descriptor.text()):
self.feature = self.lineEdit_descriptor.text()
mat = scipy.io.loadmat(self.feature)
self.features = mat['features']['x'][0][0]
self.weights = mat['WEIGHTS']
self.feat_extr.config_features(self.features)
self.bay_cl.config_weights(self.weights)
else:
print('Error: path to mat file is invalid!')
def refresh_image_tracker(self):
if self.video_src == 'camera':
self.refresh_camera_frame()
self.frame = self.camera_frame
if self.video_src == 'video':
self.refresh_video_frame()
self.frame = self.video_frame
if self.frame is not None:
a = self.frame.shape[1] / 720
b = self.frame.shape[0] / 400
self.roi_rect = ()
if self.left_corner is not None:
self.en_roi = True
self.begin = QtCore.QPoint()
self.end = QtCore.QPoint()
self.roi_rect = (self.left_corner_real[0],
self.left_corner_real[1],
self.right_corner_real[0] -
self.left_corner_real[0],
self.right_corner_real[1] -
self.left_corner_real[1])
if self.bbox_init_eco != self.roi_rect:
self.bbox_eco = ()
if self.enable_playing:
self.frame_draw = self.frame
if self.fern_det:
self.frame_draw = self.proceed_frame.scan_window(
self.frame_draw, self.features, self.weights)
elif self.harris_det or self.orb_det or self.enable_tracking_orb or self.enable_tracking_Hungarian or self.enable_tracking_hung_orb:
self.tracker.Update(self.frame_draw, self.nkeypoints,
self.harris_det, self.enable_clust,
self.enable_tracking_Hungarian,
self.enable_tracking_orb,
self.enable_tracking_hung_orb,
self.en_roi, self.roi_rect)
self.frame_draw = self.tracker.draw_tracks(
self.frame_draw, self.en_roi, self.roi_rect)
elif self.enable_eco and len(self.roi_rect) != 0:
if len(self.frame_draw.shape) == 3:
is_color = True
else:
is_color = False
self.frame_draw = self.frame_draw[:, :, np.newaxis]
vis = True
if len(self.bbox_eco) == 0:
self.bbox_eco = self.roi_rect
self.bbox_init_eco = self.roi_rect
self.tracker_eco.init(self.frame_draw, self.bbox_eco)
self.bbox_eco = (self.bbox_eco[0] - 1,
self.bbox_eco[1] - 1,
self.bbox_eco[0] + self.bbox_eco[2] -
1, self.bbox_eco[1] +
self.bbox_eco[3] - 1)
elif self.cur_frame_id < int(self.amount_of_frames) - 1:
self.bbox_eco = self.tracker_eco.update(
self.frame_draw, True, vis)
else:
self.bbox_eco = self.tracker_eco.update(
self.frame_draw, False, vis)
frame = self.frame_draw.squeeze()
self.frame_draw = cv2.rectangle(
frame, (int(self.bbox_eco[0]), int(self.bbox_eco[1])),
(int(self.bbox_eco[2]), int(self.bbox_eco[3])),
(0, 0, 255), 1)
self.proc_qpixmap = self.proceed_frame.qpixmap_from_arr(
self.frame_draw)
self.label_video.setPixmap(self.proc_qpixmap)
if not self.enable_playing:
self.frame_draw = self.frame
if self.harris_det or self.enable_tracking_orb or self.enable_tracking_Hungarian or self.enable_tracking_hung_orb:
if self.orb:
(self.frame_draw, self.clust_kps) = self.orb.orb_desc(
self.frame_draw, self.enable_clust,
self.nkeypoints, self.brief_size, self.en_roi,
self.roi_rect)
elif self.harris_det:
self.frame_draw = self.orb.harris_corner_det(
self.frame_draw, self.nkeypoints,
self.size_orb_area)
self.proc_qpixmap = self.proceed_frame.qpixmap_from_arr(
self.frame_draw)
self.label_video.setPixmap(self.proc_qpixmap)
pass
def timeout_handler(self):
if self.enable_playing:
self.cur_frame_id += 1
self.refresh_image_tracker()
if self.frame is not None:
self.label_frame_id.setText(str(self.cur_frame_id))
self.horizontalSlider_video.setSliderPosition(
self.cur_frame_id * 100 // self.amount_of_frames)
self.horizontalSlider_time(self.cur_frame_id)
cur_time = self.time.elapsed()
self.fps_period = cur_time - self.last_timestamp
self.last_timestamp = cur_time
if self.fps_period != 0:
self.label_fps.setText(str(int(1000.0 / self.fps_period)))
pass
def refresh_camera_frame(self):
ret, self.camera_frame = self.camera_capturer.read()
pass
def refresh_video_frame(self):
self.video_capturer.set(cv2.CAP_PROP_POS_FRAMES, self.cur_frame_id)
ret, self.video_frame = self.video_capturer.read()
pass
def checkBox_camera_toggled(self, checked):
if checked:
self.video_src = 'camera'
self.refresh_image_tracker()
else:
self.video_src = 'video'
self.refresh_image_tracker()
pass
def checkBox_fern_toggled(self, checked):
self.fern_det = checked
self.checkBox_orb.setChecked(False)
pass
def checkBox_orb_toggled(self, checked):
self.orb_det = checked
self.checkBox_fern.setChecked(False)
pass
def checkBox_eco_toggled(self, checked):
self.enable_eco = checked
self.checkBox_orb.setChecked(False)
self.checkBox_clust.setChecked(False)
pass
def checkBox_harris_corn_toggled(self, checked):
self.harris_det = checked
self.checkBox_orb.setChecked(False)
self.checkBox_clust.setChecked(False)
pass
def checkBox_clust_toggled(self, checked):
self.enable_clust = checked
pass
def checkBox_track_orb_toggled(self, checked):
self.enable_tracking_orb = checked
self.checkBox_Hungarian.setChecked(False)
self.checkBox_hung_orb.setChecked(False)
self.refresh_image_tracker()
self.tracker.del_tracks()
pass
def checkBox_hung_orb_toggled(self, checked):
self.enable_tracking_hung_orb = checked
self.checkBox_Hungarian.setChecked(False)
self.checkBox_track_orb.setChecked(False)
self.refresh_image_tracker()
self.tracker.del_tracks()
pass
def checkBox_Hungarian_toggled(self, checked):
self.enable_tracking_Hungarian = checked
self.checkBox_track_orb.setChecked(False)
self.checkBox_hung_orb.setChecked(False)
self.refresh_image_tracker()
self.tracker.del_tracks()
pass
def paintEvent(self, event):
qp = QPainter()
pen = QPen(QtCore.Qt.red, 1)
width = self.end.x() - self.begin.x()
height = self.end.y() - self.begin.y()
if self.frame is not None:
qp.begin(self.label_video.pixmap())
qp.setPen(pen)
qp.drawRect(self.begin.x() - 20,
self.begin.y() - 20, width, height)
qp.end()
def mousePressEvent(self, event):
self.begin = event.pos()
self.end = event.pos()
self.update()
pass
def mouseMoveEvent(self, event):
self.end = event.pos()
self.update()
if self.frame is not None:
a = self.frame.shape[1] / 720
b = self.frame.shape[0] / 400
if 20 < self.begin.x() < 740 and 20 < self.end.x() < 740 and 20 < self.begin.y() < 420 and 20 < self.end.y() < \
420 and self.end.x() != self.begin.x() and self.end.y() != self.begin.y() and self.enable_playing == False:
self.left_corner = (self.begin.x() - 20, self.begin.y() - 20)
self.right_corner = (self.end.x() - 20, self.end.y() - 20)
self.left_corner_real = (int(a * (self.begin.x() - 20)),
int(b * (self.begin.y() - 20)))
self.right_corner_real = (int(a * (self.end.x() - 20)),
int(b * (self.end.y() - 20)))
self.en_roi = True
self.roi_rect = (self.left_corner_real[0],
self.left_corner_real[1],
self.right_corner_real[0] -
self.left_corner_real[0],
self.right_corner_real[1] -
self.left_corner_real[1])
self.proc_qpixmap = self.proceed_frame.qpixmap_from_arr_mouse_move(
self.frame, self.left_corner, self.right_corner)
self.label_video.setPixmap(self.proc_qpixmap)
def mouseReleaseEvent(self, event):
self.end = event.pos()
self.update()
if self.frame is not None:
a = self.frame.shape[1] / 720
b = self.frame.shape[0] / 400
self.kp_select = (int(a * (self.end.x() - 20)),
int(b * (self.end.y() - 20)))
if self.kp_select is not None:
if self.enable_tracking_Hungarian or self.enable_tracking_orb or self.enable_tracking_hung_orb:
(self.frame, self.clust_kps) = self.orb.orb_desc(
self.frame, self.enable_clust, self.nkeypoints,
self.brief_size, self.en_roi, self.roi_rect)
if self.en_roi:
self.kp_select = (int(a * (self.end.x() - 20)) -
self.roi_rect[0],
int(b * (self.end.y() - 20)) -
self.roi_rect[1])
if self.kp_select[0] > 0 and self.kp_select[1] > 0:
self.tracker.init_track(self.kp_select, self.clust_kps)
self.checkBox_orb.setChecked(False)
pass
def keyPressEvent(self, event):
if event.key() == QtCore.Qt.Key_Return:
self.enable_playing = not self.enable_playing
if event.key() == QtCore.Qt.Key_F12:
self.left_corner_real = (0, 0)
self.right_corner_real = (self.video_frame.shape[1],
self.video_frame.shape[0])
pass