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app.py

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+import random
+import requests
+import json
+
+import matplotlib.pyplot as plt
+from PIL import Image, ImageDraw, ImageFont
+
+import gradio as gr
+import torch
+from transformers import AutoProcessor, Qwen2_5_VLForConditionalGeneration
+from qwen_vl_utils import process_vision_info
+from spaces import GPU
+from gradio.themes.citrus import Citrus
+
+# --- Config ---
+model = Qwen2_5_VLForConditionalGeneration.from_pretrained(
+    "Qwen/Qwen2.5-VL-3B-Instruct", torch_dtype="auto", device_map="auto"
+)
+
+min_pixels = 224 * 224
+max_pixels = 512 * 512
+processor = AutoProcessor.from_pretrained("Qwen/Qwen2.5-VL-3B-Instruct", min_pixels=min_pixels, max_pixels=max_pixels)
+
+label2color = {}
+
+def get_color(label, explicit_color=None):
+    if explicit_color:
+        return explicit_color
+    if label not in label2color:
+        label2color[label] = "#" + ''.join(random.choices('0123456789ABCDEF', k=6))
+    return label2color[label]
+
+def create_annotated_image(image, json_data, height, width):
+    try:
+        json_data = json_data.split('```json')[1].split('```')[0]
+        bbox_data = json.loads(json_data)
+    except Exception:
+        return image
+
+    original_width, original_height = image.size
+    x_scale = original_width / width
+    y_scale = original_height / height
+
+    scale_factor = max(original_width, original_height) / 512
+
+    draw_image = image.copy()
+    draw = ImageDraw.Draw(draw_image)
+
+    try:
+        print(1)
+        print('int(12 * scale_factor)', int(12 * scale_factor))
+        font = ImageFont.truetype("arial.ttf", int(12 * scale_factor))
+    except:
+        print(2)
+        font = ImageFont.load_default()
+
+    for item in bbox_data:
+        label = item.get("label", "")
+        color = get_color(label, item.get("color", None))
+
+        if "bbox_2d" in item:
+            bbox = item["bbox_2d"]
+            scaled_bbox = [
+                int(bbox[0] * x_scale),
+                int(bbox[1] * y_scale),
+                int(bbox[2] * x_scale),
+                int(bbox[3] * y_scale)
+            ]
+            draw.rectangle(scaled_bbox, outline=color, width=int(2 * scale_factor))
+            draw.text(
+                (scaled_bbox[0], max(0, scaled_bbox[1] - int(15 * scale_factor))),
+                label,
+                fill=color,
+                font=font
+            )
+
+        if "point_2d" in item:
+            x, y = item["point_2d"]
+            scaled_x = int(x * x_scale)
+            scaled_y = int(y * y_scale)
+            r = int(5 * scale_factor)
+            draw.ellipse((scaled_x - r, scaled_y - r, scaled_x + r, scaled_y + r), fill=color, outline=color)
+            draw.text((scaled_x + int(6 * scale_factor), scaled_y), label, fill=color, font=font)
+
+    return draw_image
+
+@GPU
+def detect(image, prompt):
+    STANDARD_SIZE = (512, 512)
+    image.thumbnail(STANDARD_SIZE)
+    messages = [
+        {
+            "role": "user",
+            "content": [
+                {"type": "image", "image": image},
+                {"type": "text", "text": prompt},
+            ],
+        }
+    ]
+
+    text = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+    image_inputs, video_inputs = process_vision_info(messages)
+    inputs = processor(
+        text=[text],
+        images=image_inputs,
+        videos=video_inputs,
+        padding=True,
+        return_tensors="pt",
+    ).to(model.device)
+
+    generated_ids = model.generate(**inputs, max_new_tokens=1024)
+    generated_ids_trimmed = [
+        out_ids[len(in_ids):] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)
+    ]
+    output_text = processor.batch_decode(
+        generated_ids_trimmed, do_sample=True, skip_special_tokens=True, clean_up_tokenization_spaces=False
+    )[0]
+
+    input_height = inputs['image_grid_thw'][0][1] * 14
+    input_width = inputs['image_grid_thw'][0][2] * 14
+
+    annotated_image = create_annotated_image(image, output_text, input_height, input_width)
+
+    return annotated_image, output_text
+
+css_hide_share = """
+button#gradio-share-link-button-0 {
+    display: none !important;
+}
+"""
+
+# --- Gradio Interface ---
+with gr.Blocks(theme=Citrus(), css=css_hide_share) as demo:
+
+    gr.Markdown("# Object Understanding with Vision-Language Models")
+    gr.Markdown("### Explore object detection, visual grounding, keypoint detection, and/or object counting through natural language prompts.")
+    gr.Markdown("""
+    *Powered by Qwen2.5-VL*  
+    *Inspired by the tutorial [Object Detection and Visual Grounding with Qwen 2.5](https://pyimagesearch.com/2025/06/09/object-detection-and-visual-grounding-with-qwen-2-5/) on PyImageSearch.*
+    """)
+
+
+    with gr.Column():
+        with gr.Row():
+            image_input = gr.Image(label="Upload an image", type="pil", height=500)
+
+            with gr.Column():
+                prompt_input = gr.Textbox(label="Enter your prompt", placeholder="e.g., Detect all red cars in the image")
+                category_input = gr.Textbox(label="Category", interactive=False)
+
+        generate_btn = gr.Button(value="Generate")
+
+        with gr.Row():
+            output_image = gr.Image(type="pil", label="Annotated image", height=500)
+            output_textbox = gr.Textbox(label="Model response", lines=10)
+
+        example_prompts = [
+            ["examples/example_1.jpg", "Detect all objects in the image and return their locations and labels.", "Object Detection"],
+            ["examples/example_2.JPG", "Detect all the individual candies in the image and return their locations and labels.", "Object Detection"],
+            ["examples/example_1.jpg", "Count the number of red cars in the image.", "Object Counting"],
+            ["examples/example_2.JPG", "Count the number of blue candies in the image.", "Object Counting"],
+            ["examples/example_1.jpg", "Identify the red cars in this image, detect their key points and return their positions in the form of points.", "Visual Grounding + Keypoint Detection"],
+            ["examples/example_2.JPG", "Identify the blue candies in this image, detect their key points and return their positions in the form of points.", "Visual Grounding + Keypoint Detection"],
+            ["examples/example_1.jpg", "Detect the red car that is leading in this image and return its location and label.", "Visual Grounding + Object Detection"],
+            ["examples/example_2.JPG", "Detect the blue candy located at the top of the group in this image and return its location and label.", "Visual Grounding + Object Detection"],
+        ]
+
+
+        gr.Markdown("### Examples")
+        gr.Examples(
+            examples=example_prompts,
+            inputs=[image_input, prompt_input, category_input],
+            label="Click an example to populate the input"
+        )
+
+    generate_btn.click(fn=detect, inputs=[image_input, prompt_input], outputs=[output_image, output_textbox])
+
+if __name__ == "__main__":
+    demo.launch()

requirements.txt

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+torch
+transformers
+datasets
+Pillow
+gradio
+accelerate
+qwen-vl-utils
+torchvision
+matplotlib