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import gradio as gr |
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from transformers import AutoModelForCausalLM, AutoProcessor |
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from PIL import Image, ImageDraw |
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import requests |
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import matplotlib.pyplot as plt |
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import matplotlib.patches as patches |
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import numpy as np |
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import random |
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model_id = 'microsoft/Florence-2-large' |
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model = AutoModelForCausalLM.from_pretrained(model_id, trust_remote_code=True).eval() |
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processor = AutoProcessor.from_pretrained(model_id, trust_remote_code=True) |
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def run_example(task_prompt, image, text_input=None): |
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prompt = task_prompt if text_input is None else task_prompt + text_input |
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inputs = processor(text=prompt, images=image, return_tensors="pt") |
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generated_ids = model.generate( |
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input_ids=inputs["input_ids"], |
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pixel_values=inputs["pixel_values"], |
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max_new_tokens=1024, |
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early_stopping=False, |
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do_sample=False, |
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num_beams=3, |
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) |
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generated_text = processor.batch_decode(generated_ids, skip_special_tokens=False)[0] |
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parsed_answer = processor.post_process_generation( |
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generated_text, |
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task=task_prompt, |
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image_size=(image.width, image.height) |
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) |
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return parsed_answer |
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def plot_bbox(image, data): |
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fig, ax = plt.subplots() |
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ax.imshow(image) |
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for bbox, label in zip(data['bboxes'], data['labels']): |
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x1, y1, x2, y2 = bbox |
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rect = patches.Rectangle((x1, y1), x2-x1, y2-y1, linewidth=1, edgecolor='r', facecolor='none') |
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ax.add_patch(rect) |
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plt.text(x1, y1, label, color='white', fontsize=8, bbox=dict(facecolor='red', alpha=0.5)) |
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plt.axis('off') |
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plt.show() |
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def draw_polygons(image, prediction, fill_mask=False): |
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draw = ImageDraw.Draw(image) |
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colormap = ['blue', 'orange', 'green', 'purple', 'brown', 'pink', 'gray', 'olive', 'cyan', 'red'] |
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for polygons, label in zip(prediction['polygons'], prediction['labels']): |
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color = random.choice(colormap) |
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fill_color = color if fill_mask else None |
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for polygon in polygons: |
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draw.polygon(polygon, outline=color, fill=fill_color) |
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draw.text((polygon[0][0], polygon[0][1]), label, fill=color) |
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image.show() |
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def gradio_interface(image, task_prompt, text_input): |
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result = run_example(task_prompt, image, text_input) |
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if task_prompt in ['<OD>', '<OPEN_VOCABULARY_DETECTION>']: |
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plot_bbox(image, result) |
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elif task_prompt in ['<REFERRING_EXPRESSION_SEGMENTATION>', '<REGION_TO_SEGMENTATION>']: |
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draw_polygons(image, result, fill_mask=True) |
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return result |
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with gr.Blocks() as demo: |
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gr.Markdown("## Florence Model Advanced Tasks") |
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with gr.Row(): |
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image_input = gr.Image(type="pil") |
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task_input = gr.Dropdown(label="Select Task", choices=[ |
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'<CAPTION>', '<DETAILED_CAPTION>', '<MORE_DETAILED_CAPTION>', |
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'<OD>', '<DENSE_REGION_CAPTION>', '<REGION_PROPOSAL>', |
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'<CAPTION_TO_PHRASE_GROUNDING>', '<REFERRING_EXPRESSION_SEGMENTATION>', |
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'<REGION_TO_SEGMENTATION>', '<OPEN_VOCABULARY_DETECTION>', |
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'<REGION_TO_CATEGORY>', '<REGION_TO_DESCRIPTION>', '<OCR>', '<OCR_WITH_REGION>' |
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]) |
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text_input = gr.Textbox(label="Optional Text Input", placeholder="Enter text here if required by the task") |
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submit_btn = gr.Button("Run Task") |
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output = gr.Textbox(label="Output") |
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submit_btn.click(fn=gradio_interface, inputs=[image_input, task_input, text_input], outputs=output) |
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demo.launch() |
