def inference_on_single_labelled_image_pca(
query_img_file,
labels_dir,
img_dir,
img_fts_dir,
weights_file,
top_k=1000,
plot=True,
):
"""
Function that returns the average precision for a given query image and also plots the top 20 results
Args:
query_img_file : path of query image file
labels_dir : Directory for ground truth labels
img_dir : Directory holding the images
img_fts_dir : Directory holding the pca reduced features generated through create_db.py script
weights_file: path of trained weights file
top_k : top_k values used to calculate the average precison
plot : if True, top 20 results are plotted
Returns:
Average precision for the query image file
"""
# Create cuda parameters
use_cuda = torch.cuda.is_available()
np.random.seed(2019)
torch.manual_seed(2019)
device = torch.device("cuda" if use_cuda else "cpu")
print("Available device = ", device)
# Create embedding network
resnet_model = create_embedding_net()
model = TripletNet(resnet_model)
model.load_state_dict(torch.load(weights_file))
model.to(device)
model.eval()
# Get query name
query_img_name = query_img_file.split("/")[-1]
query_img_path = os.path.join(img_dir, query_img_name)
# Create Query extractor object
QUERY_EXTRACTOR = QueryExtractor(labels_dir, img_dir, subset="inference")
# Create query ground truth dictionary
query_gt_dict = QUERY_EXTRACTOR.get_query_map()[query_img_name]
# Creat image database
QUERY_IMAGES_FTS = [
os.path.join(img_fts_dir, file)
for file in sorted(os.listdir(img_fts_dir))
]
QUERY_IMAGES = [
os.path.join(img_fts_dir, file) for file in sorted(os.listdir(img_dir))
]
# Query fts
query_fts = get_query_embedding(model, device,
query_img_file).detach().cpu().numpy()
query_fts = perform_pca_on_single_vector(query_fts)
# Create similarity list
similarity = []
for file in tqdm(QUERY_IMAGES_FTS):
file_fts = np.squeeze(np.load(file))
cos_sim = np.dot(query_fts, file_fts) / (np.linalg.norm(query_fts) *
np.linalg.norm(file_fts))
similarity.append(cos_sim)
# Get best matches using similarity
similarity = np.asarray(similarity)
indexes = (-similarity).argsort()[:top_k]
best_matches = [QUERY_IMAGES[index] for index in indexes]
# Get preds
if plot:
preds = get_preds_and_visualize(best_matches, query_gt_dict, img_dir,
20)
else:
preds = get_preds(best_matches, query_gt_dict)
# Get average precision
ap = ap_per_query(best_matches, query_gt_dict)
return ap
def inference_on_single_labelled_image_pca_web(
model,
query_img_file,
labels_dir="./static/data/oxbuild/gt_files/",
img_dir="./static/data/oxbuild/images/",
img_fts_dir="./static/fts_pca/oxbuild/",
top_k=60,
plot=False,
):
"""
Function similar to inference_on_single_labelled_image_pca, but modified return values for usage during deployment
Args:
model : model used (either paris or oxford)
query_img_file : path of query image file
labels_dir : Directory for ground truth labels
img_dir : Directory holding the images
img_fts_dir : Directory holding the pca reduced features generated through create_db.py script
top_k : top_k values used to calculate the average precison, default is 60 for web deployment
plot : if True, top 20 results are plotted
Returns:
List of top k similar images; list of ground truth labels for top k images
"""
# Create cuda parameters
use_cuda = torch.cuda.is_available()
np.random.seed(2019)
torch.manual_seed(2019)
device = torch.device("cuda" if use_cuda else "cpu")
print("Available device = ", device)
# Get query name
query_img_name = query_img_file.split("/")[-1]
query_img_path = os.path.join(img_dir, query_img_name)
# Create Query extractor object
QUERY_EXTRACTOR = QueryExtractor(labels_dir, img_dir, subset="inference")
# Creat image database
QUERY_IMAGES_FTS = [
os.path.join(img_fts_dir, file)
for file in sorted(os.listdir(img_fts_dir))
]
QUERY_IMAGES = [
os.path.join(img_dir, file) for file in sorted(os.listdir(img_dir))
]
# Query fts
query_fts = get_query_embedding(model, device, "." +
query_img_file).detach().cpu().numpy()
query_fts = perform_pca_on_single_vector(query_fts)
# Create similarity list
similarity = []
for file in tqdm(QUERY_IMAGES_FTS):
file_fts = np.squeeze(np.load(file))
cos_sim = np.dot(query_fts, file_fts) / (np.linalg.norm(query_fts) *
np.linalg.norm(file_fts))
similarity.append(cos_sim)
# Get best matches using similarity
similarity = np.asarray(similarity)
indexes = (-similarity).argsort()[:top_k]
best_matches = [QUERY_IMAGES[index] for index in indexes]
print(best_matches)
# Create query ground truth dictionary
gt_map = [0] * 60
try:
query_gt_dict = QUERY_EXTRACTOR.get_query_map()[query_img_name]
gt_map = get_gt_web(best_matches, query_gt_dict)
except:
pass
print(gt_map)
for i in range(len(best_matches)):
best_matches[i] = best_matches[i][1:]
return best_matches, gt_map