def _get_gauss_response(self, img, gt):
# get the shape of the image..
height, width = img.shape
# get the mesh grid...
xx, yy = np.meshgrid(np.arange(width), np.arange(height))
# get the center of the object...
# 得到选定的目标区域的中心点坐标
center_x = gt[0] + 0.5 * gt[2]
center_y = gt[1] + 0.5 * gt[3]
# cal the distance...
# 创建一个以选定的目标中点为中心，且符合二维高斯分布的响应矩阵，矩阵大小等于原图像 img 的大小
# 原始的二维高斯函数中，方差有两个: sigmaX 和 sigmaY，其中 sigmaX 为 x 方向的方差，sigmaY 为 y 方向的方差
# 不过这里取相同的值，使得二维高斯模型在平面上的投影就是一个圆形，意思是与目标中心 (x0, y0) 的距离一样的点的权重是一样的，
# 如果取不一样的值，那么投影为一个椭圆形，距离目标中心会得到不一样的权重
exponent = (np.square(xx - center_x) + np.square(yy - center_y)) / (2 * self.args.sigma)
# get the response map...
# 获取到高斯响应矩阵
response = np.exp(-exponent)
# normalize...
# 对响应矩阵进行归一化处理: (x - min) / (max - min)
response = linear_mapping(response)
return response
def track(self, current_frame):
# for idx in range(len(frame_list)):
frame_gray = cv2.cvtColor(current_frame, cv2.COLOR_BGR2GRAY)
frame_gray = frame_gray.astype(np.float32)
# import ipdb;ipdb.set_trace()
Hi = self.Ai / self.Bi
fi = frame_gray[self.pos[1]:self.pos[1] + self.pos[3],
self.pos[0]:self.pos[0] + self.pos[2]]
fi = pre_process(fi)
Gi = Hi * np.fft.fft2(fi)
gi = linear_mapping(np.fft.ifft2(Gi))
# find the max self.pos...
max_pos = np.unravel_index(np.argmax(gi, axis=None), gi.shape)
# update the position...
self.pos[1] += max_pos[0] - gi.shape[0] // 2
self.pos[0] += max_pos[1] - gi.shape[1] // 2
# get the current fi..
fi = frame_gray[self.pos[1]:self.pos[1] + self.pos[3],
self.pos[0]:self.pos[0] + self.pos[2]]
fi = pre_process(fi)
# online update...
self.Ai = self.learning_rate * (self.G * np.conjugate(
np.fft.fft2(fi))) + (1 - self.learning_rate) * self.Ai
self.Bi = self.learning_rate * (np.fft.fft2(fi) * np.conjugate(
np.fft.fft2(fi))) + (1 - self.learning_rate) * self.Bi
return self.pos
def _get_gauss_response(self, img, gt):
# get the shape of the image..
height, width = img.shape
# get the mesh grid...
xx, yy = np.meshgrid(np.arange(width), np.arange(height))
# get the center of the object...
center_x = gt[0] + 0.5 * gt[2]
center_y = gt[1] + 0.5 * gt[3]
# cal the distance...
dist = (np.square(xx - center_x) + np.square(yy - center_y)) / (2 * self.args.sigma)
# get the response map...
response = np.exp(-dist)
# normalize...
response = linear_mapping(response)
return response
def _get_gauss_response(self, img, gt):
# get the shape of the image..
height, width = img.shape
# get the mesh grid...
xx, yy = np.meshgrid(np.arange(width), np.arange(height))
# get the center of the object...
center_x = gt[0] + 0.5 * gt[2]
center_y = gt[1] + 0.5 * gt[3]
# cal the distance...
dist = (np.square(xx - center_x) + np.square(yy - center_y)) / (2 * self.args.sigma)
# get the response map...
response = np.exp(-dist)
# normalize...
response = linear_mapping(response)
return response
def _get_gauss_response(self, img, gt):
# get the shape of the image..
height, width = img.shape
# get the mesh grid...
xx, yy = np.meshgrid(np.arange(width), np.arange(height)) #获得height行x可能的取值，width列y可能的取值
# get the center of the object...
center_x = gt[0] + 0.5 * gt[2] #获取矩形框中心x坐标
center_y = gt[1] + 0.5 * gt[3] #获得矩形框中心y坐标
# cal the distance...
dist = (np.square(xx - center_x) + np.square(yy - center_y)) / (2 * self.args.sigma) #记录图片上每个点坐标到中心距离平方从除以2sigama
# get the response map...
response = np.exp(-dist) #e^-dist
# normalize...
response = linear_mapping(response)
return response
def start_tracking(self):
time_list = []
# get the image of the first frame... (read as gray scale image...)
init_img = cv2.imread(self.frame_lists[0])
init_frame = cv2.cvtColor(init_img, cv2.COLOR_BGR2GRAY)
init_frame = init_frame.astype(np.float32)
# get the init ground truth.. [x, y, width, height]
init_gt = cv2.selectROI('demo', init_img, False, False)
init_gt = np.array(init_gt).astype(np.int64)
# start to draw the gaussian response...
response_map = self._get_gauss_response(init_frame, init_gt)
# start to create the training set ...
# get the goal..
g = response_map[init_gt[1]:init_gt[1]+init_gt[3], init_gt[0]:init_gt[0]+init_gt[2]]
fi = init_frame[init_gt[1]:init_gt[1]+init_gt[3], init_gt[0]:init_gt[0]+init_gt[2]]
G = np.fft.fft2(g)
# start to do the pre-training...
Ai, Bi = self._pre_training(fi, G)
# start the tracking...
i=0
for idx in range(len(self.frame_lists)):
start = time.time()
current_frame = cv2.imread(self.frame_lists[idx])
frame_gray = cv2.cvtColor(current_frame, cv2.COLOR_BGR2GRAY)
frame_gray = frame_gray.astype(np.float32)
if idx == 0:
Ai = self.args.lr * Ai
Bi = self.args.lr * Bi
pos = init_gt.copy()
clip_pos = np.array([pos[0], pos[1], pos[0]+pos[2], pos[1]+pos[3]]).astype(np.int64)
else:
Hi = Ai / Bi
fi = frame_gray[clip_pos[1]:clip_pos[3], clip_pos[0]:clip_pos[2]]
fi = pre_process(cv2.resize(fi, (init_gt[2], init_gt[3])))
Gi = Hi * np.fft.fft2(fi)
gi = linear_mapping(np.fft.ifft2(Gi))
# find the max pos...
max_value = np.max(gi)
max_pos = np.where(gi == max_value)
dy = int(np.mean(max_pos[0]) - gi.shape[0] / 2)
dx = int(np.mean(max_pos[1]) - gi.shape[1] / 2)
# update the position...
pos[0] = pos[0] + dx
pos[1] = pos[1] + dy
# trying to get the clipped position [xmin, ymin, xmax, ymax]
clip_pos[0] = np.clip(pos[0], 0, current_frame.shape[1])
clip_pos[1] = np.clip(pos[1], 0, current_frame.shape[0])
clip_pos[2] = np.clip(pos[0]+pos[2], 0, current_frame.shape[1])
clip_pos[3] = np.clip(pos[1]+pos[3], 0, current_frame.shape[0])
clip_pos = clip_pos.astype(np.int64)
# get the current fi..
fi = frame_gray[clip_pos[1]:clip_pos[3], clip_pos[0]:clip_pos[2]]
fi = pre_process(cv2.resize(fi, (init_gt[2], init_gt[3])))
# online update...
Ai = self.args.lr * (G * np.conjugate(np.fft.fft2(fi))) + (1 - self.args.lr) * Ai
Bi = self.args.lr * (np.fft.fft2(fi) * np.conjugate(np.fft.fft2(fi))) + (1 - self.args.lr) * Bi
# visualize the tracking process...
cv2.rectangle(current_frame, (pos[0], pos[1]), (pos[0]+pos[2], pos[1]+pos[3]), (255, 0, 0), 2)
#out.write(current_frame)
cv2.imshow('demo11', current_frame)
cv2.imwrite('goog/'+str(i)+'.jpg', current_frame)
i += 1
cv2.waitKey(10)
# if record... save the frames..
# if self.args.record:
# frame_path = 'record_frames/' + self.img_path.split('/')[1] + '/'
# if not os.path.exists(frame_path):
# os.mkdir(frame_path)
# cv2.imwrite(frame_path + str(idx).zfill(5) + '.png', current_frame)
# #out.write(current_frame)
end = time.time()
time_list.append(end-start)
out.release()
print('视频写入成功！')
return time_list
(255, 255, 255), 2) # white
# write down the bounding box location
fm.write(str(lpos))
fm.write('\n')
else:
# mosse
Hi = Ai / Bi
# subWindow
fi = frame_gray[clip_pos[1]:clip_pos[3],
clip_pos[0]:clip_pos[2]]
# keep win size unchanged
fi = pre_process(cv2.resize(fi, (init_gt[2], init_gt[3])))
Gi = Hi * np.fft.fft2(fi)
gi = linear_mapping(np.fft.ifft2(Gi))
# find the max pos
max_value = np.max(gi)
max_pos = np.where(gi == max_value)
dy = int(np.mean(max_pos[0]) - gi.shape[0] / 2)
dx = int(np.mean(max_pos[1]) - gi.shape[1] / 2)
# update the position
pos[0] = pos[0] + dx
pos[1] = pos[1] + dy
# trying to get the clipped position [xmin, ymin, xmax, ymax]
clip_pos[0] = np.clip(pos[0], 0, frame.shape[1])
clip_pos[1] = np.clip(pos[1], 0, frame.shape[0])
clip_pos[2] = np.clip(pos[0] + pos[2], 0, frame.shape[1])
clip_pos[3] = np.clip(pos[1] + pos[3], 0, frame.shape[0])
clip_pos = clip_pos.astype(np.int64)
# get the next fi using the new bounding box
def start_tracking(self):
# get the image of the first frame... (read as gray scale image...)
init_img = cv2.imread(self.frame_lists[0])
init_frame = cv2.cvtColor(init_img, cv2.COLOR_BGR2GRAY)
init_frame = init_frame.astype(np.float32)
# get the init ground truth.. [x, y, width, height]
init_gt = cv2.selectROI('demo', init_img, False, False)
init_gt = np.array(init_gt).astype(np.int64)
# start to draw the gaussian response...
response_map = self._get_gauss_response(init_frame, init_gt)
# start to create the training set ...
# get the goal..
g = response_map[init_gt[1]:init_gt[1]+init_gt[3], init_gt[0]:init_gt[0]+init_gt[2]]
fi = init_frame[init_gt[1]:init_gt[1]+init_gt[3], init_gt[0]:init_gt[0]+init_gt[2]]
G = np.fft.fft2(g)
# start to do the pre-training...
Ai, Bi = self._pre_training(fi, G)
# start the tracking...
for idx in range(len(self.frame_lists)):
current_frame = cv2.imread(self.frame_lists[idx])
frame_gray = cv2.cvtColor(current_frame, cv2.COLOR_BGR2GRAY)
frame_gray = frame_gray.astype(np.float32)
if idx == 0:
Ai = self.args.lr * Ai
Bi = self.args.lr * Bi
pos = init_gt.copy()
clip_pos = np.array([pos[0], pos[1], pos[0]+pos[2], pos[1]+pos[3]]).astype(np.int64)
else:
Hi = Ai / Bi
fi = frame_gray[clip_pos[1]:clip_pos[3], clip_pos[0]:clip_pos[2]]
fi = pre_process(cv2.resize(fi, (init_gt[2], init_gt[3])))
Gi = Hi * np.fft.fft2(fi)
gi = linear_mapping(np.fft.ifft2(Gi))
# find the max pos...
max_value = np.max(gi)
max_pos = np.where(gi == max_value)
dy = int(np.mean(max_pos[0]) - gi.shape[0] / 2)
dx = int(np.mean(max_pos[1]) - gi.shape[1] / 2)
# update the position...
pos[0] = pos[0] + dx
pos[1] = pos[1] + dy
# trying to get the clipped position [xmin, ymin, xmax, ymax]
clip_pos[0] = np.clip(pos[0], 0, current_frame.shape[1])
clip_pos[1] = np.clip(pos[1], 0, current_frame.shape[0])
clip_pos[2] = np.clip(pos[0]+pos[2], 0, current_frame.shape[1])
clip_pos[3] = np.clip(pos[1]+pos[3], 0, current_frame.shape[0])
clip_pos = clip_pos.astype(np.int64)
# get the current fi..
fi = frame_gray[clip_pos[1]:clip_pos[3], clip_pos[0]:clip_pos[2]]
fi = pre_process(cv2.resize(fi, (init_gt[2], init_gt[3])))
# online update...
Ai = self.args.lr * (G * np.conjugate(np.fft.fft2(fi))) + (1 - self.args.lr) * Ai
Bi = self.args.lr * (np.fft.fft2(fi) * np.conjugate(np.fft.fft2(fi))) + (1 - self.args.lr) * Bi
# visualize the tracking process...
cv2.rectangle(current_frame, (pos[0], pos[1]), (pos[0]+pos[2], pos[1]+pos[3]), (255, 0, 0), 2)
cv2.imshow('demo', current_frame)
cv2.waitKey(100)
# if record... save the frames..
if self.args.record:
frame_path = 'record_frames/' + self.img_path.split('/')[1] + '/'
if not os.path.exists(frame_path):
os.mkdir(frame_path)
cv2.imwrite(frame_path + str(idx).zfill(5) + '.png', current_frame)
def start_tracking(self):
# get the image of the first frame... (read as gray scale image...)
init_img = cv2.imread(self.frame_lists[0])
init_frame = cv2.cvtColor(init_img, cv2.COLOR_BGR2GRAY)
init_frame = init_frame.astype(np.float32)
# get the init ground truth.. [x, y, width, height]
init_gt = cv2.selectROI('demo', init_img, False, False) #选取图片中的部分，不用准星，不从随鼠标自动扩展
init_gt = np.array(init_gt).astype(np.int64) #获得选取的矩形框的坐标
# start to draw the gaussian response...
response_map = self._get_gauss_response(init_frame, init_gt)
# start to create the training set ...
# get the goal..
g = response_map[init_gt[1]:init_gt[1]+init_gt[3], init_gt[0]:init_gt[0]+init_gt[2]] #得到高斯化之后矩形框中内容
fi = init_frame[init_gt[1]:init_gt[1]+init_gt[3], init_gt[0]:init_gt[0]+init_gt[2]] #原图中矩形框内容
G = np.fft.fft2(g) #得到理想输出模板在频域中的响应
# start to do the pre-training...
Ai, Bi = self._pre_training(fi, G) #第一帧的滤波器get
# start the tracking...
time=[]
for idx in range(len(self.frame_lists)):
start = cv2.getTickCount()
current_frame = cv2.imread(self.frame_lists[idx])
frame_gray = cv2.cvtColor(current_frame, cv2.COLOR_BGR2GRAY)
frame_gray = frame_gray.astype(np.float32)
if idx == 0:
Ai = self.args.lr * Ai #权值
Bi = self.args.lr * Bi
pos = init_gt.copy()
clip_pos = np.array([pos[0], pos[1], pos[0]+pos[2], pos[1]+pos[3]]).astype(np.int64)
else:
Hi = Ai / Bi #得到上一帧滤波器
fi = frame_gray[clip_pos[1]:clip_pos[3], clip_pos[0]:clip_pos[2]] #获取上一帧锁定位置
fi = pre_process(cv2.resize(fi, (init_gt[2], init_gt[3])))
Gi = Hi * np.fft.fft2(fi) #理想输出模板获取
gi = linear_mapping(np.fft.ifft2(Gi)) #转到空域
# find the max pos...
max_value = np.max(gi) #找到相应最大的值
max_pos = np.where(gi == max_value) #响应最大的坐标
dy = int(np.mean(max_pos[0]) - gi.shape[0] / 2)
dx = int(np.mean(max_pos[1]) - gi.shape[1] / 2)
# update the position...
pos[0] = pos[0] + dx #确定新的锁定框的x坐标
pos[1] = pos[1] + dy #确定新的锁定框的y坐标
# trying to get the clipped position [xmin, ymin, xmax, ymax]
clip_pos[0] = np.clip(pos[0], 0, current_frame.shape[1])
clip_pos[1] = np.clip(pos[1], 0, current_frame.shape[0])
clip_pos[2] = np.clip(pos[0]+pos[2], 0, current_frame.shape[1])
clip_pos[3] = np.clip(pos[1]+pos[3], 0, current_frame.shape[0]) #控制矩形框不要超出图片范围
clip_pos = clip_pos.astype(np.int64)
# get the current fi..
fi = frame_gray[clip_pos[1]:clip_pos[3], clip_pos[0]:clip_pos[2]]
fi = pre_process(cv2.resize(fi, (init_gt[2], init_gt[3]))) #根据当前选中的框大小调整滤波器的大小
# online update...
Ai = self.args.lr * (G * np.conjugate(np.fft.fft2(fi))) + (1 - self.args.lr) * Ai
Bi = self.args.lr * (np.fft.fft2(fi) * np.conjugate(np.fft.fft2(fi))) + (1 - self.args.lr) * Bi #加权下一帧滤波器
# visualize the tracking process...
cv2.rectangle(current_frame, (pos[0], pos[1]), (pos[0]+pos[2], pos[1]+pos[3]), (255, 0, 0), 2) #圈出锁定的目标
cv2.imshow('demo', current_frame)
cv2.waitKey(100)
# if record... save the frames..
if self.args.record:
frame_path = 'record_frames/' + self.img_path.split('/')[1] + '/'
if not os.path.exists(frame_path):
os.mkdir(frame_path)
cv2.imwrite(frame_path + str(idx).zfill(5) + '.jpg', current_frame)
end = cv2.getTickCount()
time.append((end - start) / cv2.getTickFrequency()*1000)
print(time)
print(np.mean(time))
def start_tracking(self):
# get the image of the first frame... (read as gray scale image...)
# 读取到初始的第一帧图像，然后将图像由 BGR 转变为 GRAY 图像
init_img = cv2.imread(self.frame_lists[0])
init_frame = cv2.cvtColor(init_img, cv2.COLOR_BGR2GRAY)
init_frame = init_frame.astype(np.float32)
# get the init ground truth.. [x, y, width, height]
# 这里通过手工框出想要选择的目标区域 [x, y, width, height]，其中 x 和 y 表示的是目标区域左上角顶点的坐标
init_gt = cv2.selectROI('demo', init_img, False, False)
init_gt = np.array(init_gt).astype(np.int64)
# start to draw the gaussian response...
# 得到高斯响应图（输入原始图像以及目标区域的位置 [x, y, width, height]）返回高斯函数矩阵，在选定的目标框的中心，其值最大
response_map = self._get_gauss_response(init_frame, init_gt)
# start to create the training set ...
# get the goal...
# 抽取高斯响应矩阵，矩阵的大小和选中的 ROI 的大小相同。
g = response_map[init_gt[1]:init_gt[1] + init_gt[3], init_gt[0]:init_gt[0] + init_gt[2]]
# 抽取目标区域的图像
fi = init_frame[init_gt[1]:init_gt[1] + init_gt[3], init_gt[0]:init_gt[0] + init_gt[2]]
# 对目标区域的高斯响应图做快速傅立叶变换
G = np.fft.fft2(g)
# 做滤波器的预训练
# start to do the pre-training...
Ai, Bi = self._pre_training(fi, G)
# start the tracking...
for idx in range(len(self.frame_lists)):
current_frame = cv2.imread(self.frame_lists[idx])
frame_gray = cv2.cvtColor(current_frame, cv2.COLOR_BGR2GRAY)
frame_gray = frame_gray.astype(np.float32)
if idx == 0:
Ai = self.args.lr * Ai
Bi = self.args.lr * Bi
pos = init_gt.copy()
# pos 的内容是 [leftX, topY, roi width, roi height]
clip_pos = np.array([pos[0], pos[1], pos[0] + pos[2], pos[1] + pos[3]]).astype(np.int64)
else:
'''
在当前帧中，使用上一帧更新后的搜索区域 (clip_pos) 在本帧中截取相同的位置，使用过滤器与截取区域执行相关操作
相关性最大的位置就是响应最大值的位置，然后更新过滤器 (Ai, Bi)，更新搜索区域 (clip_pos)。
'''
# Ai 和 Bi 在上一帧中已经更新了，现在重新计算出滤波模板 Hi
Hi = Ai / Bi
fi = frame_gray[clip_pos[1]:clip_pos[3], clip_pos[0]:clip_pos[2]]
fi = pre_process(cv2.resize(fi, (init_gt[2], init_gt[3])))
# 使用 Hi 和 fi 进行卷积操作，得到一个响应矩阵
Gi = Hi * np.fft.fft2(fi)
# 对于频域下的 Gi 进行逆傅立叶变换得到实际的 gi
gi = linear_mapping(np.fft.ifft2(Gi))
# 找到响应矩阵 gi 中的最大值
max_value = np.max(gi)
# 获取到 gi 中最大值的坐标，这个位置就是当前帧中被跟踪目标的坐标，只不过这个坐标是相对于 gi，也就是目标区域而言的
max_pos = np.where(gi == max_value)
# gi.shape[0] / 2 就是上一个目标的 y 坐标，也是相对于 gi 这个区域而言，相减得到的 dy 就是当前目标与上一个目标在 y 方向的偏移量
dy = int(np.mean(max_pos[0]) - gi.shape[0] / 2)
# gi.shape[1] / 2 就是上一个目标的 x 坐标，也是相对于 gi 这个区域而言，相减得到的 dx 就是当前目标与上一个目标在 x 方向的偏移量
dx = int(np.mean(max_pos[1]) - gi.shape[1] / 2)
# update the position...
# pos 的内容是 [leftX, topY, roi width, roi height]，也就是 roi 目标框左上角的坐标与目标框的宽 width 和高 height
# 这里只是单纯的将 roi 目标框左上角的坐标进行移动，而对 roi 的长和宽不进行修改，因此 mosse 滤波无法处理跟踪目标的大
# 小发生变化的情况
pos[0] = pos[0] + dx
pos[1] = pos[1] + dy
# trying to get the clipped position [xmin, ymin, xmax, ymax]
# clip_pos 表示的是在这一帧中，目标区域的新位置 [leftX, topY, rightX, bottomY]
clip_pos[0] = np.clip(pos[0], 0, current_frame.shape[1])
clip_pos[1] = np.clip(pos[1], 0, current_frame.shape[0])
clip_pos[2] = np.clip(pos[0] + pos[2], 0, current_frame.shape[1])
clip_pos[3] = np.clip(pos[1] + pos[3], 0, current_frame.shape[0])
clip_pos = clip_pos.astype(np.int64)
# get the current fi..
fi = frame_gray[clip_pos[1]:clip_pos[3], clip_pos[0]:clip_pos[2]]
fi = pre_process(cv2.resize(fi, (init_gt[2], init_gt[3])))
# online update...
# 在线更新 Ai, Bi
# 这里的 lr 就是 learning rate，学习率，加入 lr 可以使得模型更加重视最近的帧，并且使得先前的帧的效果随时间衰减
Ai = self.args.lr * (G * np.conjugate(np.fft.fft2(fi))) + (1 - self.args.lr) * Ai
Bi = self.args.lr * (np.fft.fft2(fi) * np.conjugate(np.fft.fft2(fi))) + (1 - self.args.lr) * Bi
# visualize the tracking process...
cv2.rectangle(current_frame, (pos[0], pos[1]), (pos[0] + pos[2], pos[1] + pos[3]), (0, 0, 255), 2)
cv2.imshow('demo', current_frame)
cv2.waitKey(100)
# if record... save the frames..
if self.args.record:
frame_path = 'record_frames/' + self.img_path.split('/')[1] + '/'
if not os.path.exists(frame_path):
os.mkdir(frame_path)
cv2.imwrite(frame_path + str(idx).zfill(5) + '.png', current_frame)
def start_tracking(self,
coords,
frame_num,
end_frame_num,
bounding_box,
output_bbox,
Video=None,
show_prediction=True,
show_timing=True):
# get the image of the first frame... (read as gray scale image...)
init_img = cv2.imread(self.frame_lists[frame_num])
init_frame = cv2.cvtColor(init_img, cv2.COLOR_BGR2GRAY)
init_frame = init_frame.astype(np.float32)
# get the init ground truth.. [x, y, width, height]
init_gt = coords
init_gt = np.array(init_gt).astype(np.int64)
# start to draw the gaussian response...
response_map = self._get_gauss_response(init_frame, init_gt)
# start to create the training set ...
# get the goal..
g = response_map[init_gt[1]:init_gt[1] + init_gt[3],
init_gt[0]:init_gt[0] + init_gt[2]]
fi = init_frame[init_gt[1]:init_gt[1] + init_gt[3],
init_gt[0]:init_gt[0] + init_gt[2]]
G = np.fft.fft2(g)
# start to do the pre-training...
Ai, Bi = self._pre_training(fi, G)
# start the tracking...
initial = True
x = end_frame_num
if end_frame_num > len(self.frame_lists):
x = len(self.frame_lists)
for idx in range(frame_num, x):
start = time.time()
current_frame = cv2.imread(self.frame_lists[idx])
frame_gray = cv2.cvtColor(current_frame, cv2.COLOR_BGR2GRAY)
frame_gray = frame_gray.astype(np.float32)
if initial:
Ai = self.args.lr * Ai
Bi = self.args.lr * Bi
pos = init_gt.copy()
clip_pos = np.array(
[pos[0], pos[1], pos[0] + pos[2],
pos[1] + pos[3]]).astype(np.int64)
initial = False
else:
Hi = Ai / Bi
fi = frame_gray[clip_pos[1]:clip_pos[3],
clip_pos[0]:clip_pos[2]]
fi = pre_process(cv2.resize(fi, (init_gt[2], init_gt[3])))
Gi = Hi * np.fft.fft2(fi)
gi = linear_mapping(np.fft.ifft2(Gi))
# find the max pos...
max_value = np.max(gi)
max_pos = np.where(gi == max_value)
dy = int(np.mean(max_pos[0]) - gi.shape[0] / 2)
dx = int(np.mean(max_pos[1]) - gi.shape[1] / 2)
# update the position...
pos[0] = pos[0] + dx
pos[1] = pos[1] + dy
# trying to get the clipped position [xmin, ymin, xmax, ymax]
clip_pos[0] = np.clip(pos[0], 0, current_frame.shape[1])
clip_pos[1] = np.clip(pos[1], 0, current_frame.shape[0])
clip_pos[2] = np.clip(pos[0] + pos[2], 0,
current_frame.shape[1])
clip_pos[3] = np.clip(pos[1] + pos[3], 0,
current_frame.shape[0])
clip_pos = clip_pos.astype(np.int64)
# get the current fi..
fi = frame_gray[clip_pos[1]:clip_pos[3],
clip_pos[0]:clip_pos[2]]
fi = pre_process(cv2.resize(fi, (init_gt[2], init_gt[3])))
# online update...
Ai = self.args.lr * (G * np.conjugate(np.fft.fft2(fi))) + (
1 - self.args.lr) * Ai
Bi = self.args.lr * (np.fft.fft2(fi) * np.conjugate(
np.fft.fft2(fi))) + (1 - self.args.lr) * Bi
if show_timing:
print('[INFO] MOSSE took {} seconds'.format(time.time() -
start))
# visualize the tracking process...
if show_prediction:
cv2.rectangle(current_frame, (pos[0], pos[1]),
(pos[0] + pos[2], pos[1] + pos[3]), (255, 0, 0),
6)
plt.figure(figsize=(
15,
4)) #change figure size here. native aspect ratio is 16:9
plt.imshow(cv2.cvtColor(current_frame, cv2.COLOR_BGR2RGB))
plt.show()
if Video is not None:
Video.write(current_frame)
#Save bounding boxes to the array
pos_list = [pos[0], pos[1], pos[2], pos[3]]
bounding_box.append(pos_list)
#print(bounding_box)
# if record... save the frames..
if self.args.record:
frame_path = 'record_frames/' + self.img_path.split(
'/')[1] + '/'
if not os.path.exists(frame_path):
os.mkdir(frame_path)
cv2.imwrite(frame_path + str(idx).zfill(5) + '.png',
current_frame)
return bounding_box
def start_tracking(self):
# get the image of the first frame... (read as gray scale image...)
init_img = cv2.imread(self.frame_lists[0])
init_frame = cv2.cvtColor(init_img, cv2.COLOR_BGR2GRAY)
init_frame = init_frame.astype(np.float32)
# get the init ground truth.. [x, y, width, height]
init_gt = cv2.selectROI('demo', init_img, False, False)
init_gt = np.array(init_gt).astype(np.int64)
print(init_gt)
# start to draw the gaussian response...
response_map = self._get_gauss_response(init_frame, init_gt)
# start to create the training set ...
# get the goal..
g = response_map[init_gt[1]:init_gt[1] + init_gt[3],
init_gt[0]:init_gt[0] + init_gt[2]]
fi = init_frame[init_gt[1]:init_gt[1] + init_gt[3],
init_gt[0]:init_gt[0] + init_gt[2]]
#cv2.imshow('init_frame', init_frame)
#cv2.imshow('fi', fi)
#cv2.waitKey(100)
G = np.fft.fft2(g)
# start to do the pre-training...
Ai, Bi = self._pre_training(fi, G)
# start the tracking...
for idx in range(len(self.frame_lists)):
current_frame = cv2.imread(self.frame_lists[idx])
frame_gray = cv2.cvtColor(current_frame, cv2.COLOR_BGR2GRAY)
frame_gray = frame_gray.astype(np.float32)
if idx == 0:
Ai = self.args.lr * Ai
Bi = self.args.lr * Bi
pos = init_gt.copy()
clip_pos = np.array(
[pos[0], pos[1], pos[0] + pos[2],
pos[1] + pos[3]]).astype(np.int64)
#print("pos and clip pos are : ")
#print(pos)
#print(clip_pos)
#print("Ai and Bi are")
#print(Ai, Bi)
else:
Hi = Ai / Bi
fi = frame_gray[clip_pos[1]:clip_pos[3],
clip_pos[0]:clip_pos[2]]
if (idx == 1):
print("frame gray and fi before preprocessing")
#cv2.imshow("frame_gray", frame_gray)
print(fi)
fi = pre_process(cv2.resize(fi, (init_gt[2], init_gt[3])))
if (idx == 1):
print("fi after preproc:")
print(fi)
Gi = Hi * np.fft.fft2(fi)
gi = linear_mapping(np.fft.ifft2(Gi))
# find the max pos...
max_value = np.max(gi)
max_pos = np.where(gi == max_value)
dy = int(np.mean(max_pos[0]) - gi.shape[0] / 2)
dx = int(np.mean(max_pos[1]) - gi.shape[1] / 2)
if (idx == 1):
print("gi = ")
print(gi)
print(max_value)
print("max_pos = ")
print(max_pos)
print(np.mean(max_pos[0]))
print(gi.shape[0])
print(max_pos[1])
print(gi.shape[0])
print(dy)
print(dx)
# update the position...
pos[0] = pos[0] + dx
pos[1] = pos[1] + dy
# trying to get the clipped position [xmin, ymin, xmax, ymax]
clip_pos[0] = np.clip(pos[0], 0, current_frame.shape[1])
clip_pos[1] = np.clip(pos[1], 0, current_frame.shape[0])
clip_pos[2] = np.clip(pos[0] + pos[2], 0,
current_frame.shape[1])
clip_pos[3] = np.clip(pos[1] + pos[3], 0,
current_frame.shape[0])
clip_pos = clip_pos.astype(np.int64)
# get the current fi..
fi = frame_gray[clip_pos[1]:clip_pos[3],
clip_pos[0]:clip_pos[2]]
fi = pre_process(cv2.resize(fi, (init_gt[2], init_gt[3])))
# online update...
Ai = self.args.lr * (G * np.conjugate(np.fft.fft2(fi))) + (
1 - self.args.lr) * Ai
Bi = self.args.lr * (np.fft.fft2(fi) * np.conjugate(
np.fft.fft2(fi))) + (1 - self.args.lr) * Bi
# visualize the tracking process...
cv2.rectangle(current_frame, (pos[0], pos[1]),
(pos[0] + pos[2], pos[1] + pos[3]), (255, 0, 0), 2)
cv2.imshow('demo', current_frame)
cv2.waitKey(100)
# if record... save the frames..
if self.args.record:
frame_path = 'record_frames/' + self.img_path.split(
'/')[1] + '/'
if not os.path.exists(frame_path):
os.mkdir(frame_path)
cv2.imwrite(frame_path + str(idx).zfill(5) + '.png',
current_frame)
