def estimate_translation(R, T, max_size=1024):
"""
estimate translation
T(x,y) = R(x - dx, y - dy)
motion vector (dx, dy)
"""
Rcrop = util.crop_image(R, (max_size, max_size))
Tcrop = util.crop_image(T, (max_size, max_size))
rgb2gray = lambda x: (0.2125 * x[:, :, 0] + 0.7154 * x[:, :, 1] + 0.0721 *
x[:, :, 2])
if len(R.shape) == 3 and R.shape[2] == 3:
Rg = rgb2gray(Rcrop)
Tg = rgb2gray(Tcrop)
elif len(R.shape) == 2:
Rg = R
Tg = T
else:
raise RuntimeError("Invalid image size!")
# FFT
Fr = np.fft.fft2(Rg)
Ft = np.fft.fft2(Tg)
Fc = Fr * np.conj(Ft)
Rc = Fc / np.abs(Fc)
r = np.fft.ifft2(Rc)
# get the peak
max_r = np.max(r)
max_index = np.argmax(r)
shift = list(np.unravel_index(max_index, r.shape))
for i in range(2):
if r.shape[i] / 2 <= shift[i]:
shift[i] -= r.shape[i]
return shift
def precomputeValues(fishDict,
imgKey,
spotsJSONKey='spotsJson',
maskImgKey='mask',
precomputedPickle='precompAA'):
"""Precomputes values necessary for comparison of the fish to another fish
Args:
fishDict (dict[str,str]): The dictionary describing all components of the fish image
imgKey (str): The key of the fish imge
spotsJSONKey (str, optional): The key in the dictionary indicating the filepath to the json file containing the spot information for the fish. Defaults to 'spotsJson'.
maskImgKey (str, optional): The key in the dictionary indicating the filepath to the mask file containing the boolean mask for the fish. Defaults to 'mask'.
precomputedPickle (str, optional): The key in the dictionary indicating the filepath to the precomputed values of the fish. Defaults to 'precompAA'.
Returns:
[type]: [description]
"""
spots = None
if fishDict[spotsJSONKey] is not None:
with open(fishDict[spotsJSONKey], 'r') as f:
spots = np.asarray(json.load(f))
if len(spots) > 5:
spots = spots[:, :2] # Remove size from spots
mask = crop_image(cv2.imread(fishDict[maskImgKey], 0))
R = get_normalise_direction_matrix(mask)
tmpPoints = np.copy(np.asarray(spots))
tmpPoints = np.dot(R[:, (0, 1)],
np.array([tmpPoints[:, 0], tmpPoints[:, 1]]))
tmpPoints = np.array(list(zip(tmpPoints[0], tmpPoints[1])))
warpedTargetMask = crop_image(
cv2.warpAffine(mask, R,
(mask.shape[1] + 500, mask.shape[0] + 500)))
spotsProcessed = tmpPoints / np.max(warpedTargetMask.nonzero())
invariants, asterisms = astroalign._generate_invariants(
spotsProcessed[:astroalign.MAX_CONTROL_POINTS])
kdTree = cKDTree(invariants)
fishDict[precomputedPickle] = os.path.join(get_cache_dir(),
imgKey + ".aa.pickle")
precomputedObject = {
"max_control_points": astroalign.MAX_CONTROL_POINTS,
"invariants": invariants,
"asterisms": asterisms,
"kdtree": kdTree,
"spots": spots,
"spots_standardised": spotsProcessed,
"nn": astroalign.NUM_NEAREST_NEIGHBORS,
"version": 1
}
with open(os.path.join(get_cache_dir(), imgKey + ".aa.pickle"),
'wb') as f:
pickle.dump(precomputedObject, f)
return precomputedObject
return None
def get_image(self,
crop=False,
output_size=None,
greyscale=False,
flip_red_blue=False):
if self.request_render:
self._render()
if output_size is None:
output_size = self.size
string_image = pygame.image.tostring(self.window, 'RGB')
image = pygame.image.fromstring(string_image, (self.size, self.size),
'RGB')
if greyscale:
array = np.transpose(pygame.surfarray.array3d(image)[:, :, 0])
else:
array = np.transpose(
pygame.surfarray.array3d(image)[:, :, (
2, 1, 0) if flip_red_blue else slice(None)], (1, 0, 2))
if crop:
array = crop_image(array)
if output_size != self.size:
array = cv2.resize(array,
dsize=(output_size, output_size),
interpolation=cv2.INTER_CUBIC)
return array
def crop_workflow_image(workflow, seq_num, img_type):
# TODO: We have to update the checksum!
page = get_next(p for p in workflow.pages if p.sequence_num == seq_num)
if not page:
raise ApiException("Could not find page with sequence number {0}"
.format(seq_num), 404)
if img_type != 'raw':
raise ApiException("Can only crop raw images.", 400)
left = int(request.args.get('left', 0))
top = int(request.args.get('top', 0))
width = int(request.args.get('width', 0)) or None
height = int(request.args.get('height', 0)) or None
crop_image(unicode(page.raw_image), left, top, width, height)
cache_key = "{0}.{1}.{2}".format(workflow.id, 'raw', page.raw_image.name)
cache.delete(cache_key)
return 'OK'
def crop_workflow_image(workflow, seq_num, img_type):
# TODO: We have to update the checksum!
page = get_next(p for p in workflow.pages if p.sequence_num == seq_num)
if not page:
raise ApiException("Could not find page with sequence number {0}"
.format(seq_num), 404)
if img_type != 'raw':
raise ApiException("Can only crop raw images.", 400)
left = int(request.args.get('left', 0))
top = int(request.args.get('top', 0))
width = int(request.args.get('width', 0)) or None
height = int(request.args.get('height', 0)) or None
crop_image(unicode(page.raw_image), left, top, width, height)
cache_key = "{0}.{1}.{2}".format(workflow.id, 'raw', page.raw_image.name)
cache.delete(cache_key)
return 'OK'
async def predict_with_camera_feed(self, robot: anki_vector.Robot) -> None:
"""Use the camera feed from Vector to detect sign language hand signs by applying a trained
convolutional neural network on to images received from the camera feed.
.. code-block:: python
recognizer = SignLanguageRecognizer()
recognizer.load_model("/path/to/model_config_filename",
"/path/to/model_weights_filename")
with anki_vector.Robot(show_viewer=True) as robot:
print("------ predicting hand signs, press ctrl+c to exit early ------")
try:
robot.conn.run_coroutine(recognizer.predict_with_camera_feed(robot))
except KeyboardInterrupt:
print("------ predicting done ------")
"""
with self.graph.as_default():
while True:
await asyncio.sleep(2)
# Get the latest image from the robot's camera feed
camera_image = robot.camera.latest_image.raw_image
# - Image pre-processing -
# Convert the image into black and white using Pillow
black_white_image = camera_image.convert("L")
# Crop the image to reduce the complexity of the network
cropped_image = util.crop_image(black_white_image, util.NetworkConstants.IMAGE_WIDTH, util.NetworkConstants.IMAGE_HEIGHT)
# Convert image to an array with shape (image_width, image_height, 1)
image = img_to_array(cropped_image)
# Normalize the image data
image = image.astype("float") / 255.0
# Expand array shape to add an axis to denote the number of images fed as input
image = np.expand_dims(image, axis=0)
prediction = self.model.predict(image)[0]
prediction = enumerate(prediction)
prediction = sorted(prediction, key=lambda x: x[1], reverse=True)[0]
label = prediction[0]
if label == (util.NetworkConstants.NUM_CLASSES - 1):
label = "No Sign Displayed"
else:
label = chr(label + 97)
prediction = (label, prediction[1] * 100)
print(f"Prediction: {prediction[0]} Confidence: {prediction[1]:.2f}%")
if prediction[0] != "No Sign Displayed":
# If valid prediction is available, use Vector's text-to-speech system to say the
# recognized alphabet out loud
await robot.behavior.say_text(prediction[0])
def main(args):
input_path = args.image_path
dir_path, file_name = ntpath.split(input_path)
original_image = cv2.imread(input_path)
height, width, depth = original_image.shape
detector = MTCNN()
landmarks = detector.detect_faces(original_image)
old_landmark = landmarks[
0] # you have just one face in your profile, otherwise you pick the image with most confident
_, _, nose, theta = get_coordinates(old_landmark)
rotated_image = ndimage.rotate(original_image, theta + 180.0)
new_height, new_width, _ = rotated_image.shape
delta_width = abs(new_width - width) // 2
delta_height = abs(new_height - height) // 2
cropped_image = crop_image(delta_width, new_width - delta_width,
delta_height, new_height - delta_height,
rotated_image)
cv2.imwrite(os.path.join(dir_path, 'rotated_' + file_name), cropped_image)
def tonemap(E, l_remap=(0, 1), saturation=1., numtiles=(4, 4)):
"""
render HDR for viewing
exposure estimate -> log2 -> CLAHE -> remap to l_remap -> gamma correction -> HDR
@param E: exposure (N x M x 3)
@param l_remap: remap intensity to l_remap in the image adjust step
@param saturation: saturation of the color.
@param numtiles: number of contextual tiles in the CLAHE step
return contrast reduced image
"""
if E.shape[0] % numtiles[0] != 0 or E.shape[1] % numtiles[1] != 0:
E = util.crop_image(E, (E.shape[0] // numtiles[0] * numtiles[0],
E.shape[1] // numtiles[1] * numtiles[1]))
l2E, has_nonzero = lognormal(E)
if has_nonzero:
I = tone_operator(l2E, l_remap, saturation, numtiles)
else:
I = l2E
# clip
I[I < 0] = 0
I[1 < I] = 1
return np.uint8(I * 255.)
#############################################################################################################################################
########################### Resize Depth images to match input size #################################
### Crop the depth images using the crop size in mm and store in cropped directory
#####################################################################################################
cropped = 'cropped/' ### directory for the cropped images
names = util.load_names(dataset, phase)
centers = util.load_centers(dataset, phase).astype(float)
cube_size = 150 # crop size in mm
for idx, name in enumerate(names):
img = util.load_image(dataset, os.path.join(root_dir, name))
center = centers[idx]
crop = crop_image(img, center, dataset)
crop -= center[2]
crop = np.maximum(crop, -cube_size)
crop = np.minimum(crop, cube_size)
crop /= cube_size
### depth values are stored temporarily in [0,255] in order to enable viewing/validating
crop += 1
crop *= 255
crop /= 2
cv2.imwrite(os.path.join(root_dir, cropped, name), crop)
if idx % 500 == 0:
print('{}/{}'.format(idx + 1, len(names)))
#####################################################################################################
viewer.set_mesh(mesh, center_and_scale=True)
image = viewer.get_image(crop=False,
output_size=viewer.size,
greyscale=False)
cv2.imwrite(image_filename, image)
if "wgan-results" in sys.argv:
from util import crop_image
COUNT = 5
plot = ImageGrid(COUNT, create_viewer=False)
for i in range(COUNT):
image = plt.imread('screenshots/wgan/{:d}.png'.format(i))
plot.set_image(crop_image(image, background=1), i)
plot.save('plots/wgan-results.pdf')
if 'sdf_net_reconstruction' in sys.argv:
from rendering.raymarching import render_image_for_index
from PIL import Image
from util import crop_image
sdf_net, latent_codes = load_sdf_net(return_latent_codes=True)
COUNT = 5
MESH_FILENAME = 'screenshots/sdf_meshes/{:d}.png'
indices = random.sample(range(latent_codes.shape[0]), COUNT)
print(indices)
def get_data(self, idx):
img_idx, r_idx = idx
annolist = getattr(self.mat['RELEASE'], 'annolist')[img_idx]
if r_idx != -1:
annorect = getattr(annolist, 'annorect')[r_idx]
else:
annorect = getattr(annolist, 'annorect')
image_name = self.image_path + getattr(getattr(annolist, 'image'),
'name')
image = skimage.img_as_float(skimage.io.imread(image_name))
scale = annorect.scale
rotate = 0
if self.task == 'train':
scale *= 1.25
if self.augmentation:
scale *= 2**(random.gauss(0, 1) * MPII.SCALE_FACTOR)
rotate = random.gauss(
0, 1) * MPII.ROTATE_FACTOR if random.random() <= 0.4 else 0
hitbox = 200 * scale
objpos = getattr(annorect, 'objpos')
center = Vector2(getattr(objpos, 'x'), getattr(objpos, 'y'))
ret_image = crop_image(image, center, scale, rotate, 256)
if self.task and self.augmentation:
ret_image[:, :, 0] *= random.uniform(0.6, 1.4)
ret_image[:, :, 1] *= random.uniform(0.6, 1.4)
ret_image[:, :, 2] *= random.uniform(0.6, 1.4)
ret_image = np.clip(ret_image, 0, 1)
assert ret_image.shape == (256, 256, 3)
ret_heatmap = np.zeros(shape=(64, 64, self.joint_num),
dtype=np.float32)
ret_keypoint = np.zeros(shape=(self.joint_num, 2))
keypoints = annorect.annopoints.point
for key_idx in range(keypoints.shape[0]):
joint_id = keypoints[key_idx].id
in_rgb = Vector2(keypoints[key_idx].x,
keypoints[key_idx].y) # input RGB coordinate.
in_heatmap = (in_rgb - center) * 64 / hitbox
if rotate != 0:
cos = math.cos(rotate * math.pi / 180)
sin = math.sin(rotate * math.pi / 180)
in_heatmap = Vector2(sin * in_heatmap.y + cos * in_heatmap.x,
cos * in_heatmap.y - sin * in_heatmap.x)
keypoint = in_heatmap + Vector2(32, 32)
if min(keypoint) < 0 or max(keypoint) >= 64:
continue
ret_heatmap[:, :, joint_id] = generate_heatmap(
64, keypoint.y, keypoint.x) # cropped RGB coordinate.
ret_keypoint[joint_id, :] = [keypoint.y, keypoint.x]
act = getattr(self.mat['RELEASE'], 'act')[img_idx]
ret_activity = act.act_id
ret_threshold = 25.6
return ret_image, ret_heatmap, ret_keypoint, ret_activity, ret_threshold
#############################################################################################################################################
############################ Resize RGB images to match input size #################################
### Crop the RGB images using the crop size in mm and store in cropped directory
#####################################################################################################
cropped = 'cropped/'
names = util.load_names(dataset,phase)
centers = util.load_centers(dataset,phase).astype(float)
centers = np.reshape(centers, (-1,3))
for idx, name in enumerate(names):
img = util.load_image(dataset, os.path.join(root_dir, name))
crop = crop_image(img, centers[idx], dataset)
name = name.replace('.jpeg','.png')
cv2.imwrite(os.path.join(root_dir,cropped,name), crop)
if idx % 500 == 0:
print('{}/{}'.format(idx + 1, len(names)))
#####################################################################################################
############################ Draw pose on cropped RGB samples from normalized labels #################################
### Plot the normalized labels on a sample of RGB images to validate cropping and label normalization
### this segment is only for validation
######################################################################################################################
lbls = util.load_labels(dataset,phase) ### load test/train data
names = util.load_names(dataset,phase)
cords_3d = util.pixel2world(cords_d, 'fpad')
# from world coordinates to rgb image coordinates, gives us an array containing the mapping of each depth image pixel to its corresponding rgb image pixel (if it exists)
cords_c, skel_camcoords = util.world2pixel(cords_3d, 'fpac')
cords_3d = np.reshape(cords_3d, (480, 640, -1))
img_rgbd = np.zeros((img.shape[0], img.shape[1], 4))
cords_c = np.reshape(cords_c, (480, 640, 3))
# using cython functions to drastically increase the speed of image pixel looping
# loops over all RGB-D image pixels and assigns RGB value using cimg and cords_c
img_rgbd = image_loops.color_map(img, cimg, img_rgbd, cords_c)
img_rgbd = np.asarray(img_rgbd)
# use center to crop the image to required input size then normalize depth and RGB values
center = centers[idx]
crop = util.crop_image(img_rgbd, center, 'fpad')
# norm depth values
crop[:, :, 3] -= center[2]
crop[:, :, 3] = np.maximum(crop[:, :, 3], -cube_size)
crop[:, :, 3] = np.minimum(crop[:, :, 3], cube_size)
crop[:, :, 3] /= cube_size
# norm RGB values
crop[:, :, :3] *= 2
crop[:, :, :3] /= 255
crop[:, :, :3] -= 1
# swap axes; channels should be the first axis
imgs[idx] = np.swapaxes(crop, 0, 2)
lbls[idx] = np.asarray(labels[idx].split(), dtype=np.float32)
# print progress
if idx % 500 == 0:
def render_image(sdf_net,
latent_code,
resolution=800,
threshold=0.0005,
sdf_offset=0,
iterations=1000,
ssaa=2,
radius=1.0,
crop=False,
color=(0.8, 0.1, 0.1),
vertical_cutoff=None):
camera_forward = camera_position / np.linalg.norm(camera_position) * -1
camera_distance = np.linalg.norm(camera_position).item()
up = np.array([0, 1, 0])
camera_right = np.cross(camera_forward, up)
camera_right /= np.linalg.norm(camera_right)
camera_up = np.cross(camera_forward, camera_right)
camera_up /= np.linalg.norm(camera_up)
screenspace_points = np.meshgrid(
np.linspace(-1, 1, resolution * ssaa),
np.linspace(-1, 1, resolution * ssaa),
)
screenspace_points = np.stack(screenspace_points)
screenspace_points = screenspace_points.reshape(2, -1).transpose()
points = np.tile(camera_position, (screenspace_points.shape[0], 1))
points = points.astype(np.float32)
focal_distance = 1.0 / math.tan(math.asin(radius / camera_distance))
ray_directions = screenspace_points[:, 0] * camera_right[:, np.newaxis] \
+ screenspace_points[:, 1] * camera_up[:, np.newaxis] \
+ focal_distance * camera_forward[:, np.newaxis]
ray_directions = ray_directions.transpose().astype(np.float32)
ray_directions /= np.linalg.norm(ray_directions, axis=1)[:, np.newaxis]
b = np.einsum('ij,ij->i', points, ray_directions) * 2
c = np.dot(camera_position, camera_position) - radius * radius
distance_to_sphere = (-b - np.sqrt(np.power(b, 2) - 4 * c)) / 2
indices = np.argwhere(np.isfinite(distance_to_sphere)).reshape(-1)
points[indices] += ray_directions[indices] * distance_to_sphere[indices,
np.newaxis]
points = torch.tensor(points, device=device, dtype=torch.float32)
ray_directions_t = torch.tensor(ray_directions,
device=device,
dtype=torch.float32)
indices = torch.tensor(indices, device=device, dtype=torch.int64)
model_mask = torch.zeros(points.shape[0], dtype=torch.uint8)
for i in tqdm(range(iterations)):
test_points = points[indices, :]
sdf = sdf_net.evaluate_in_batches(
test_points, latent_code, return_cpu_tensor=False) + sdf_offset
torch.clamp_(sdf, -0.02, 0.02)
points[indices, :] += ray_directions_t[indices, :] * sdf.unsqueeze(1)
hits = (sdf > 0) & (sdf < threshold)
model_mask[indices[hits]] = 1
indices = indices[~hits]
misses = torch.norm(points[indices, :], dim=1) > radius
indices = indices[~misses]
if indices.shape[0] < 2:
break
model_mask[indices] = 1
if vertical_cutoff is not None:
model_mask[points[:, 1] > vertical_cutoff] = 0
model_mask[points[:, 1] < -vertical_cutoff] = 0
normal = get_normals(sdf_net, points[model_mask],
latent_code).cpu().numpy()
model_mask = model_mask.cpu().numpy().astype(bool)
points = points.cpu().numpy()
model_points = points[model_mask]
seen_by_light = 1.0 - get_shadows(sdf_net,
model_points,
light_position,
latent_code,
radius=radius,
sdf_offset=sdf_offset)
light_direction = light_position[np.newaxis, :] - model_points
light_direction /= np.linalg.norm(light_direction, axis=1)[:, np.newaxis]
diffuse = np.einsum('ij,ij->i', light_direction, normal)
diffuse = np.clip(diffuse, 0, 1) * seen_by_light
reflect = light_direction - np.einsum('ij,ij->i', light_direction,
normal)[:, np.newaxis] * normal * 2
reflect /= np.linalg.norm(reflect, axis=1)[:, np.newaxis]
specular = np.einsum('ij,ij->i', reflect, ray_directions[model_mask, :])
specular = np.clip(specular, 0.0, 1.0)
specular = np.power(specular, 20) * seen_by_light
rim_light = -np.einsum('ij,ij->i', normal, ray_directions[model_mask, :])
rim_light = 1.0 - np.clip(rim_light, 0, 1)
rim_light = np.power(rim_light, 4) * 0.3
color = np.array(color)[np.newaxis, :] * (diffuse * 0.5 + 0.5)[:,
np.newaxis]
color += (specular * 0.3 + rim_light)[:, np.newaxis]
color = np.clip(color, 0, 1)
ground_points = ray_directions[:, 1] < 0
ground_points[model_mask] = 0
ground_points = np.argwhere(ground_points).reshape(-1)
ground_plane = np.min(model_points[:, 1]).item()
points[ground_points, :] -= ray_directions[ground_points, :] * (
(points[ground_points, 1] - ground_plane) /
ray_directions[ground_points, 1])[:, np.newaxis]
ground_points = ground_points[
np.linalg.norm(points[ground_points, ::2], axis=1) < 3]
ground_shadows = get_shadows(sdf_net,
points[ground_points, :],
light_position,
latent_code,
sdf_offset=sdf_offset)
pixels = np.ones((points.shape[0], 3))
pixels[model_mask] = color
pixels[ground_points] -= ((1.0 - 0.65) * ground_shadows)[:, np.newaxis]
pixels = pixels.reshape((resolution * ssaa, resolution * ssaa, 3))
if crop:
from util import crop_image
pixels = crop_image(pixels, background=1)
image = Image.fromarray(np.uint8(pixels * 255), 'RGB')
if ssaa != 1:
image = image.resize((resolution, resolution), Image.ANTIALIAS)
return image
def findClosestMatch(modelImageName,
modelDict,
imagesToComareDicts,
spotsJsonKey='spotsJson',
maskImgKey='mask',
verbose=False,
progress=False):
"""Finds the best matches for the model image in the images to compare dictionaries
Args:
modelImageName (str): The name of the image being compared
modelDict (dict): The dictionary containing data about the image
imagesToComareDicts (Dict[Dict]): The list of other image dictionaries to compare to.
spotsJsonKey (str, optional): The key in the dictionary indicating the filepath to the json file containing the spot information for the fish. Defaults to 'spotsJson'.
maskImgKey (str, optional): The key in the dictionary indicating the filepath to the mask file containing the boolean mask for the fish. Defaults to 'mask'.
verbose (bool, optional): If to print verbose information to stdout. Defaults to False.
progress (bool, optional): If to pring progress information to stdout. Defaults to False.
Returns:
List[Tuple[float, float, str]]: List of tuples of (score, maskCoverage, image name) for each image the model image was compared to.
"""
modelPrecompValues = open_cached(modelDict,
modelImageName,
spotsJsonKey=spotsJsonKey,
maskImgKey=maskImgKey)
if modelPrecompValues is None:
if verbose:
print("Not enough points")
return []
centerTranslationMatrix = np.float32([[1, 0, 250], [0, 1, 250], [0, 0, 1]])
targetSpots = cv2.imread(modelDict["spots"], 0)
targetMask = crop_image(cv2.imread(modelDict[maskImgKey], 0))
targetMaskShifted = cv2.warpPerspective(
targetMask, centerTranslationMatrix,
(targetMask.shape[1] + 500, targetMask.shape[0] + 500))
if verbose:
print(modelImageName)
visualize(modelPrecompValues["spots_standardised"],
modelPrecompValues["spots_standardised"],
annotateOrder=True,
invertYAxis=False,
figsize=(10, 10))
ranking = []
for imageKey in imagesToComareDicts:
if progress or verbose:
print(imageKey)
comparatorImageDict = imagesToComareDicts[imageKey]
dataPrecompValues = open_cached(comparatorImageDict,
imageKey,
spotsJsonKey=spotsJsonKey,
maskImgKey=maskImgKey)
if dataPrecompValues is None:
ranking.append((-1, 0, imageKey))
continue
if verbose:
visualize(dataPrecompValues["spots_standardised"],
dataPrecompValues["spots_standardised"],
annotateOrder=True,
invertYAxis=False,
figsize=(10, 10))
T, num_points, score, s_idx, t_idx = aamatch(
dataPrecompValues["spots_standardised"],
modelPrecompValues["spots_standardised"],
dataPrecompValues["invariants"], dataPrecompValues["asterisms"],
dataPrecompValues["kdtree"], modelPrecompValues["invariants"],
modelPrecompValues["asterisms"], modelPrecompValues["kdtree"])
maskCoverage = 0
spotsCoverage = 0
if T is not None:
if verbose:
transformedCoords = []
for coord in dataPrecompValues["spots_standardised"]:
coordNew = np.array([coord[0], coord[1], 1])
coordNew = T.params @ coordNew
transformedCoords.append(coordNew[:2])
maxValue = max(np.max(dataPrecompValues["spots_standardised"]),
np.max(dataPrecompValues["spots_standardised"]),
np.max(np.asarray(transformedCoords)))
visualize(dataPrecompValues["spots_standardised"],
dataPrecompValues["spots_standardised"],
annotateOrder=True,
invertYAxis=False,
figsize=(10, 10))
visualize(modelPrecompValues["spots_standardised"],
modelPrecompValues["spots_standardised"],
annotateOrder=True,
invertYAxis=False,
figsize=(10, 10))
visualize(modelPrecompValues["spots_standardised"],
np.asarray(transformedCoords),
annotateOrder=False,
invertYAxis=False,
figsize=(10, 10))
model = skimage.transform.AffineTransform()
model.estimate(dataPrecompValues["spots"][s_idx],
modelPrecompValues["spots"][t_idx])
dataMask = crop_image(
cv2.imread(comparatorImageDict[maskImgKey], 0))
warped = cv2.warpPerspective(
dataMask, centerTranslationMatrix @ model.params,
(targetMaskShifted.shape[1], targetMaskShifted.shape[0]))
pMask, rMask = get_average_precision_recall(
np.array([warped]),
np.array([targetMaskShifted]),
verbose=verbose)
maskCoverage = 2 * (pMask * rMask) / (pMask + rMask)
ranking.append((score * maskCoverage, maskCoverage, imageKey))
return ranking
def data_capture(camera: anki_vector.camera.CameraComponent, stats: dict,
root_folder: str) -> None:
"""Build an image dataset using the camera feed from Vector.
This method uses an image from the camera and generates a multiplier number of images by
rotating the original image. The keystroke used to initiate the image capture and processing
is used to label the image.
"""
try:
# TODO: curses works well with Mac OS and Linux, explore msvcrt for Windows
terminal = curses.initscr()
curses.cbreak()
curses.noecho()
terminal.nodelay(True)
# The number of images to generate using the image captured as a seed
image_multiplier = 10
# The maximum amount of rotation by which to rotate the original image to generate more images
min_rotation = -10
max_rotation = 10
print(
"------ capturing hand signs dataset, press ctrl+c to exit ------")
while True:
key = terminal.getch()
if (ord("a") <= key <= ord("z")) or (key == ord(" ")):
# Represents background images, filenames are switched to be prefixed with "background" instead of " "
if key == ord(" "):
key = "background"
else:
key = chr(key)
# Pull image from camera
original_image = camera.latest_image.raw_image
if original_image:
# Convert image to black and white
black_white_image = original_image.convert("L")
rotation_axes = [1, 1, 0]
# Generate more images with random rotation
for rotation in random.sample(
range(min_rotation, max_rotation),
image_multiplier):
# Randomly define which axis to rotate the image by
random.shuffle(rotation_axes)
x_axis_rotation_enabled, y_axis_rotation_enabled = rotation_axes[:
2]
rotated_image_array = ndimage.rotate(
black_white_image,
rotation,
axes=(x_axis_rotation_enabled,
y_axis_rotation_enabled),
reshape=False)
# Convert to a 200*200 image
rotated_image = Image.fromarray(rotated_image_array)
cropped_image = util.crop_image(
rotated_image, util.NetworkConstants.IMAGE_WIDTH,
util.NetworkConstants.IMAGE_HEIGHT)
# Save the image
image_filename = key + "_" + str(stats.get(key,
0)) + ".png"
stats[key] = stats.get(key, 0) + 1
cropped_image.save(
os.path.join(root_folder, image_filename))
# Character
print(f"Recorded images for {key}\n\r")
except (CancelledError, KeyboardInterrupt):
pass
finally:
curses.nocbreak()
curses.echo()
curses.endwin()