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import os
import clip
import faiss
import torch
import tarfile
import gradio as gr
import pandas as pd
from PIL import Image
from braceexpand import braceexpand
from torchvision import transforms
# Load model
checkpoint_path = "ViT-B/16"
device = "cpu"
model, preprocess = clip.load(checkpoint_path, device=device, jit=False)
def generate_caption(img):
# Load caption bank
df = pd.read_parquet("files/captions.parquet")
caption_list = df["caption"].tolist()
# Load index
index = faiss.read_index("files/caption_bank.index")
# Encode the image and query the caption bank index
query_features = model.encode_image(preprocess(img).unsqueeze(0).to(device))
query_features = query_features / query_features.norm(dim=-1, keepdim=True)
query_features = query_features.cpu().detach().numpy().astype("float32")
# Get nearest captions
d, i = index.search(query_features, 1)
d, i = d[0], i[0]
idx = i[0]
distance = d[0]
# Start with a description of the image
caption = caption_list[idx]
print(f"Index: {idx} - Distance: {distance:.2f}")
return caption
def predict_brand(img):
# Load brand bank
df = pd.read_parquet("files/brands.parquet")
brand_list = df["brands"].tolist()
# Load index
index = faiss.read_index("files/brand_bank.index")
# Encode the image and query the brand bank index
query_features = model.encode_image(preprocess(img).unsqueeze(0).to(device))
query_features = query_features / query_features.norm(dim=-1, keepdim=True)
query_features = query_features.cpu().detach().numpy().astype("float32")
# Get nearest brands
d, i = index.search(query_features, 1)
d, i = d[0], i[0]
idx = i[0]
distance = d[0]
brand = brand_list[idx]
print(f"Index: {idx} - Distance: {distance:.2f}")
return brand
def estimate_price_and_usage(img):
query_features = model.encode_image(preprocess(img).unsqueeze(0).to(device))
# Estimate usage
num_classes = 2
probe = torch.nn.Linear(
query_features.shape[-1],
num_classes,
dtype=torch.float16,
bias=False
)
# Load weights for the linear layer as a tensor
linear_data = torch.load("files/reuse_linear.pth", map_location="cpu")
probe.weight.data = linear_data["weight"]
# Do inference
with torch.autocast("cpu"):
probe.eval()
probe = probe.to(device)
output = probe(query_features)
output = torch.softmax(output, dim=-1)
#output = output.cpu().detach().numpy().astype("float32")
reuse = output.argmax(axis=-1)[0]
reuse_classes = ["Reuse", "Export"]
# Estimate price
num_classes = 4
probe = torch.nn.Linear(
query_features.shape[-1],
num_classes,
dtype=torch.float16,
bias=False
)
# Print output shape for the linear layer
# Load weights for the linear layer as a tensor
linear_data = torch.load("files/price_linear.pth", map_location="cpu")
probe.weight.data = linear_data["weight"]
# Do inference
with torch.autocast("cpu"):
probe.eval()
probe = probe.to(device)
output = probe(query_features)
output = torch.softmax(output, dim=-1)
#output = output.cpu().detach().numpy().astype("float32")
price = output.argmax(axis=-1)[0]
price_classes = ["<50", "50-100", "100-150", ">150"]
return f"Estimated price: {price_classes[price]} SEK - Usage: {reuse_classes[reuse]}"
def retrieve(query):
index_folder = "files/index"
num_results = 3
# Read image metadata
metadata_df = pd.read_parquet(os.path.join(index_folder, "metadata.parquet"))
key_list = metadata_df["key"].tolist()
# Load the index
index = faiss.read_index(os.path.join(index_folder, "image.index"))
# Encode the query
if isinstance(query, str):
print("Query is a string")
text = clip.tokenize([query]).to(device)
query_features = model.encode_text(text)
else:
print("Query is an image")
query_features = model.encode_image(preprocess(query).unsqueeze(0).to(device))
query_features = query_features / query_features.norm(dim=-1, keepdim=True)
query_features = query_features.cpu().detach().numpy().astype("float32")
d, i = index.search(query_features, num_results)
print(f"Found {num_results} items with query '{query}'")
indices = i[0]
similarities = d[0]
min_d = min(similarities)
max_d = max(similarities)
print(f"The minimum similarity is {min_d:.2f} and the maximum is {max_d:.2f}")
# Uncomment to generate combined.tar, combine the image_tars into a single tarfile
"""
dataset_dir = "/fs/sefs1/circularfashion/wargon_webdataset/front_only"
image_tars = [os.path.join(dataset_dir, file) for file in sorted(braceexpand("{0000..0028}.tar"))]
with tarfile.open("files/combined.tar", "w") as combined_tar:
for tar in image_tars:
with tarfile.open(tar, "r") as tar_file:
for member in tar_file.getmembers():
combined_tar.addfile(member, tar_file.extractfile(member))
"""
images = []
for idx in indices:
image_name = key_list[idx]
with tarfile.open("files/combined.tar", "r") as tar_file:
image = tar_file.extractfile(f"{image_name}.jpg")
image = Image.open(image).copy()
# Center crop the image
width, height = image.size
new_size = min(width, height)
image = transforms.CenterCrop(new_size)(image)
# Resize the image
image = transforms.Resize((600, 600))(image)
images.append(image)
return images
theme = gr.Theme.from_hub("JohnSmith9982/small_and_pretty")
with gr.Blocks(
theme=theme,
css="footer {visibility: hidden}",
) as demo:
with gr.Tab("Captioning and Prediction"):
with gr.Row(variant="compact"):
input_img = gr.Image(type="pil", show_label=False)
with gr.Column(min_width="80"):
btn_generate_caption = gr.Button("Create Description").style(size="sm")
generated_caption = gr.Textbox(label="Description", show_label=False)
gr.Examples(
examples=["files/examples/example_1.jpg", "files/examples/example_2.jpg"],
fn=generate_caption,
inputs=input_img,
outputs=generated_caption
)
with gr.Row(variant="compact"):
brand_img = gr.Image(type="pil", show_label=False)
with gr.Column(min_width="80"):
btn_predict_brand = gr.Button("Predict Brand").style(size="sm")
predicted_brand = gr.Textbox(label="Brand", show_label=False)
gr.Examples(
examples=["files/examples/example_brand_1.jpg", "files/examples/example_brand_2.jpg"],
fn=predict_brand,
inputs=brand_img,
outputs=predicted_brand
)
with gr.Column(variant="compact"):
btn_estimate = gr.Button("Estimate Price and Reuse").style(size="sm")
text_box = gr.Textbox(label="Estimates:", show_label=False)
with gr.Tab("Image Retrieval"):
with gr.Row(variant="compact"):
with gr.Column():
query_img = gr.Image(type="pil", label="Image Query")
btn_image_query = gr.Button("Retrieve Garments").style(size="sm")
img_query_gallery = gr.Gallery(show_label=False).style(rows=1, columns=3)
gr.Examples(
examples=["files/examples/example_retrieval_1.jpg", "files/examples/example_retrieval_2.jpg"],
fn=retrieve,
inputs=query_img,
outputs=img_query_gallery
)
with gr.Row(variant="compact"):
with gr.Column():
query_text = gr.Textbox(label="Text Query", placeholder="Enter a description")
btn_text_query = gr.Button("Retrieve Garments").style(size="sm")
text_query_gallery = gr.Gallery(show_label=False).style(rows=1, columns=3)
gr.Examples(
examples=["A purple sweater", "A dress with a floral pattern"],
fn=retrieve,
inputs=query_text,
outputs=text_query_gallery
)
# Listeners
btn_generate_caption.click(fn=generate_caption, inputs=input_img, outputs=generated_caption)
btn_predict_brand.click(fn=predict_brand, inputs=brand_img, outputs=predicted_brand)
btn_estimate.click(fn=estimate_price_and_usage, inputs=input_img, outputs=text_box)
btn_image_query.click(fn=retrieve, inputs=query_img, outputs=img_query_gallery)
btn_text_query.click(fn=retrieve, inputs=query_text, outputs=text_query_gallery)
if __name__ == "__main__":
demo.launch(
# inline=True
)
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