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modify od fashion
Browse files- data/dior_show/dior1.jpg +0 -0
- data/dior_show/dior2.jpg +0 -0
- data/dior_show/dior3.jpg +0 -0
- data/dior_show/dior4.jpg +0 -0
- images/fashion_ai.jpg +0 -0
- images/fashion_od.jpg +0 -0
- images/fashion_od2.png +0 -0
- pages/object_detection.py +79 -37
data/dior_show/dior1.jpg
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data/dior_show/dior2.jpg
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data/dior_show/dior3.jpg
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data/dior_show/dior4.jpg
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images/fashion_ai.jpg
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images/fashion_od.jpg
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images/fashion_od2.png
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pages/object_detection.py
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@@ -4,14 +4,24 @@ import streamlit as st
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import matplotlib.pyplot as plt
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import pandas as pd
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import numpy as np
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import altair as alt
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from PIL import Image
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from transformers import YolosFeatureExtractor, YolosForObjectDetection
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from torchvision.transforms import ToTensor, ToPILImage
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st.set_page_config(layout="wide")
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def rgb_to_hex(rgb):
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"""Converts an RGB tuple to an HTML-style Hex string."""
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@@ -76,7 +86,7 @@ def plot_results(pil_img, prob, boxes):
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plt.savefig("results_od.png",
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bbox_inches ="tight")
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st.image("results_od.png")
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return colors_used
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@@ -112,15 +122,23 @@ def visualize_probas(probas, threshold, colors):
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top_label_df["colors"] = colors
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top_label_df.sort_values(by=["proba"], ascending=False, inplace=True)
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st.dataframe(top_label_df.drop(columns=["colors"]))
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mode_func = lambda x: x.mode().iloc[0]
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top_label_df_agg = top_label_df.groupby("label").agg({"proba":"mean", "colors":mode_func})
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top_label_df_agg = top_label_df_agg.reset_index().sort_values(by=["proba"], ascending=False)
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chart = alt.Chart(top_label_df_agg).mark_bar().encode(x="proba", y="label",
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#st.altair_chart(chart)
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st.markdown(" ")
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st.divider()
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st.markdown("
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st.
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#images_dior = [os.path.join("data/dior_show",url) for url in os.listdir("data/dior_show") if url != "results"]
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#st.image(images_dior, width=250, caption=[file for file in os.listdir("data/dior_show") if file != "results"])
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st.markdown(" ")
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#st.markdown("##### Select an image")
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############## SELECT AN IMAGE ###############
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st.markdown("####
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st.
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image_ = None
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select_image_box = st.radio(
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"",
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["Choose an existing image", "Load your own image"],
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index=None, label_visibility="collapsed")
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if select_image_box == "Choose an existing image":
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fashion_images_path = r"data/
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list_images = os.listdir(fashion_images_path)
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image_ = st.selectbox("", list_images, label_visibility="collapsed")
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st.warning("""**Note**: The model tends to perform better with images of people/clothing items facing forward.
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Choose this type of image if you want optimal results.""")
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if image_ is not None:
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st.image(Image.open(image_), width=300)
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@@ -216,7 +240,7 @@ cats = ['shirt, blouse', 'top, t-shirt, sweatshirt', 'sweater', 'cardigan', 'jac
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dict_cats = dict(zip(np.arange(len(cats)), cats))
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st.markdown("####
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# Select one or more elements to detect
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container = st.container()
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############## SELECT A THRESHOLD ###############
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st.markdown("####
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st.
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The threshold helps you decide how confident you want your model to be with its predictions.
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Elements that were identified with a lower probability than the given threshold will be ignored in the final results.""")
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threshold = st.slider('**Select a threshold**', min_value=0.0, step=0.05, max_value=1.0, value=0.75, label_visibility="collapsed")
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# min_value=0.000000, step=0.000001, max_value=0.0005, value=0.0000045, format="%f"
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st.
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st.markdown(" ")
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image = fix_channels(ToTensor()(image))
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## LOAD OBJECT DETECTION MODEL
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model =
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# RUN MODEL ON IMAGE
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inputs = feature_extractor(images=image, return_tensors="pt")
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outputs = model(**inputs)
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probas, keep = return_probas(outputs, threshold)
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# PLOT BOUNDING BOX AND BARS/PROBA
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col1, col2 = st.columns(2)
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with col1:
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st.markdown("
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bboxes_scaled = rescale_bboxes(outputs.pred_boxes[0, keep].cpu(), image.size)
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colors_used = plot_results(image, probas[keep], bboxes_scaled)
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with col2:
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st.info("Done")
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else:
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st.
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import matplotlib.pyplot as plt
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import pandas as pd
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import numpy as np
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#import altair as alt
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import plotly.express as px
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from PIL import Image
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from transformers import YolosFeatureExtractor, YolosForObjectDetection
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from torchvision.transforms import ToTensor, ToPILImage
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#from utils import load_model_huggingface
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st.set_page_config(layout="wide")
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@st.cache_data(ttl=3600, show_spinner=False)
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def load_model(feature_extractor_url, model_url):
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feature_extractor_ = YolosFeatureExtractor.from_pretrained(feature_extractor_url)
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model_ = YolosForObjectDetection.from_pretrained(model_url)
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return feature_extractor_, model_
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def rgb_to_hex(rgb):
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"""Converts an RGB tuple to an HTML-style Hex string."""
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plt.savefig("results_od.png",
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bbox_inches ="tight")
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plt.show()
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st.image("results_od.png")
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return colors_used
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top_label_df["colors"] = colors
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top_label_df.sort_values(by=["proba"], ascending=False, inplace=True)
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#st.dataframe(top_label_df.drop(columns=["colors"]))
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mode_func = lambda x: x.mode().iloc[0]
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top_label_df_agg = top_label_df.groupby("label").agg({"proba":"mean", "colors":mode_func})
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top_label_df_agg = top_label_df_agg.reset_index().sort_values(by=["proba"], ascending=False)
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top_label_df_agg.columns = ["Item","Score","Colors"]
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color_map = dict(zip(top_label_df_agg["Item"].to_list(),
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top_label_df_agg["Colors"].to_list()))
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fig = px.bar(top_label_df_agg, y='Item', x='Score',
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color="Item", title="Probability scores")
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st.plotly_chart(fig, use_container_width=True)
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# chart = alt.Chart(top_label_df_agg).mark_bar().encode(x="proba", y="label",
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# color=alt.Color('colors:N', scale=None)).interactive()
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# st.altair_chart(chart)
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st.markdown(" ")
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st.divider()
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st.markdown("## Fashion Object Detection 👗")
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# st.info("""This use case showcases the application of **Object detection** to detect clothing items/features on images. <br>
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# The images used were gathered from Dior's""")
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st.info("""In this use case, we are going to identify and locate different articles of clothings, as well as finer details such as a collar or pocket using an object detection AI model.
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The images used were taken from **Dior's 2020 Fall Women Fashion Show**.""")
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st.markdown(" ")
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images_dior = [os.path.join("data/dior_show",url) for url in os.listdir("data/dior_show") if url != "results"]
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columns_img = st.columns(4)
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for img, col in zip(images_dior,columns_img):
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with col:
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st.image(img)
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st.markdown(" ")
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############## SELECT AN IMAGE ###############
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st.markdown("#### Select an image 🖼️")
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#st.markdown("""**Select an image that you wish to run the Object Detection model on.**""")
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image_ = None
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select_image_box = st.radio(
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"**Select the image you wish to run the model on**",
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["Choose an existing image", "Load your own image"],
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index=None,)# #label_visibility="collapsed")
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if select_image_box == "Choose an existing image":
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fashion_images_path = r"data/dior_show"
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list_images = os.listdir(fashion_images_path)
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image_ = st.selectbox("", list_images, label_visibility="collapsed")
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st.warning("""**Note**: The model tends to perform better with images of people/clothing items facing forward.
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Choose this type of image if you want optimal results.""")
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st.warning("""**Note:** The model was trained to detect clothing items on a single person.
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If your image contains more than one person, the model won't detect the items of the other persons.""")
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if image_ is not None:
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st.image(Image.open(image_), width=300)
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dict_cats = dict(zip(np.arange(len(cats)), cats))
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st.markdown("#### Choose the elements you want to detect 👉")
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# Select one or more elements to detect
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container = st.container()
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############## SELECT A THRESHOLD ###############
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st.markdown("#### Define a threshold for predictions 🔎")
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st.markdown("""Object detection models assign to each element detected a **probability score**. <br>
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This score represents the model's belief in the accuracy of its prediction for a specific object.
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""", unsafe_allow_html=True)
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st.warning("**Note:** Objects that are assigned a lower score than the chosen threshold will be ignored in the final results.")
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_, col, _ = st.columns([0.2,0.6,0.2])
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with col:
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st.image("images/probability_od.png", caption="Example of object detection with probability scores")
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st.markdown(" ")
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st.markdown("**Select a threshold** ")
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# st.warning("""**Note**: The threshold helps you decide how confident you want your model to be with its predictions.
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# Elements that are identified with a lower probability than the given threshold will be ignored in the final results.""")
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threshold = st.slider('**Select a threshold**', min_value=0.5, step=0.05, max_value=1.0, value=0.75, label_visibility="collapsed")
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if threshold < 0.6:
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st.error("""**Warning**: Selecting a low threshold (below 0.6) could lead the model to make errors and detect too many objects.""")
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st.write("You've selected a threshold at", threshold)
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st.markdown(" ")
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image = fix_channels(ToTensor()(image))
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## LOAD OBJECT DETECTION MODEL
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FEATURE_EXTRACTOR_PATH = "hustvl/yolos-small"
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MODEL_PATH = "valentinafeve/yolos-fashionpedia"
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feature_extractor, model = load_model(FEATURE_EXTRACTOR_PATH, MODEL_PATH)
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# feature_extractor = YolosFeatureExtractor.from_pretrained('hustvl/yolos-small')
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# model = YolosForObjectDetection.from_pretrained(MODEL)
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# RUN MODEL ON IMAGE
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inputs = feature_extractor(images=image, return_tensors="pt")
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outputs = model(**inputs)
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probas, keep = return_probas(outputs, threshold)
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st.markdown("#### See the results ☑️")
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# PLOT BOUNDING BOX AND BARS/PROBA
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col1, col2 = st.columns(2)
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with col1:
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#st.markdown("**Bounding box results**")
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bboxes_scaled = rescale_bboxes(outputs.pred_boxes[0, keep].cpu(), image.size)
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colors_used = plot_results(image, probas[keep], bboxes_scaled)
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with col2:
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#st.markdown("**Probability scores**")
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if not any(keep.tolist()):
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st.error("""No objects were detected on the image.
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Decrease your threshold or choose differents items to detect.""")
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else:
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visualize_probas(probas, threshold, colors_used)
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else:
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st.error("You must select an **image**, **elements to detect** and a **threshold** to run the model !")
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