Create app.py

app.py

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+from transformers import AutoFeatureExtractor, YolosForObjectDetection
+import gradio as gr
+from PIL import Image
+import torch
+import matplotlib.pyplot as plt
+import io
+import numpy as np
+
+
+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]]
+
+
+def get_class_list_from_input(classes_string: str):
+    if classes_string == "":
+        return []
+    classes_list = classes_string.split(",")
+    classes_list = [x.strip() for x in classes_list]
+    return classes_list
+
+def infer(img, model_name: str, prob_threshold: int, classes_to_show = str):
+    feature_extractor = AutoFeatureExtractor.from_pretrained(f"hustvl/{model_name}")
+    model = YolosForObjectDetection.from_pretrained(f"hustvl/{model_name}")
+
+    img = Image.fromarray(img)
+    
+    pixel_values = feature_extractor(img, return_tensors="pt").pixel_values
+
+    with torch.no_grad():
+        outputs = model(pixel_values, output_attentions=True)
+    
+    probas = outputs.logits.softmax(-1)[0, :, :-1]
+    keep = probas.max(-1).values > prob_threshold
+
+    target_sizes = torch.tensor(img.size[::-1]).unsqueeze(0)
+    postprocessed_outputs = feature_extractor.post_process(outputs, target_sizes)
+    bboxes_scaled = postprocessed_outputs[0]['boxes']
+    
+    classes_list = get_class_list_from_input(classes_to_show)
+    res_img = plot_results(img, probas[keep], bboxes_scaled[keep], model, classes_list)
+    
+    return res_img
+
+def plot_results(pil_img, prob, boxes, model, classes_list):
+    plt.figure(figsize=(16,10))
+    plt.imshow(pil_img)
+    ax = plt.gca()
+    colors = COLORS * 100
+    for p, (xmin, ymin, xmax, ymax), c in zip(prob, boxes.tolist(), colors):
+        cl = p.argmax()
+        object_class = model.config.id2label[cl.item()]
+        
+        if len(classes_list) > 0 :
+            if object_class not in classes_list:
+                continue
+            
+        ax.add_patch(plt.Rectangle((xmin, ymin), xmax - xmin, ymax - ymin,
+                                fill=False, color=c, linewidth=3))
+        text = f'{object_class}: {p[cl]:0.2f}'
+        ax.text(xmin, ymin, text, fontsize=15,
+                bbox=dict(facecolor='yellow', alpha=0.5))
+    plt.axis('off')
+    return fig2img(plt.gcf())
+    
+def fig2img(fig):
+    buf = io.BytesIO()
+    fig.savefig(buf)
+    buf.seek(0)
+    img = Image.open(buf)
+    return img
+
+description = """Object Detection with YOLOS. Choose https://github.com/amikelive/coco-labels/blob/master/coco-labels-2014_2017.txtyour model and you're good to go.
+You can adapt the minimum probability threshold with the slider.
+Additionally you can restrict the classes that will be shown by putting in a comma separated list of 
+[COCO classes](https://github.com/amikelive/coco-labels/blob/master/coco-labels-2014_2017.txt). 
+Leaving the field empty will show all classes"""
+
+image_in = gr.components.Image()
+image_out = gr.components.Image()
+model_choice = gr.components.Dropdown(["yolos-tiny", "yolos-small", "yolos-base", "yolos-small-300", "yolos-small-dwr"], value="yolos-small", label="YOLOS Model")
+prob_threshold_slider = gr.components.Slider(minimum=0, maximum=1.0, step=0.01, value=0.9, label="Probability Threshold")
+classes_to_show = gr.components.Textbox(placeholder="e.g. person, boat", label="Classes to use (empty means all classes)")
+
+Iface = gr.Interface(
+    fn=infer,
+    inputs=[image_in,model_choice, prob_threshold_slider, classes_to_show],
+    outputs=image_out,
+    #examples=[["examples/10_People_Marching_People_Marching_2_120.jpg"], ["examples/12_Group_Group_12_Group_Group_12_26.jpg"], ["examples/43_Row_Boat_Canoe_43_247.jpg"]],
+    title="Object Detection with YOLOS",
+    description=description,
+).launch()