def test_resize_with_fit(self):
filename = os.path.join(BASE_DIR, 'terminal.gif') # 35x28
base_ops = {'w': 50, 'h': 100}
operations = copy(base_ops)
operations.update({'fit': 'crop'})
output = image_transform(filename, operations)
img = PIL.Image.open(output)
self.assertEqual((operations['w'], operations['h']), img.size)
#
# `clip` == `bounds` which will fit only one the
# dimensions in case the aspect ratio is different
#
operations = copy(base_ops)
operations.update({'fit': 'bounds'})
output = image_transform(filename, operations)
img = PIL.Image.open(output)
self.assertEqual((operations['w'], 40), img.size)
operations = copy(base_ops)
operations.update({'fit': 'clip'})
output = image_transform(filename, operations)
img = PIL.Image.open(output)
self.assertEqual((operations['w'], 40), img.size)
def test_quality(self):
filename = os.path.join(BASE_DIR, 'lincoln.jpg')
w, h = PIL.Image.open(filename).size
#
# applying no transformation with the default quality
#
operations = {'w': w, 'h': h}
output = image_transform(filename, operations)
out_file_size = os.stat(output).st_size
operations = {'q': DEFAULT_QUALITY_RATE - 5}
output = image_transform(filename, operations)
self.assertLess(os.stat(output).st_size, out_file_size)
def test_dpr(self):
filename = os.path.join(BASE_DIR, 'lincoln.jpg')
w, h = PIL.Image.open(filename).size
dpr = 2
operations = {'dpr': dpr}
output = image_transform(filename, operations)
ow, oh = PIL.Image.open(output).size
self.assertEqual((w*dpr, h*dpr), (ow, oh))
scale = 2
w, h = w*scale, h*scale
operations = {'dpr': dpr, 'w': w, 'h': h}
output = image_transform(filename, operations)
ow, oh = PIL.Image.open(output).size
self.assertEqual((w*dpr, h*dpr), (ow, oh))
def test_resize(self):
filename = os.path.join(BASE_DIR, 'terminal.gif') # 35x28
operations = {'w': 50, 'h': 100}
output = image_transform(filename, operations)
img = PIL.Image.open(output)
self.assertEqual((operations['w'], operations['h']), img.size)
operations = {'w': 25}
output = image_transform(filename, operations)
img = PIL.Image.open(output)
self.assertEqual(operations['w'], img.size[0])
operations = {'h': 25}
output = image_transform(filename, operations)
img = PIL.Image.open(output)
self.assertEqual(operations['h'], img.size[1])
def main(image_path):
""" Main runs the program to average the
faces in a given file path, displaying
the 'average' face at the end.
**Parameters**
image_path: str
A string indicating the filepath containing
the images to be 'averaged'
**Returns**
None
"""
print('Opening {} and checking for faces...'.format(image_path))
print('Processing images...')
face_check(image_path)
images = process_images(image_path)
print('Finding facial landmarks...')
allandmarks = find_landmarks(image_path)
print('Scaling images to common space...')
scaled_images = scale_images(images, allandmarks)
pointsAvg, pointsNorm = scale_landmarks(images, allandmarks)
print('Triangulating points...')
dt = calculateDelaunayTriangles(np.array(pointsAvg))
print('Averaging faces...')
output = image_transform(scaled_images, pointsNorm, pointsAvg, dt)
print('Success!')
cv2.imshow('image', output)
cv2.waitKey(0)
def test_auto_compress(self):
filename = os.path.join(BASE_DIR, 'lincoln.jpg')
w, h = PIL.Image.open(filename).size
in_file_size = os.stat(filename).st_size
w, h = PIL.Image.open(filename).size
operations = {'q': AGRESSIVE_QUALITY_RATE}
output = image_transform(filename, operations)
size_with_metadata = os.stat(output).st_size
self.assertLess(os.stat(output).st_size, in_file_size)
# applies agressive quality and remove metadata
operations = {'auto': 'compress'}
output = image_transform(filename, operations)
self.assertLess(os.stat(output).st_size, in_file_size)
size_with_no_metadata = os.stat(output).st_size
self.assertLess(size_with_no_metadata, size_with_metadata)
def test_all_ops(self):
for img in ALL_IMAGES:
filename = os.path.join(BASE_DIR, img)
side = 100
dpr = 2
operations = {'w': side,'h': side, 'fit': 'crop', 'fm': 'gif', 'dpr': dpr, 'q': 50, 'auto': 'compress'}
output = image_transform(filename, operations)
ow, oh = PIL.Image.open(output).size
self.assertEqual((side*dpr, side*dpr), (ow, oh))
def test_format(self):
filename = os.path.join(BASE_DIR, 'lincoln.jpg')
in_file_size = os.stat(filename).st_size
operations = {'fm': 'png'}
output = image_transform(filename, operations)
self.assertIn('png', output)
self.assertGreater(os.stat(output).st_size, in_file_size)
filename = os.path.join(BASE_DIR, 'terminal.gif')
in_file_size = os.stat(filename).st_size
operations = {'fm': 'jpeg', 'q': 10}
output = image_transform(filename, operations)
self.assertIn('jpeg', output)
self.assertGreater(os.stat(output).st_size, in_file_size)
for fm in SUPPORTED_FORMATS:
#FIXME: https://github.com/caffeinetv/snappy/issues/10
if fm != 'webp':
operations = {'fm': fm}
output = image_transform(filename, operations)
self.assertIn(fm, output)
def main(image_path):
""" Main runs the program to average the
faces in a given file path, saving the
averaged image in an output image file
and clearing the original image path.
**Parameters**
image_path: str
A string indicating the filepath containing
the images to be 'averaged'
**Returns**
None
"""
print('Opening {} and checking for faces...'.format(image_path))
print('Processing images...')
face_check(image_path)
images = process_images(image_path)
print('Finding facial landmarks...')
allandmarks = find_landmarks(image_path)
print('Scaling images to common space...')
scaled_images = scale_images(images, allandmarks)
pointsAvg, pointsNorm = scale_landmarks(images, allandmarks)
print('Triangulating points...')
dt = calculateDelaunayTriangles(np.array(pointsAvg))
print('Averaging faces...')
output = image_transform(scaled_images, pointsNorm, pointsAvg, dt)
print('Success!')
output = output * 255
output = output.astype('uint8')
cv2.imwrite('static/outputimage/average_face.png', output)
os.chdir('./static/faces')
for file in os.listdir('.'):
print(file)
if file.endswith('.jpg'):
os.remove(file)
elif file.endswith('.tiff'):
os.remove(file)
elif file.endswith('.jpeg'):
os.remove(file)
elif file.endswith('.png'):
os.remove(file)
else:
continue
os.chdir('../..')