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Code Issues Projects Releases Packages Wiki Activity Actions 16

Resolve paths to absolute paths once. Use pathlib glob directly

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This commit is contained in:
Debanjum Singh Solanky 2021-08-09 00:29:51 -07:00
parent ca0a22f4dd
commit 91a2c598fe
1 changed files with 11 additions and 9 deletions
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20
image-search.py
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@ -1,7 +1,5 @@
import sentence_transformers
from sentence_transformers import SentenceTransformer, util from sentence_transformers import SentenceTransformer, util
from PIL import Image from PIL import Image
import glob
import torch import torch
import argparse import argparse
import pathlib import pathlib
@ -17,7 +15,7 @@ def initialize_model():
def extract_entries(image_directory, verbose=False): def extract_entries(image_directory, verbose=False):
image_names = glob.glob(f'{image_directory.expanduser()}/*.jpg') image_names = list(image_directory.glob('*.jpg'))
if verbose: if verbose:
print(f'Found {len(image_names)} images in {image_directory}') print(f'Found {len(image_names)} images in {image_directory}')
return image_names return image_names
@ -28,7 +26,7 @@ def compute_embeddings(image_names, model, embeddings_file, verbose=False):
# Load pre-computed embeddings from file if exists # Load pre-computed embeddings from file if exists
if embeddings_file.exists(): if embeddings_file.exists():
image_embeddings = torch.load(embeddings_file.expanduser()) image_embeddings = torch.load(embeddings_file)
if verbose: if verbose:
print(f"Loaded pre-computed embeddings from {embeddings_file}") print(f"Loaded pre-computed embeddings from {embeddings_file}")
@ -41,7 +39,7 @@ def compute_embeddings(image_names, model, embeddings_file, verbose=False):
if len(images) > 0: if len(images) > 0:
image_embeddings = model.encode(images, batch_size=128, convert_to_tensor=True, show_progress_bar=True) image_embeddings = model.encode(images, batch_size=128, convert_to_tensor=True, show_progress_bar=True)
torch.save(image_embeddings, embeddings_file.expanduser()) torch.save(image_embeddings, embeddings_file)
if verbose: if verbose:
print(f"Saved computed embeddings to {embeddings_file}") print(f"Saved computed embeddings to {embeddings_file}")
@ -72,7 +70,7 @@ def search(query, image_embeddings, model, count=3, verbose=False):
def render_results(hits, image_names, image_directory, count): def render_results(hits, image_names, image_directory, count):
for hit in hits[:count]: for hit in hits[:count]:
print(image_names[hit['corpus_id']]) print(image_names[hit['corpus_id']])
image_path = image_directory.joinpath(image_names[hit['corpus_id']]).expanduser() image_path = image_directory.joinpath(image_names[hit['corpus_id']])
with Image.open(image_path) as img: with Image.open(image_path) as img:
img.show() img.show()
@ -87,14 +85,18 @@ if __name__ == '__main__':
parser.add_argument('--verbose', action='store_true', default=False, help="Show verbose conversion logs. Default: false") parser.add_argument('--verbose', action='store_true', default=False, help="Show verbose conversion logs. Default: false")
args = parser.parse_args() args = parser.parse_args()
# Resolve file, directory paths in args to absolute paths
embeddings_file = args.embeddings_file.expanduser().resolve()
image_directory = args.image_directory.expanduser().resolve(strict=True)
# Initialize Model # Initialize Model
model, count = initialize_model() model, count = initialize_model()
# Extract Entries # Extract Entries
image_names = extract_entries(args.image_directory, args.verbose) image_names = extract_entries(image_directory, args.verbose)
# Compute or Load Embeddings # Compute or Load Embeddings
image_embeddings = compute_embeddings(image_names, model, args.embeddings_file, args.verbose) image_embeddings = compute_embeddings(image_names, model, embeddings_file, args.verbose)
# Run User Queries on Entries in Interactive Mode # Run User Queries on Entries in Interactive Mode
while args.interactive: while args.interactive:
@ -107,4 +109,4 @@ if __name__ == '__main__':
hits = search(user_query, image_embeddings, model, args.results_count, args.verbose) hits = search(user_query, image_embeddings, model, args.results_count, args.verbose)
# render results # render results
render_results(hits, image_names, args.image_directory, count=args.results_count) render_results(hits, image_names, image_directory, count=args.results_count)