def part_3a():
"""Complete the remaining parts of this section as instructed in the
instructions document."""
feature1 = ps6.HaarFeature((2, 1), (25, 30), (50, 100))
feature1.preview((200, 200), filename="ps6-3-a-1.png")
# TODO: Generate and save all required images
feature2 = ps6.HaarFeature((1, 2), (10, 25), (50, 150))
feature2.preview((200, 200), filename="ps6-3-a-2.png")
feature3 = ps6.HaarFeature((3, 1), (50, 50), (100, 50))
feature3.preview((200, 200), filename="ps6-3-a-3.png")
feature4 = ps6.HaarFeature((1, 3), (50, 125), (100, 50))
feature4.preview((200, 200), filename="ps6-3-a-4.png")
feature5 = ps6.HaarFeature((2, 2), (50, 25), (100, 150))
feature5.preview((200, 200), filename="ps6-3-a-5.png")
def test_preview(self):
feat_types = 2 * [(1, 2)] + 2 * [(2, 1)] + 2 * [(1, 3)] \
+ 2 * [(3, 1)] + 2 * [(2, 2)]
positions = 5 * [(6, 9), (8, 5)]
sizes = 5 * [(14, 18), (11, 15)]
samples = 5 * [0, 1]
for j in range(10):
feat_type = feat_types[j]
pos = positions[j]
size = sizes[j]
s = samples[j]
file_name = "haar_preview_ft{}_pos{}_sz{}_{}.npy".format(
feat_type, pos, size, s)
ref_img = np.load(os.path.join(self.input_dir, file_name))
#Uncomment if you want to see the reference image
#cv2.imshow("ref_img", ref_img.astype("uint8"))
#cv2.waitKey(0)
hf = ps6.HaarFeature(feat_type, pos, size)
hf_img = hf.preview((50, 50))
correct = np.allclose(hf_img, ref_img)
message = "Output image does not match the reference solution."
self.assertTrue(correct, message)
def part_3a():
"""Complete the remaining parts of this section as instructed in the
instructions document."""
# feature1 = ps6.HaarFeature((2, 1), (25, 30), (50, 100))
# feature1.preview((200, 200), filename="ps6-3-a-1.png")
feat1 = ps6.HaarFeature((2, 1), (25, 30), (50, 100))
feat1.preview((200, 200), filename='ps6-3-a-1')
feat2 = ps6.HaarFeature((1, 2), (10, 25), (50, 150))
feat2.preview((200, 200), filename='ps6-3-a-2')
feat3 = ps6.HaarFeature((3, 1), (50, 50), (100, 50))
feat3.preview((200, 200), filename='ps6-3-a-3')
feat4 = ps6.HaarFeature((1, 3), (50, 125), (100, 50))
feat4.preview((200, 200), filename='ps6-3-a-4')
feat4 = ps6.HaarFeature((2, 2), (50, 25), (50, 150))
feat4.preview((200, 200), filename='ps6-3-a-5')
def part_3a():
"""Complete the remaining parts of this section as instructed in the
instructions document."""
feature1 = ps6.HaarFeature((2, 1), (25, 30), (50, 100))
feature1.preview((200, 200), filename="ps6-3-a-1")
feature2 = ps6.HaarFeature((1, 2), (10, 25), (50, 150))
feature2.preview((200, 200), filename="ps6-3-a-2")
feature3 = ps6.HaarFeature((3, 1), (50, 50), (100, 50))
feature3.preview((200, 200), filename="ps6-3-a-3")
feature4 = ps6.HaarFeature((1, 3), (50, 125), (100, 50))
feature4.preview((200, 200), filename="ps6-3-a-4")
feature5 = ps6.HaarFeature((2, 2), (50, 25), (100, 150))
feature5.preview((200, 200), filename="ps6-3-a-5")
def part_3a():
"""Complete the remaining parts of this section as instructed in the
instructions document."""
posn = {1 : [(2, 1), (25, 30), (50, 100)],
2 : [(1, 2), (10, 25), (50, 150)],
3 : [(3, 1), (50, 50), (100, 50)],
4 : [(1, 3), (50, 125), (100, 50)],
5 : [(2, 2), (50, 25), (100, 150)]}
for k, p in posn.items():
feature = ps6.HaarFeature(*p)
feature.preview((200, 200), filename="ps6-3-a-%s"%(k))
def test_HaarFeature_evaluate(self):
ti_path = os.path.join(self.input_dir, "test_image_ii.npy")
ii_path = os.path.join(self.input_dir, "integral_image_ii.npy")
test_image = np.load(ti_path)
integral_image = np.load(ii_path)
feat_type = (1, 2) # Change feature type
pos = (5, 5)
size = (30, 30)
if feat_type == (2, 1):
A = np.sum(test_image[pos[0]:pos[0] + size[0] / 2,
pos[1]:pos[1] + size[1]])
B = np.sum(test_image[pos[0] + size[0] / 2:pos[0] + size[0],
pos[1]:pos[1] + size[1]])
ref = A - B
if feat_type == (1, 2):
A = np.sum(test_image[pos[0]:pos[0] + size[0],
pos[1]:pos[1] + size[1] / 2])
B = np.sum(test_image[pos[0]:pos[0] + size[0],
pos[1] + size[1] / 2:pos[1] + size[1]])
ref = A - B
if feat_type == (3, 1):
A = np.sum(test_image[pos[0]:pos[0] + size[0] / 3,
pos[1]:pos[1] + size[1]])
B = np.sum(test_image[pos[0] + size[0] / 3:pos[0] +
2 * size[0] / 3, pos[1]:pos[1] + size[1]])
C = np.sum(test_image[pos[0] + 2 * size[0] / 3:pos[0] + size[0],
pos[1]:pos[1] + size[1]])
ref = A - B + C
if feat_type == (1, 3):
A = np.sum(test_image[pos[0]:pos[0] + size[0],
pos[1]:pos[1] + size[1] / 3])
B = np.sum(test_image[pos[0]:pos[0] + size[0], pos[1] +
size[1] / 3:pos[1] + 2 * size[1] / 3])
C = np.sum(test_image[pos[0]:pos[0] + size[0],
pos[1] + 2 * size[1] / 3:pos[1] + size[1]])
ref = A - B + C
if feat_type == (2, 2):
A = np.sum(test_image[pos[0]:pos[0] + size[0] / 2,
pos[1]:pos[1] + size[1] / 2])
B = np.sum(test_image[pos[0]:pos[0] + size[0] / 2,
pos[1] + size[1] / 2:pos[1] + size[1]])
C = np.sum(test_image[pos[0] + size[0] / 2:pos[0] + size[0],
pos[1]:pos[1] + size[1] / 2])
D = np.sum(test_image[pos[0] + size[0] / 2:pos[0] + size[0],
pos[1] + size[1] / 2:pos[1] + size[1]])
ref = -A + B + C - D
hf = ps6.HaarFeature(feat_type, pos, size)
score = hf.evaluate(integral_image)
showImage(integral_image)
correct = np.allclose(score, ref)
message = "Wrong score returned."
self.assertTrue(correct, message)
