def test_find_haar_features():
img = Factory.Image(testimage)
img1 = img.copy()
face = HaarCascade("face.xml") # old HaarCascade
f = img.find_haar_features(face)
f2 = img1.find_haar_features("face_cv2.xml") # new cv2 HaarCascade
assert len(f) > 0
assert len(f2) > 0
f.draw()
f2.draw()
f[0].width
f[0].height
f[0].length
f[0].area
results = [img, img1]
name_stem = "test_find_haar_features"
perform_diff(results, name_stem)
# incorrect cascade name
f3 = img.find_haar_features(cascade="incorrect_cascade.xml")
assert_equals(f3, None)
# incorrect cascade object
f4 = img.find_haar_features(cascade=img1)
assert_equals(f4, None)
# Empty image
img2 = Factory.Image((100, 100))
f5 = img2.find_haar_features("face_cv2.xml")
assert_equals(f5, None)
def test_anonymize():
img = Factory.Image(source="lenna")
anon_img = img.anonymize()
# provide features
anon_img1 = img.anonymize(features=["face.xml", "profile.xml"])
# match both images
assert_equals(anon_img.data, anon_img1.data)
# transform function
def transform_blur(img, rect):
np_array = img
x, y, w, h = rect
crop_np_array = np_array[y:y + h, x:x + w]
crop_img = Factory.Image(array=crop_np_array)
blur_img = crop_img.blur((15, 15))
blur_np_array = blur_img
np_array[y:y + h, x:x + w] = blur_np_array
return Factory.Image(array=np_array)
# apply tranform function
anon_img2 = img.anonymize(transform=transform_blur)
perform_diff([anon_img1, anon_img2], "test_anonymize")
def test_find_haar_features():
img = Factory.Image(testimage)
img1 = img.copy()
face = HaarCascade("face.xml") # old HaarCascade
f = img.find_haar_features(face)
f2 = img1.find_haar_features("face_cv2.xml") # new cv2 HaarCascade
assert len(f) > 0
assert len(f2) > 0
f.draw()
f2.draw()
f[0].width
f[0].height
f[0].length
f[0].area
results = [img, img1]
name_stem = "test_find_haar_features"
perform_diff(results, name_stem)
# incorrect cascade name
f3 = img.find_haar_features(cascade="incorrect_cascade.xml")
assert_equals(f3, None)
# incorrect cascade object
f4 = img.find_haar_features(cascade=img1)
assert_equals(f4, None)
# Empty image
img2 = Factory.Image((100, 100))
f5 = img2.find_haar_features("face_cv2.xml")
assert_equals(f5, None)
def test_anonymize():
img = Factory.Image(source="lenna")
anon_img = img.anonymize()
# provide features
anon_img1 = img.anonymize(features=["face.xml", "profile.xml"])
# match both images
assert_equals(anon_img.data, anon_img1.data)
# transform function
def transform_blur(img, rect):
np_array = img
x, y, w, h = rect
crop_np_array = np_array[y:y+h, x:x+w]
crop_img = Factory.Image(array=crop_np_array)
blur_img = crop_img.blur((15, 15))
blur_np_array = blur_img
np_array[y:y+h, x:x+w] = blur_np_array
return Factory.Image(array=np_array)
# apply tranform function
anon_img2 = img.anonymize(transform=transform_blur)
perform_diff([anon_img1, anon_img2], "test_anonymize")
def test_barcode_find_barcode():
img = Factory.Image(BARCODE_IMAGE)
featureset = img.find_barcode()
f = featureset[0]
img_crop = f.crop()
result = [img_crop]
name_stem = "test_barcode_find_barcode"
perform_diff(result, name_stem, 0.0)
def test_barcode():
img = Factory.Image(BARCODE_IMAGE)
barcode = img.find_barcode()[0]
repr_str = "%s.%s at (%d,%d), read data: %s" % (
barcode.__class__.__module__, barcode.__class__.__name__, barcode.x, barcode.y,
barcode.data)
assert_equals(barcode.__repr__(), repr_str)
barcode.draw(color=(255, 0, 0), width=5)
assert_equals(barcode.length(),[262.0])
assert_almost_equals(barcode.get_area(), 68644.0)
perform_diff([img], "test_barcode", 0.0)