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import torch
from torch import nn
from torchvision import transforms
import src.extractor as extractor
from PIL import Image
from typing import Union, List, Tuple
from src.correspondences import chunk_cosine_sim
from sklearn.cluster import KMeans
import numpy as np
import time
from external.kmeans_pytorch.kmeans_pytorch import kmeans
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class PoseViTExtractor(extractor.ViTExtractor):
def __init__(self, model_type: str = 'dino_vits8', stride: int = 4, model: nn.Module = None, device: str = 'cuda'):
self.model_type = model_type
self.stride = stride
self.model = model
self.device = device
super().__init__(model_type = self.model_type, stride = self.stride, model=self.model, device=self.device)
self.prep = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize(mean=self.mean, std=self.std)
])
def preprocess(self, img: Image.Image,
load_size: Union[int, Tuple[int, int]] = None) -> Tuple[torch.Tensor, Image.Image]:
scale_factor = 1
if load_size is not None:
width, height = img.size # img has to be quadratic
img = transforms.Resize(load_size, interpolation=transforms.InterpolationMode.LANCZOS)(img)
scale_factor = img.size[0]/width
prep_img = self.prep(img)[None, ...]
return prep_img, img, scale_factor
# Overwrite functionality of _extract_features(self, batch: torch.Tensor, layers: List[int] = 11, facet: str = 'key') -> List[torch.Tensor]
# to extract multiple facets and layers in one turn
def _extract_multi_features(self, batch: torch.Tensor, layers: List[int] = [9,11], facet: str = 'key') -> List[torch.Tensor]:
B, C, H, W = batch.shape
self._feats = []
# for (layer,fac) in zip(layers,facet):
self._register_hooks(layers, facet)
_ = self.model(batch)
self._unregister_hooks()
self.load_size = (H, W)
self.num_patches = (1 + (H - self.p) // self.stride[0], 1 + (W - self.p) // self.stride[1])
return self._feats
def extract_multi_descriptors(self, batch: torch.Tensor, layers: List[int] = [9,11], facet: str = 'key',
bin: List[bool] = [True, False], include_cls: List[bool] = [False, False]) -> torch.Tensor:
assert facet in ['key', 'query', 'value', 'token'], f"""{facet} is not a supported facet for descriptors.
choose from ['key' | 'query' | 'value' | 'token'] """
self._extract_multi_features(batch, layers, facet)
descs = []
for i, x in enumerate(self._feats):
if facet[i] == 'token':
x.unsqueeze_(dim=1) #Bx1xtxd
if not include_cls[i]:
x = x[:, :, 1:, :] # remove cls token
else:
assert not bin[i], "bin = True and include_cls = True are not supported together, set one of them False."
if not bin:
desc = x.permute(0, 2, 3, 1).flatten(start_dim=-2, end_dim=-1).unsqueeze(dim=1) # Bx1xtx(dxh)
else:
desc = self._log_bin(x)
descs.append(desc)
return descs
PhilippAuss
committed
def find_correspondences_fastkmeans(self, pil_img1, pil_img2, num_pairs: int = 10, load_size: int = 224,
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layer: int = 9, facet: str = 'key', bin: bool = True,
thresh: float = 0.05) -> Tuple[List[Tuple[float, float]], List[Tuple[float, float]], Image.Image, Image.Image]:
start_time_corr = time.time()
start_time_desc = time.time()
image1_batch, image1_pil, scale_factor = self.preprocess(pil_img1, load_size)
descriptors1 = self.extract_descriptors(image1_batch.to(self.device), layer, facet, bin)
num_patches1, load_size1 = self.num_patches, self.load_size
image2_batch, image2_pil, scale_factor = self.preprocess(pil_img2, load_size)
descriptors2 = self.extract_descriptors(image2_batch.to(self.device), layer, facet, bin)
num_patches2, load_size2 = self.num_patches, self.load_size
end_time_desc = time.time()
elapsed_desc = end_time_desc - start_time_desc
start_time_saliency = time.time()
# extracting saliency maps for each image
saliency_map1 = self.extract_saliency_maps(image1_batch.to(self.device))[0]
saliency_map2 = self.extract_saliency_maps(image2_batch.to(self.device))[0]
end_time_saliency = time.time()
elapsed_saliencey = end_time_saliency - start_time_saliency
# saliency_map1 = self.extract_saliency_maps(image1_batch)[0]
# saliency_map2 = self.extract_saliency_maps(image2_batch)[0]
# threshold saliency maps to get fg / bg masks
fg_mask1 = saliency_map1 > thresh
fg_mask2 = saliency_map2 > thresh
# calculate similarity between image1 and image2 descriptors
start_time_chunk_cosine = time.time()
similarities = chunk_cosine_sim(descriptors1, descriptors2)
end_time_chunk_cosine = time.time()
elapsed_time_chunk_cosine = end_time_chunk_cosine - start_time_chunk_cosine
start_time_bb = time.time()
# calculate best buddies
image_idxs = torch.arange(num_patches1[0] * num_patches1[1], device=self.device)
sim_1, nn_1 = torch.max(similarities, dim=-1) # nn_1 - indices of block2 closest to block1
sim_2, nn_2 = torch.max(similarities, dim=-2) # nn_2 - indices of block1 closest to block2
sim_1, nn_1 = sim_1[0, 0], nn_1[0, 0]
sim_2, nn_2 = sim_2[0, 0], nn_2[0, 0]
bbs_mask = nn_2[nn_1] == image_idxs
# remove best buddies where at least one descriptor is marked bg by saliency mask.
fg_mask2_new_coors = nn_2[fg_mask2]
fg_mask2_mask_new_coors = torch.zeros(num_patches1[0] * num_patches1[1], dtype=torch.bool, device=self.device)
fg_mask2_mask_new_coors[fg_mask2_new_coors] = True
bbs_mask = torch.bitwise_and(bbs_mask, fg_mask1)
bbs_mask = torch.bitwise_and(bbs_mask, fg_mask2_mask_new_coors)
# applying k-means to extract k high quality well distributed correspondence pairs
# bb_descs1 = descriptors1[0, 0, bbs_mask, :].cpu().numpy()
# bb_descs2 = descriptors2[0, 0, nn_1[bbs_mask], :].cpu().numpy()
bb_descs1 = descriptors1[0, 0, bbs_mask, :]
bb_descs2 = descriptors2[0, 0, nn_1[bbs_mask], :]
# apply k-means on a concatenation of a pairs descriptors.
# all_keys_together = np.concatenate((bb_descs1, bb_descs2), axis=1)
all_keys_together = torch.cat((bb_descs1, bb_descs2), axis=1)
n_clusters = min(num_pairs, len(all_keys_together)) # if not enough pairs, show all found pairs.
length = torch.sqrt((all_keys_together ** 2).sum(axis=1, keepdim=True))
normalized = all_keys_together / length
start_time_kmeans = time.time()
# cluster_ids_x, cluster_centers = kmeans(X = normalized, num_clusters=n_clusters, distance='cosine', device=self.device)
cluster_ids_x, cluster_centers = kmeans(X = normalized,
num_clusters=n_clusters,
distance='cosine',
tqdm_flag = False,
iter_limit=200,
device=self.device)
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kmeans_labels = cluster_ids_x.detach().cpu().numpy()
# kmeans = KMeans(n_clusters=n_clusters, random_state=0).fit(normalized)
end_time_kmeans = time.time()
elapsed_kmeans = end_time_kmeans - start_time_kmeans
bb_topk_sims = np.full((n_clusters), -np.inf)
bb_indices_to_show = np.full((n_clusters), -np.inf)
# rank pairs by their mean saliency value
bb_cls_attn1 = saliency_map1[bbs_mask]
bb_cls_attn2 = saliency_map2[nn_1[bbs_mask]]
bb_cls_attn = (bb_cls_attn1 + bb_cls_attn2) / 2
ranks = bb_cls_attn
for k in range(n_clusters):
for i, (label, rank) in enumerate(zip(kmeans_labels, ranks)):
if rank > bb_topk_sims[label]:
bb_topk_sims[label] = rank
bb_indices_to_show[label] = i
# get coordinates to show
indices_to_show = torch.nonzero(bbs_mask, as_tuple=False).squeeze(dim=1)[
bb_indices_to_show] # close bbs
img1_indices_to_show = torch.arange(num_patches1[0] * num_patches1[1], device=self.device)[indices_to_show]
img2_indices_to_show = nn_1[indices_to_show]
# coordinates in descriptor map's dimensions
img1_y_to_show = (img1_indices_to_show / num_patches1[1]).cpu().numpy()
img1_x_to_show = (img1_indices_to_show % num_patches1[1]).cpu().numpy()
img2_y_to_show = (img2_indices_to_show / num_patches2[1]).cpu().numpy()
img2_x_to_show = (img2_indices_to_show % num_patches2[1]).cpu().numpy()
points1, points2 = [], []
for y1, x1, y2, x2 in zip(img1_y_to_show, img1_x_to_show, img2_y_to_show, img2_x_to_show):
x1_show = (int(x1) - 1) * self.stride[1] + self.stride[1] + self.p // 2
y1_show = (int(y1) - 1) * self.stride[0] + self.stride[0] + self.p // 2
x2_show = (int(x2) - 1) * self.stride[1] + self.stride[1] + self.p // 2
y2_show = (int(y2) - 1) * self.stride[0] + self.stride[0] + self.p // 2
points1.append((y1_show, x1_show))
points2.append((y2_show, x2_show))
end_time_bb = time.time()
end_time_corr = time.time()
elapsed_bb = end_time_bb - start_time_bb
elapsed_corr = end_time_corr - start_time_corr
#print(f"all_corr: {elapsed_corr}, desc: {elapsed_desc}, chunk cosine: {elapsed_time_chunk_cosine}, saliency: {elapsed_saliencey}, kmeans: {elapsed_kmeans}, bb: {elapsed_bb}")
return points1, points2, image1_pil, image2_pil
def find_correspondences(self, pil_img1, pil_img2, num_pairs: int = 10, load_size: int = 224,
layer: int = 9, facet: str = 'key', bin: bool = True,
thresh: float = 0.05) -> Tuple[List[Tuple[float, float]], List[Tuple[float, float]], Image.Image, Image.Image]:
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start_time_corr = time.time()
start_time_desc = time.time()
image1_batch, image1_pil, scale_factor = self.preprocess(pil_img1, load_size)
descriptors1 = self.extract_descriptors(image1_batch.to(self.device), layer, facet, bin)
num_patches1, load_size1 = self.num_patches, self.load_size
image2_batch, image2_pil, scale_factor = self.preprocess(pil_img2, load_size)
descriptors2 = self.extract_descriptors(image2_batch.to(self.device), layer, facet, bin)
num_patches2, load_size2 = self.num_patches, self.load_size
end_time_desc = time.time()
elapsed_desc = end_time_desc - start_time_desc
start_time_saliency = time.time()
# extracting saliency maps for each image
saliency_map1 = self.extract_saliency_maps(image1_batch.to(self.device))[0]
saliency_map2 = self.extract_saliency_maps(image2_batch.to(self.device))[0]
end_time_saliency = time.time()
elapsed_saliencey = end_time_saliency - start_time_saliency
# saliency_map1 = self.extract_saliency_maps(image1_batch)[0]
# saliency_map2 = self.extract_saliency_maps(image2_batch)[0]
# threshold saliency maps to get fg / bg masks
fg_mask1 = saliency_map1 > thresh
fg_mask2 = saliency_map2 > thresh
# calculate similarity between image1 and image2 descriptors
start_time_chunk_cosine = time.time()
similarities = chunk_cosine_sim(descriptors1, descriptors2)
end_time_chunk_cosine = time.time()
elapsed_time_chunk_cosine = end_time_chunk_cosine - start_time_chunk_cosine
start_time_bb = time.time()
# calculate best buddies
image_idxs = torch.arange(num_patches1[0] * num_patches1[1], device=self.device)
sim_1, nn_1 = torch.max(similarities, dim=-1) # nn_1 - indices of block2 closest to block1
sim_2, nn_2 = torch.max(similarities, dim=-2) # nn_2 - indices of block1 closest to block2
sim_1, nn_1 = sim_1[0, 0], nn_1[0, 0]
sim_2, nn_2 = sim_2[0, 0], nn_2[0, 0]
bbs_mask = nn_2[nn_1] == image_idxs
# remove best buddies where at least one descriptor is marked bg by saliency mask.
fg_mask2_new_coors = nn_2[fg_mask2]
fg_mask2_mask_new_coors = torch.zeros(num_patches1[0] * num_patches1[1], dtype=torch.bool, device=self.device)
fg_mask2_mask_new_coors[fg_mask2_new_coors] = True
bbs_mask = torch.bitwise_and(bbs_mask, fg_mask1)
bbs_mask = torch.bitwise_and(bbs_mask, fg_mask2_mask_new_coors)
# applying k-means to extract k high quality well distributed correspondence pairs
bb_descs1 = descriptors1[0, 0, bbs_mask, :].cpu().numpy()
bb_descs2 = descriptors2[0, 0, nn_1[bbs_mask], :].cpu().numpy()
# apply k-means on a concatenation of a pairs descriptors.
all_keys_together = np.concatenate((bb_descs1, bb_descs2), axis=1)
n_clusters = min(num_pairs, len(all_keys_together)) # if not enough pairs, show all found pairs.
length = np.sqrt((all_keys_together ** 2).sum(axis=1))[:, None]
normalized = all_keys_together / length
start_time_kmeans = time.time()
kmeans = KMeans(n_clusters=n_clusters, random_state=0).fit(normalized)
end_time_kmeans = time.time()
elapsed_kmeans = end_time_kmeans - start_time_kmeans
bb_topk_sims = np.full((n_clusters), -np.inf)
bb_indices_to_show = np.full((n_clusters), -np.inf)
# rank pairs by their mean saliency value
bb_cls_attn1 = saliency_map1[bbs_mask]
bb_cls_attn2 = saliency_map2[nn_1[bbs_mask]]
bb_cls_attn = (bb_cls_attn1 + bb_cls_attn2) / 2
ranks = bb_cls_attn
for k in range(n_clusters):
for i, (label, rank) in enumerate(zip(kmeans.labels_, ranks)):
if rank > bb_topk_sims[label]:
bb_topk_sims[label] = rank
bb_indices_to_show[label] = i
# get coordinates to show
indices_to_show = torch.nonzero(bbs_mask, as_tuple=False).squeeze(dim=1)[
bb_indices_to_show] # close bbs
img1_indices_to_show = torch.arange(num_patches1[0] * num_patches1[1], device=self.device)[indices_to_show]
img2_indices_to_show = nn_1[indices_to_show]
# coordinates in descriptor map's dimensions
img1_y_to_show = (img1_indices_to_show / num_patches1[1]).cpu().numpy()
img1_x_to_show = (img1_indices_to_show % num_patches1[1]).cpu().numpy()
img2_y_to_show = (img2_indices_to_show / num_patches2[1]).cpu().numpy()
img2_x_to_show = (img2_indices_to_show % num_patches2[1]).cpu().numpy()
points1, points2 = [], []
for y1, x1, y2, x2 in zip(img1_y_to_show, img1_x_to_show, img2_y_to_show, img2_x_to_show):
x1_show = (int(x1) - 1) * self.stride[1] + self.stride[1] + self.p // 2
y1_show = (int(y1) - 1) * self.stride[0] + self.stride[0] + self.p // 2
x2_show = (int(x2) - 1) * self.stride[1] + self.stride[1] + self.p // 2
y2_show = (int(y2) - 1) * self.stride[0] + self.stride[0] + self.p // 2
points1.append((y1_show, x1_show))
points2.append((y2_show, x2_show))
end_time_bb = time.time()
end_time_corr = time.time()
elapsed_bb = end_time_bb - start_time_bb
elapsed_corr = end_time_corr - start_time_corr
print(f"all_corr: {elapsed_corr}, desc: {elapsed_desc}, chunk cosine: {elapsed_time_chunk_cosine}, saliency: {elapsed_saliencey}, kmeans: {elapsed_kmeans}, bb: {elapsed_bb}")
return points1, points2, image1_pil, image2_pil
def find_correspondences_old(self, pil_img1, pil_img2, num_pairs: int = 10, load_size: int = 224,
layer: int = 9, facet: str = 'key', bin: bool = True,
thresh: float = 0.05) -> Tuple[List[Tuple[float, float]], List[Tuple[float, float]], Image.Image, Image.Image]:
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image1_batch, image1_pil, scale_factor = self.preprocess(pil_img1, load_size)
descriptors1 = self.extract_descriptors(image1_batch.to(self.device), layer, facet, bin)
num_patches1, load_size1 = self.num_patches, self.load_size
image2_batch, image2_pil, scale_factor = self.preprocess(pil_img2, load_size)
descriptors2 = self.extract_descriptors(image2_batch.to(self.device), layer, facet, bin)
num_patches2, load_size2 = self.num_patches, self.load_size
# extracting saliency maps for each image
saliency_map1 = self.extract_saliency_maps(image1_batch.to(self.device))[0]
saliency_map2 = self.extract_saliency_maps(image2_batch.to(self.device))[0]
# saliency_map1 = self.extract_saliency_maps(image1_batch)[0]
# saliency_map2 = self.extract_saliency_maps(image2_batch)[0]
# threshold saliency maps to get fg / bg masks
fg_mask1 = saliency_map1 > thresh
fg_mask2 = saliency_map2 > thresh
# calculate similarity between image1 and image2 descriptors
similarities = chunk_cosine_sim(descriptors1, descriptors2)
# calculate best buddies
image_idxs = torch.arange(num_patches1[0] * num_patches1[1], device=self.device)
sim_1, nn_1 = torch.max(similarities, dim=-1) # nn_1 - indices of block2 closest to block1
sim_2, nn_2 = torch.max(similarities, dim=-2) # nn_2 - indices of block1 closest to block2
sim_1, nn_1 = sim_1[0, 0], nn_1[0, 0]
sim_2, nn_2 = sim_2[0, 0], nn_2[0, 0]
bbs_mask = nn_2[nn_1] == image_idxs
# remove best buddies where at least one descriptor is marked bg by saliency mask.
fg_mask2_new_coors = nn_2[fg_mask2]
fg_mask2_mask_new_coors = torch.zeros(num_patches1[0] * num_patches1[1], dtype=torch.bool, device=self.device)
fg_mask2_mask_new_coors[fg_mask2_new_coors] = True
bbs_mask = torch.bitwise_and(bbs_mask, fg_mask1)
bbs_mask = torch.bitwise_and(bbs_mask, fg_mask2_mask_new_coors)
# applying k-means to extract k high quality well distributed correspondence pairs
bb_descs1 = descriptors1[0, 0, bbs_mask, :].cpu().numpy()
bb_descs2 = descriptors2[0, 0, nn_1[bbs_mask], :].cpu().numpy()
# apply k-means on a concatenation of a pairs descriptors.
all_keys_together = np.concatenate((bb_descs1, bb_descs2), axis=1)
n_clusters = min(num_pairs, len(all_keys_together)) # if not enough pairs, show all found pairs.
length = np.sqrt((all_keys_together ** 2).sum(axis=1))[:, None]
normalized = all_keys_together / length
kmeans = KMeans(n_clusters=n_clusters, random_state=0).fit(normalized)
bb_topk_sims = np.full((n_clusters), -np.inf)
bb_indices_to_show = np.full((n_clusters), -np.inf)
# rank pairs by their mean saliency value
bb_cls_attn1 = saliency_map1[bbs_mask]
bb_cls_attn2 = saliency_map2[nn_1[bbs_mask]]
bb_cls_attn = (bb_cls_attn1 + bb_cls_attn2) / 2
ranks = bb_cls_attn
for k in range(n_clusters):
for i, (label, rank) in enumerate(zip(kmeans.labels_, ranks)):
if rank > bb_topk_sims[label]:
bb_topk_sims[label] = rank
bb_indices_to_show[label] = i
# get coordinates to show
indices_to_show = torch.nonzero(bbs_mask, as_tuple=False).squeeze(dim=1)[
bb_indices_to_show] # close bbs
img1_indices_to_show = torch.arange(num_patches1[0] * num_patches1[1], device=self.device)[indices_to_show]
img2_indices_to_show = nn_1[indices_to_show]
# coordinates in descriptor map's dimensions
img1_y_to_show = (img1_indices_to_show / num_patches1[1]).cpu().numpy()
img1_x_to_show = (img1_indices_to_show % num_patches1[1]).cpu().numpy()
img2_y_to_show = (img2_indices_to_show / num_patches2[1]).cpu().numpy()
img2_x_to_show = (img2_indices_to_show % num_patches2[1]).cpu().numpy()
points1, points2 = [], []
for y1, x1, y2, x2 in zip(img1_y_to_show, img1_x_to_show, img2_y_to_show, img2_x_to_show):
x1_show = (int(x1) - 1) * self.stride[1] + self.stride[1] + self.p // 2
y1_show = (int(y1) - 1) * self.stride[0] + self.stride[0] + self.p // 2
x2_show = (int(x2) - 1) * self.stride[1] + self.stride[1] + self.p // 2
y2_show = (int(y2) - 1) * self.stride[0] + self.stride[0] + self.p // 2
points1.append((y1_show, x1_show))
points2.append((y2_show, x2_show))
return points1, points2, image1_pil, image2_pil