def test_expected_time(self): d = PerfectDetector(self.train_dataset,self.train_dataset,'dog') img = Image(500,375,self.train_dataset.classes,'test') print d.expected_time(img) assert(d.expected_time(img) == 10) img = Image(250,375,self.train_dataset.classes,'test') assert(d.expected_time(img) == 5)
def generate_synthetic(self):
"""
Generate a synthetic dataset of 4 classes that follows some simple
but strong cooccurence rules.
"""
num_images = 1000
choice_probs = {
(1, 0, 0, 0): 2,
(0, 1, 0, 0): 1,
(0, 0, 1, 0): 1,
(0, 0, 0, 1): 1,
(1, 1, 0, 0): 2,
(1, 0, 1, 0): 0,
(1, 0, 0, 1): 0,
(0, 1, 1, 0): 0,
(0, 1, 0, 1): 1,
(0, 0, 1, 1): 2,
(1, 1, 1, 0): 0,
(1, 1, 0, 1): 1,
(1, 0, 1, 1): 1,
(0, 1, 1, 1): 2
}
probs = np.array(choice_probs.values())
cum_probs = np.cumsum(1. * probs / np.sum(probs))
self.classes = ['A', 'B', 'C', 'D']
for i in range(0, num_images):
image = Image(100, 100, self.classes, str(i))
choice = np.where(cum_probs > np.random.rand())[0][0]
objects = []
for cls_ind, clas in enumerate(choice_probs.keys()[choice]):
if clas == 1:
objects.append(np.array([0, 0, 0, 0, cls_ind, 0, 0]))
image.objects_table = Table(np.array(objects), Image.columns)
self.images.append(image)
def test_get_random_windows(self):
image = Image(width=3, height=2, classes=[], name='test')
window_params = WindowParams(min_width=2,
stride=1,
scales=[1, 0.5],
aspect_ratios=[0.5])
windows = image.get_random_windows(window_params, 2)
assert (windows.shape[0] == 2)
windows = image.get_random_windows(window_params, 3)
assert (windows.shape[0] == 3)
def test_get_windows_lots(self):
t = time.time()
image = Image(width=640, height=480, classes=[], name='test')
window_params = WindowParams()
window_params.min_width = 10
window_params.stride = 8
window_params.aspect_ratios = [0.5, 1, 1.5]
window_params.scales = 1. / 2**np.array([0, 0.5, 1, 1.5, 2])
print(window_params)
windows = image.get_windows(window_params)
time_passed = time.time() - t
print("Generating windows took %.3f seconds" % time_passed)
print(np.shape(windows))
print(windows[:10, :])
rand_ind = np.random.permutation(np.shape(windows)[0])[:10]
print(windows[rand_ind, :])
def test_get_whole_image_bbox(self):
image = Image(20, 10, [], 'test_image')
assert (image.get_whole_image_bbox() == BoundingBox((0, 0, 20, 10)))
image = Image(2, 100, [], 'test_image')
assert (image.get_whole_image_bbox() == BoundingBox((0, 0, 2, 100)))
def test_constructor(self):
image = Image(20, 10, ['A', 'B', 'C'], 'test_image')
assert (image.width == 20 and image.height == 10)
assert (image.classes == ['A', 'B', 'C'])
assert (image.name == 'test_image')
slices = []
for i in range(num_bins):
hist = np.matrix(count_histogram_for_slice(assignments, im_width, im_height, \
num_bins, i, num_words)[1])
hist_sum = float(np.sum(hist))
slices.append(hist / hist_sum)
return slices
if __name__ == '__main__':
assignments = np.zeros((25, 3))
ind = 0
for i in range(5):
for j in range(5):
ass = 1
if (i, j) == (1, 1) or (i, j) == (2, 1) or (i, j) == (3, 1):
ass = 2
if (i, j) == (1, 2) or (i, j) == (2, 2) or (i, j) == (3, 2):
ass = 3
if (i, j) == (1, 3) or (i, j) == (2, 3) or (i, j) == (3, 3):
ass = 4
assignments[ind, :] = np.matrix([[i, j, ass]])
ind += 1
image = Image(size=(5, 5))
num_words = 4
slices = extract_horiz_sclices(3, assignments, image, num_words)
corr_stack = np.matrix([[7, 3, 0, 0], [2, 0, 3, 0], [7, 0, 0, 3]])
slice_stack = np.vstack(slices)
assert (corr_stack.all() == slice_stack.all())
#print slices
