def parse_args():
parser = argparse.ArgumentParser()
parser.add_argument('--cuda', action='store_true', default=False,
help='Use NVIDIA GPU acceleration')
parser.add_argument('--img', type=str, default='',
help='Input image path')
parser.add_argument('--out_dir', type=str, default='./result/cam/',
help='Result directory path')
args = parser.parse_args()
args.cuda = args.cuda and torch.cuda.is_available()
if args.cuda:
print("Using GPU for acceleration")
else:
print("Using CPU for computation")
if args.img:
print('Input image: {}'.format(args.img))
else:
print('Input image: raccoon face (scipy.misc.face())')
print('Output directory: {}'.format(args.out_dir))
print()
return args
python类face()的实例源码
def parse_args():
parser = argparse.ArgumentParser()
parser.add_argument('--cuda', action='store_true', default=False,
help='Use NVIDIA GPU acceleration')
parser.add_argument('--img', type=str, default='',
help='Input image path')
parser.add_argument('--out_dir', type=str, default='./result/grad/',
help='Result directory path')
parser.add_argument('--n_samples', type=int, default=10,
help='Sample size of SmoothGrad')
args = parser.parse_args()
args.cuda = args.cuda and torch.cuda.is_available()
if args.cuda:
print("Using GPU for acceleration")
else:
print("Using CPU for computation")
if args.img:
print('Input image: {}'.format(args.img))
else:
print('Input image: raccoon face (scipy.misc.face())')
print('Output directory: {}'.format(args.out_dir))
print('Sample size of SmoothGrad: {}'.format(args.n_samples))
print()
return args
def test_extract_patches_max_patches():
face = downsampled_face
i_h, i_w = face.shape
p_h, p_w = 16, 16
patches = extract_patches_2d(face, (p_h, p_w), max_patches=100)
assert_equal(patches.shape, (100, p_h, p_w))
expected_n_patches = int(0.5 * (i_h - p_h + 1) * (i_w - p_w + 1))
patches = extract_patches_2d(face, (p_h, p_w), max_patches=0.5)
assert_equal(patches.shape, (expected_n_patches, p_h, p_w))
assert_raises(ValueError, extract_patches_2d, face, (p_h, p_w),
max_patches=2.0)
assert_raises(ValueError, extract_patches_2d, face, (p_h, p_w),
max_patches=-1.0)
def load_image(source,
scale=1,
gray=False,
memory=Memory(cachedir=None)):
data_dir = get_data_dirs()[0]
if source == 'face':
image = face(gray=gray)
image = image.astype(np.float32) / 255
if image.ndim == 2:
image = image[..., np.newaxis]
if scale != 1:
image = memory.cache(rescale)(image, scale=scale)
return image
elif source == 'lisboa':
image = imread(join(data_dir, 'images', 'lisboa.jpg'), as_grey=gray)
image = image.astype(np.float32) / 255
if image.ndim == 2:
image = image[..., np.newaxis]
if scale != 1:
image = memory.cache(rescale)(image, scale=scale)
return image
elif source == 'aviris':
image = open_image(
join(data_dir,
'aviris',
'f100826t01p00r05rdn_b/'
'f100826t01p00r05rdn_b_sc01_ort_img.hdr'))
image = np.array(image.open_memmap(), dtype=np.float32)
good_bands = list(range(image.shape[2]))
good_bands.remove(110)
image = image[:, :, good_bands]
indices = image == -50
image[indices] = -1
image[~indices] -= np.min(image[~indices])
image[~indices] /= np.max(image[~indices])
return image
else:
raise ValueError('Data source is not known')
def test_connect_regions():
try:
face = sp.face(gray=True)
except AttributeError:
# Newer versions of scipy have face in misc
from scipy import misc
face = misc.face(gray=True)
for thr in (50, 150):
mask = face > thr
graph = img_to_graph(face, mask)
assert_equal(ndimage.label(mask)[1], connected_components(graph)[0])
def test_connect_regions_with_grid():
try:
face = sp.face(gray=True)
except AttributeError:
# Newer versions of scipy have face in misc
from scipy import misc
face = misc.face(gray=True)
mask = face > 50
graph = grid_to_graph(*face.shape, mask=mask)
assert_equal(ndimage.label(mask)[1], connected_components(graph)[0])
mask = face > 150
graph = grid_to_graph(*face.shape, mask=mask, dtype=None)
assert_equal(ndimage.label(mask)[1], connected_components(graph)[0])
def _downsampled_face():
try:
face = sp.face(gray=True)
except AttributeError:
# Newer versions of scipy have face in misc
from scipy import misc
face = misc.face(gray=True)
face = face.astype(np.float32)
face = (face[::2, ::2] + face[1::2, ::2] + face[::2, 1::2]
+ face[1::2, 1::2])
face = (face[::2, ::2] + face[1::2, ::2] + face[::2, 1::2]
+ face[1::2, 1::2])
face = face.astype(np.float32)
face /= 16.0
return face
def _orange_face(face=None):
face = _downsampled_face() if face is None else face
face_color = np.zeros(face.shape + (3,))
face_color[:, :, 0] = 256 - face
face_color[:, :, 1] = 256 - face / 2
face_color[:, :, 2] = 256 - face / 4
return face_color
def _make_images(face=None):
face = _downsampled_face() if face is None else face
# make a collection of faces
images = np.zeros((3,) + face.shape)
images[0] = face
images[1] = face + 1
images[2] = face + 2
return images
def test_extract_patches_all_color():
face = orange_face
i_h, i_w = face.shape[:2]
p_h, p_w = 16, 16
expected_n_patches = (i_h - p_h + 1) * (i_w - p_w + 1)
patches = extract_patches_2d(face, (p_h, p_w))
assert_equal(patches.shape, (expected_n_patches, p_h, p_w, 3))
def test_extract_patches_all_rect():
face = downsampled_face
face = face[:, 32:97]
i_h, i_w = face.shape
p_h, p_w = 16, 12
expected_n_patches = (i_h - p_h + 1) * (i_w - p_w + 1)
patches = extract_patches_2d(face, (p_h, p_w))
assert_equal(patches.shape, (expected_n_patches, p_h, p_w))
def test_reconstruct_patches_perfect():
face = downsampled_face
p_h, p_w = 16, 16
patches = extract_patches_2d(face, (p_h, p_w))
face_reconstructed = reconstruct_from_patches_2d(patches, face.shape)
np.testing.assert_array_equal(face, face_reconstructed)
def test_reconstruct_patches_perfect_color():
face = orange_face
p_h, p_w = 16, 16
patches = extract_patches_2d(face, (p_h, p_w))
face_reconstructed = reconstruct_from_patches_2d(patches, face.shape)
np.testing.assert_array_equal(face, face_reconstructed)
def main():
args = parse_args()
if not os.path.exists(args.out_dir):
os.makedirs(args.out_dir)
target_layer_names = ['35']
target_index = None
# Prepare input image
if args.img:
img = cv2.imread(args.img, 1)
else:
img = misc.face()
img = np.float32(cv2.resize(img, (224, 224))) / 255
preprocessed_img = preprocess_image(img, args.cuda)
model = vgg19(pretrained=True)
if args.cuda:
model.cuda()
# Prediction
output = model(preprocessed_img)
pred_index = np.argmax(output.data.cpu().numpy())
print('Prediction: {}'.format(IMAGENET_LABELS[pred_index]))
# Prepare grad cam
grad_cam = GradCam(
pretrained_model=model,
target_layer_names=target_layer_names,
cuda=args.cuda)
# Compute grad cam
mask = grad_cam(preprocessed_img, target_index)
save_cam_image(img, mask, os.path.join(args.out_dir, 'grad_cam.jpg'))
print('Saved Grad-CAM image')
# Reload preprocessed image
preprocessed_img = preprocess_image(img)
# Compute guided backpropagation
guided_backprop = GuidedBackpropGrad(
pretrained_model=model, cuda=args.cuda)
guided_backprop_saliency = guided_backprop(preprocessed_img, index=target_index)
cam_mask = np.zeros(guided_backprop_saliency.shape)
for i in range(guided_backprop_saliency.shape[0]):
cam_mask[i, :, :] = mask
cam_guided_backprop = np.multiply(cam_mask, guided_backprop_saliency)
save_as_gray_image(
cam_guided_backprop,
os.path.join(args.out_dir, 'guided_grad_cam.jpg'))
print('Saved Guided Grad-CAM image')
