def initialize(self, image, region):
if len(region) == 8:
x_ = np.array(region[::2])
y_ = np.array(region[1::2])
region = [
np.min(x_),
np.min(y_),
np.max(x_) - np.min(x_) + 1,
np.max(y_) - np.min(y_) + 1
]
self.window = max(region[2],
region[3]) * self.parameters.enlarge_factor
left = max(region[0], 0)
top = max(region[1], 0)
right = min(region[0] + region[2], image.shape[1] - 1)
bottom = min(region[1] + region[3], image.shape[0] - 1)
self.position = (region[0] + region[2] / 2, region[1] + region[3] / 2)
self.size = (round(region[2]), round(region[3]))
self.template = image[int(top):int(bottom), int(left):int(right)]
self.kernel = create_epanechnik_kernel(self.size[0], self.size[1],
self.parameters.kernel_sigma)
self.q = normalize_histogram(
extract_histogram(self.template,
self.parameters.histogram_bins,
weights=self.kernel))
def initialize(self, img, region):
"""
Initialize the mean-shift tracker.
Args:
img (numpy.ndarray): First image.
region (list): bounding box specification for the
object on the first image. First and second values
represent the position of the left-upper corner. The
third and fourth values represent the width and the
height of the bounding box respectively.
"""
# Set tracker name.
self.name = "mean-shift-tracker"
# Number of iterations performed to reposition bounding box,
# maximum number of iterations performed and umber of trackings performed.
self.num_it = []
self.max_it = 0
self.num_tracking_runs = 0
# Get initialized position.
self.pos = [region[0] + region[2] / 2, region[1] + region[3] / 2]
# Set tracking patch size. Increment size by one if even.
self.size = (int(region[2]) + abs(int(region[2]) % 2 - 1),
int(region[3]) + abs(int(region[3]) % 2 - 1))
# Initialize tracking window indices grid.
self.mesh_x, self.mesh_y = np.meshgrid(
np.arange(-self.size[0] // 2 + 1, self.size[0] // 2 + 1),
np.arange(-self.size[1] // 2 + 1, self.size[1] // 2 + 1))
# Initialize kernels.
self.kern1 = create_epanechnik_kernel(self.size[0], self.size[1], 2)
self.kern2 = np.ones((self.size[1], self.size[0]))
self.kern_bandwidth = 4
# Get initial patch.
patch = get_patch(img, self.pos, self.size)
# Extract hitrogram from template using the specified kernel.
hist_ = extract_histogram(patch[0],
self.parameters.n_bins,
weights=self.kern1)
self.hist = hist_ / np.sum(hist_)
def initialize(self, image, region):
if len(region) == 8:
x_ = np.array(region[::2])
y_ = np.array(region[1::2])
region = [
np.min(x_),
np.min(y_),
np.max(x_) - np.min(x_) + 1,
np.max(y_) - np.min(y_) + 1
]
self.window = max(region[2],
region[3]) * self.parameters.enlarge_factor
left = max(region[0], 0)
top = max(region[1], 0)
right = min(region[0] + region[2], image.shape[1] - 1)
bottom = min(region[1] + region[3], image.shape[0] - 1)
self.position = (region[0] + region[2] / 2, region[1] + region[3] / 2)
self.size = (region[2], region[3])
self.template = ex2_utils.get_patch(image, self.position, self.size)[0]
# print(self.size)
# print(self.position)
# print(self.window)
# cv2.imshow("Init", self.template)
# cv2.waitKey(0)
ep_kernel = ex2_utils.create_epanechnik_kernel(
self.size[0], self.size[1], self.parameters.kernel_sigma)
self.kernel = ep_kernel[:self.template.shape[0], :self.template.
shape[1]]
# print(f"template shape: {self.template.shape}")
# print(f"Kernel shape: {self.kernel.shape}")
# print(f"Size 0: {self.size[0]}")
# print(f"Size 1: {self.size[1]}")
h1 = ex2_utils.extract_histogram(self.template,
16,
weights=self.kernel)
self.current_model = h1 / np.sum(h1)
def initialize(self, image, region):
region = [int(el) for el in region]
if (region[2] % 2 == 0):
region[2] += 1
if (region[3] % 2 == 0):
region[3] += 1
self.kernel_sigma = 0.5
self.histogram_bins = 8
self.n_of_particles = 50
self.enlarge_factor = 2
self.distance_sigma = 0.11
self.update_alpha = 0.01
self.color_change = (True, "YCRCB")
self.draw_particles = False
self.dynamic_model = "NCV"
if self.color_change[0]:
image = change_colorspace(image, self.color_change[1])
if len(region) == 8:
x_ = np.array(region[::2])
y_ = np.array(region[1::2])
region = [
np.min(x_),
np.min(y_),
np.max(x_) - np.min(x_) + 1,
np.max(y_) - np.min(y_) + 1
]
self.window = max(region[2], region[3]) * self.enlarge_factor
self.position = (region[0] + region[2] / 2, region[1] + region[3] / 2)
self.size = (region[2], region[3])
self.template, _ = get_patch(image, self.position, self.size)
image_pl = image.shape[0] * image.shape[1]
patch_pl = self.size[0] * self.size[1]
q = int(patch_pl / image_pl * 200)
self.q = q if q > 0 else 1
#self.q = 100
# CREATING VISUAL MODEL
self.kernel = create_epanechnik_kernel(self.size[0], self.size[1],
self.kernel_sigma)
self.patch_size = self.kernel.shape
self.template_histogram = normalize_histogram(
extract_histogram(self.template,
self.histogram_bins,
weights=self.kernel))
# GENERATING DYNAMIC MODEL MATRICES
self.system_matrix, self.system_covariance = get_dynamic_model_matrices(
self.q, self.dynamic_model)
self.particle_state = [self.position[0], self.position[1]]
if self.dynamic_model == "NCV":
self.particle_state.extend([0, 0])
if self.dynamic_model == "NCA":
self.particle_state.extend([0, 0, 0, 0])
# GENERATING N PARTICLES AROUND POSITION
self.particles = sample_gauss(self.particle_state,
self.system_covariance,
self.n_of_particles)
self.weights = np.array(
[1 / self.n_of_particles for _ in range(self.n_of_particles)])
def epanechnikov(w, h, sigma):
return ex2_utils.create_epanechnik_kernel(w, h, sigma)