def test_crop_shape():
from cvl.image_io import crop_patch
from cvl.dataset import BoundingBox
import numpy as np
img = np.random.uniform(size=(1200, 600))
region = BoundingBox('tl-size', 50, 60, 100, 200)
crop = crop_patch(img, region)
assert crop.shape == region.shape()
def detect(self, image):
# Convert to grayscale
image = np.sum(image, 2) / 3
patch = self.crop_patch(image)
patch = patch / 255
patch = patch - np.mean(patch)
patch = patch / np.std(patch)
patchf = fft2(patch)
responsef = np.conj(self.template) * patchf
response = ifft2(responsef)
r, c = np.unravel_index(np.argmax(response), response.shape)
# Keep for visualisation
self.last_response = response
r_offset = np.mod(r + self.region_center[0], self.region.height) - self.region_center[0]
c_offset = np.mod(c + self.region_center[1], self.region.width) - self.region_center[1]
self.region.xpos += c_offset
self.region.ypos += r_offset
# Revert update if bbox is completely out of image
if self.region.intersection_box(BoundingBox("tl-size", 0, 0, image.shape[1], image.shape[0])).area() == 0.0:
self.region.ypos -= r_offset
self.region.xpos -= c_offset
return self.region
def detect(self, image):
"""
Find the object's new position in image, using current model M
"""
# Convert to grayscale
image = np.sum(image, 2) / 3
patch = crop_patch(image, self.region)
P = fft2(MOSSETrackerGrayscale.normalize(patch))
response = ifft2( np.conjugate(self.M) * P )
r, c = np.unravel_index(np.argmax(response), response.shape)
# Keep for visualisation
self.last_response = response
r_offset = np.mod(r + self.region_center[0], self.region.height) - self.region_center[0]
c_offset = np.mod(c + self.region_center[1], self.region.width) - self.region_center[1]
self.region.xpos += c_offset
self.region.ypos += r_offset
# Revert update if bbox is completely out of image
if self.region.intersection_box(BoundingBox("tl-size", 0, 0, image.shape[1], image.shape[0])).area() == 0.0:
self.region.ypos -= r_offset
self.region.xpos -= c_offset
return self.region
def test_crop_pad_value_negative():
from cvl.image_io import crop_patch
from cvl.dataset import BoundingBox
import numpy as np
img = np.random.uniform(size=(1200, 600))
region = BoundingBox('tl-size', -1, 0, 100, 75)
crop = crop_patch(img, region)
assert crop[0, 0] == 0
def test_crop_region():
from cvl.image_io import crop_patch
from cvl.dataset import BoundingBox
import numpy as np
img = np.random.uniform(size=(1200, 600))
region = BoundingBox('tl-size', 60, 50, 100, 200)
crop = crop_patch(img, region)
assert crop[0, 0] == img[region.ypos, region.xpos]
assert crop[-1, -1] == img[region.ypos + region.height - 1, region.xpos + region.width - 1]
from cvl.dataset import OnlineTrackingBenchmark, BoundingBox
from cvl.rgb_mosse import MultiMosseTracker
SHOW_TRACKING = True
if __name__ == "__main__":
a_seq = {}
j_x = 0
j_y = 0
for i in range(20):
image = np.zeros((500, 500, 3)).astype('float32')
image[j_x:40 + j_x, j_y:40 + j_y, :] = 1.0
a_seq[i] = {
'image': image,
'bounding_box': BoundingBox('tl-size', j_x - 20, j_y - 20, 80, 80)
}
j_x += 5
j_y += 5
for i in range(20, 40):
image = np.zeros((500, 500, 3)).astype('float32')
image[j_x:40 + j_x, j_y:40 + j_y, :] = 1.0
a_seq[i] = {
'image': image,
'bounding_box': BoundingBox('tl-size', j_x - 20, j_y - 20, 80, 80)
}
j_x -= 5
j_y += 5
for i in range(40, 60):