Create app.py

app.py

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+import os
+import gradio as gr
+from transformers import pipeline, DetrForObjectDetection, DetrConfig, DetrImageProcessor
+import numpy as np
+import cv2
+from PIL import Image
+def model_is_panoptic(model_name):
+    return "panoptic" in model_name
+def load_model(model_name, threshold):
+    config = DetrConfig.from_pretrained(model_name, threshold=threshold)
+    model = DetrForObjectDetection.from_pretrained(model_name, config=config)
+    image_processor = DetrImageProcessor.from_pretrained(model_name)
+    return pipeline(task='object-detection', model=model, image_processor=image_processor)
+# Initial model with default threshold
+od_pipe = load_model("facebook/detr-resnet-101", 0.25)
+def draw_detections(image, detections, model_name):
+    np_image = np.array(image)
+    np_image = cv2.cvtColor(np_image, cv2.COLOR_RGB2BGR)
+    for detection in detections:
+        if model_is_panoptic(model_name):
+            # Handle segmentations for panoptic models
+            mask = detection['mask']
+            color = np.random.randint(0, 255, size=3)
+            mask = np.round(mask * 255).astype(np.uint8)
+            mask = cv2.resize(mask, (image.width, image.height))
+            mask_image = np.stack([mask]*3, axis=-1)
+            np_image[mask == 255] = np_image[mask == 255] * 0.5 + color * 0.5
+        else:
+            # Handle bounding boxes for standard models
+            score = detection['score']
+            label = detection['label']
+            box = detection['box']
+            x_min, y_min = box['xmin'], box['ymin']
+            x_max, y_max = box['xmax'], box['ymax']
+            cv2.rectangle(np_image, (x_min, y_min), (x_max, y_max), (0, 255, 0), 2)
+            label_text = f'{label} {score:.2f}'
+            cv2.putText(np_image, label_text, (x_min, y_min - 10), cv2.FONT_HERSHEY_SIMPLEX, 1.5, (255, 255, 255), 4)
+    final_image = cv2.cvtColor(np_image, cv2.COLOR_BGR2RGB)
+    final_pil_image = Image.fromarray(final_image)
+    return final_pil_image
+def get_pipeline_prediction(model_name, threshold, pil_image):
+    global od_pipe
+    od_pipe = load_model(model_name, threshold)
+    try:
+        if not isinstance(pil_image, Image.Image):
+            pil_image = Image.fromarray(np.array(pil_image).astype('uint8'), 'RGB')
+        result = od_pipe(pil_image)
+        processed_image = draw_detections(pil_image, result, model_name)
+        description = f'Model used: {model_name}, Detection Threshold: {threshold}'
+        return processed_image, result, description
+    except Exception as e:
+        return pil_image, {"error": str(e)}, "Failed to process image"
+with gr.Blocks() as demo:
+    with gr.Row():
+        with gr.Column():
+            gr.Markdown("## Object Detection")
+            inp_image = gr.Image(label="Upload your image here")
+            model_dropdown = gr.Dropdown(choices=["facebook/detr-resnet-50", "facebook/detr-resnet-50-panoptic", "facebook/detr-resnet-101", "facebook/detr-resnet-101-panoptic"], value="facebook/detr-resnet-101", label="Select Model")
+            threshold_slider = gr.Slider(minimum=0.0, maximum=1.0, step=0.01, value=0.25, label="Detection Threshold")
+            run_button = gr.Button("Detect Objects")
+        with gr.Column():
+            with gr.Tab("Annotated Image"):
+                output_image = gr.Image()
+            with gr.Tab("Detection Results"):
+                output_data = gr.JSON()
+            with gr.Tab("Description"):
+                description_output = gr.Textbox()
+    run_button.click(get_pipeline_prediction, inputs=[model_dropdown, threshold_slider, inp_image], outputs=[output_image, output_data, description_output])
+demo.launch()