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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()
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