Duplicate from nateraw/detr-object-detection

Co-authored-by: Nate Raw <nateraw@users.noreply.huggingface.co>

.gitattributes

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+*.bin.* filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
+*.tar.gz filter=lfs diff=lfs merge=lfs -text
+*.ot filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text

README.md

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+---
+title: Detr Object Detection
+emoji: 🤯
+colorFrom: pink
+colorTo: green
+sdk: streamlit
+app_file: app.py
+pinned: false
+duplicated_from: nateraw/detr-object-detection
+---
+
+# Configuration
+
+`title`: _string_  
+Display title for the Space
+
+`emoji`: _string_  
+Space emoji (emoji-only character allowed)
+
+`colorFrom`: _string_  
+Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
+
+`colorTo`: _string_  
+Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
+
+`sdk`: _string_  
+Can be either `gradio` or `streamlit`
+
+`app_file`: _string_  
+Path to your main application file (which contains either `gradio` or `streamlit` Python code).  
+Path is relative to the root of the repository.
+
+`pinned`: _boolean_  
+Whether the Space stays on top of your list.

app.py

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+import io
+
+import matplotlib.pyplot as plt
+import requests
+import streamlit as st
+import torch
+from PIL import Image
+from transformers import DetrFeatureExtractor, DetrForObjectDetection
+
+# colors for visualization
+COLORS = [
+    [0.000, 0.447, 0.741],
+    [0.850, 0.325, 0.098],
+    [0.929, 0.694, 0.125],
+    [0.494, 0.184, 0.556],
+    [0.466, 0.674, 0.188],
+    [0.301, 0.745, 0.933]
+]
+
+
+@st.cache(allow_output_mutation=True)
+def get_hf_components(model_name_or_path):
+    feature_extractor = DetrFeatureExtractor.from_pretrained(model_name_or_path)
+    model = DetrForObjectDetection.from_pretrained(model_name_or_path)
+    model.eval()
+    return feature_extractor, model
+
+
+@st.cache
+def get_img_from_url(url):
+    return Image.open(requests.get(url, stream=True).raw)
+
+
+def fig2img(fig):
+    buf = io.BytesIO()
+    fig.savefig(buf)
+    buf.seek(0)
+    img = Image.open(buf)
+    return img
+
+
+def visualize_prediction(pil_img, output_dict, threshold=0.7, id2label=None):
+    keep = output_dict["scores"] > threshold
+    boxes = output_dict["boxes"][keep].tolist()
+    scores = output_dict["scores"][keep].tolist()
+    labels = output_dict["labels"][keep].tolist()
+    if id2label is not None:
+        labels = [id2label[x] for x in labels]
+
+    plt.figure(figsize=(16, 10))
+    plt.imshow(pil_img)
+    ax = plt.gca()
+    colors = COLORS * 100
+    for score, (xmin, ymin, xmax, ymax), label, color in zip(scores, boxes, labels, colors):
+        ax.add_patch(plt.Rectangle((xmin, ymin), xmax - xmin, ymax - ymin, fill=False, color=color, linewidth=3))
+        ax.text(xmin, ymin, f"{label}: {score:0.2f}", fontsize=15, bbox=dict(facecolor="yellow", alpha=0.5))
+    plt.axis("off")
+    return fig2img(plt.gcf())
+
+
+def make_prediction(img, feature_extractor, model):
+    inputs = feature_extractor(img, return_tensors="pt")
+    outputs = model(**inputs)
+    img_size = torch.tensor([tuple(reversed(img.size))])
+    processed_outputs = feature_extractor.post_process(outputs, img_size)
+    return processed_outputs[0]
+
+
+def main():
+    option = st.selectbox("Which model should we use?", ("facebook/detr-resnet-50", "facebook/detr-resnet-101"))
+    feature_extractor, model = get_hf_components(option)
+    url = st.text_input("URL to some image", "http://images.cocodataset.org/val2017/000000039769.jpg")
+    img = get_img_from_url(url)
+    processed_outputs = make_prediction(img, feature_extractor, model)
+    threshold = st.slider("Prediction Threshold", 0.0, 1.0, 0.7)
+    viz_img = visualize_prediction(img, processed_outputs, threshold, model.config.id2label)
+    st.image(viz_img)
+
+
+if __name__ == "__main__":
+    main()

requirements.txt

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+streamlit
+https://download.pytorch.org/whl/cpu/torch-1.8.1%2Bcpu-cp38-cp38-linux_x86_64.whl
+git+https://github.com/huggingface/transformers.git
+Pillow
+matplotlib
+timm