def apply(self, face, patch_size=20, num_patches=50, visualize=False):
patcher = Patcher(patch_size)
patches = patcher.generate_patches(face, num_patches)
if visualize:
for patch in patches:
cv2.rectangle(face,(patch[0][0],patch[0][1]),(patch[1][0],patch[1][1]),(255,0,0),1)
cv2.imshow('img',face)
cv2.waitKey(1500)
cv2.destroyAllWindows()
keypoints = np.array([])
for i in range(patches.shape[0]):
node = self.nodes[0]
while not node.is_leaf:
bt = binary_test(patches[i], face, node.params)
# If 0, go left, if 1 go right
node = node.children[int(bt)]
if keypoints.shape[0] == 0:
keypoints = centroid(patches[i])+node.prob_dist[0]
else:
keypoints += centroid(patches[i])+node.prob_dist[0]
keypoints /= patches.shape[0]
return keypoints
def load_data_provider(self, data, patch_size=20, num_patches=50, num_params=20):
self.data = data
patcher = Patcher(patch_size)
self.patches = np.array([])
self.offsets = np.array([])
self.indexes = np.array([])
# Extract patches and offsets from data provided.
# TODO: Restrict patch extraction to face bounding box
for i, (image, eyes, keypoints, face, face_bbox, rkeypoints) in enumerate(self.data):
patches = patcher.generate_patches(face, num_patches)
offsets = patcher.calculate_offsets_from_patches(patches, rkeypoints)
indexes = np.ones(patches.shape[0],dtype="int")*i
if not self.patches.size:
self.patches = patches
self.offsets = offsets
self.indexes = indexes
else:
self.patches = np.concatenate((self.patches, patches),axis=0)
self.offsets = np.concatenate((self.offsets, offsets),axis=0)
self.indexes = np.concatenate((self.indexes, indexes),axis=0)
self.params_list = patcher.generate_parameters(num_params)
