import gradio as gr from transformers import AutoProcessor, AutoModelForCausalLM from PIL import Image import requests import copy import matplotlib.pyplot as plt import matplotlib.patches as patches import random import numpy as np import subprocess subprocess.run('pip install flash-attn --no-build-isolation', env={'FLASH_ATTENTION_SKIP_CUDA_BUILD': "TRUE"}, shell=True) model_id = 'microsoft/Florence-2-large' model = AutoModelForCausalLM.from_pretrained(model_id, trust_remote_code=True).eval() processor = AutoProcessor.from_pretrained(model_id, trust_remote_code=True) def run_example(task_prompt, image, text_input=None): if text_input is None: prompt = task_prompt else: prompt = task_prompt + text_input inputs = processor(text=prompt, images=image, return_tensors="pt") generated_ids = model.generate( input_ids=inputs["input_ids"], pixel_values=inputs["pixel_values"], max_new_tokens=1024, early_stopping=False, do_sample=False, num_beams=3, ) generated_text = processor.batch_decode(generated_ids, skip_special_tokens=False)[0] parsed_answer = processor.post_process_generation( generated_text, task=task_prompt, image_size=(image.width, image.height) ) return parsed_answer def plot_bbox(image, data): fig, ax = plt.subplots() ax.imshow(image) for bbox, label in zip(data['bboxes'], data['labels']): x1, y1, x2, y2 = bbox rect = patches.Rectangle((x1, y1), x2-x1, y2-y1, linewidth=1, edgecolor='r', facecolor='none') ax.add_patch(rect) plt.text(x1, y1, label, color='white', fontsize=8, bbox=dict(facecolor='red', alpha=0.5)) ax.axis('off') return fig def draw_polygons(image, prediction, fill_mask=False): draw = ImageDraw.Draw(image) scale = 1 for polygons, label in zip(prediction['polygons'], prediction['labels']): color = random.choice(colormap) fill_color = random.choice(colormap) if fill_mask else None for _polygon in polygons: _polygon = np.array(_polygon).reshape(-1, 2) if len(_polygon) < 3: print('Invalid polygon:', _polygon) continue _polygon = (_polygon * scale).reshape(-1).tolist() if fill_mask: draw.polygon(_polygon, outline=color, fill=fill_color) else: draw.polygon(_polygon, outline=color) draw.text((_polygon[0] + 8, _polygon[1] + 2), label, fill=color) return image def convert_to_od_format(data): bboxes = data.get('bboxes', []) labels = data.get('bboxes_labels', []) od_results = { 'bboxes': bboxes, 'labels': labels } return od_results def draw_ocr_bboxes(image, prediction): scale = 1 draw = ImageDraw.Draw(image) bboxes, labels = prediction['quad_boxes'], prediction['labels'] for box, label in zip(bboxes, labels): color = random.choice(colormap) new_box = (np.array(box) * scale).tolist() draw.polygon(new_box, width=3, outline=color) draw.text((new_box[0]+8, new_box[1]+2), "{}".format(label), align="right", fill=color) return image def process_image(image, task_prompt, text_input=None): if task_prompt == '': result = run_example(task_prompt, image) return result elif task_prompt == '': result = run_example(task_prompt, image) return result elif task_prompt == '': result = run_example(task_prompt, image) return result elif task_prompt == '': results = run_example(task_prompt, image) fig = plot_bbox(image, results['']) return fig elif task_prompt == '': results = run_example(task_prompt, image) fig = plot_bbox(image, results['']) return fig elif task_prompt == '': results = run_example(task_prompt, image) fig = plot_bbox(image, results['']) return fig elif task_prompt == '': results = run_example(task_prompt, image, text_input) fig = plot_bbox(image, results['']) return fig elif task_prompt == '': results = run_example(task_prompt, image, text_input) output_image = copy.deepcopy(image) output_image = draw_polygons(output_image, results[''], fill_mask=True) return output_image elif task_prompt == '': results = run_example(task_prompt, image, text_input) output_image = copy.deepcopy(image) output_image = draw_polygons(output_image, results[''], fill_mask=True) return output_image elif task_prompt == '': results = run_example(task_prompt, image, text_input) bbox_results = convert_to_od_format(results['']) fig = plot_bbox(image, bbox_results) return fig elif task_prompt == '': results = run_example(task_prompt, image, text_input) return results elif task_prompt == '': results = run_example(task_prompt, image, text_input) return results elif task_prompt == '': result = run_example(task_prompt, image) return result elif task_prompt == '': results = run_example(task_prompt, image) output_image = copy.deepcopy(image) output_image = draw_ocr_bboxes(output_image, results['']) return output_image css = """ #output { height: 500px; overflow: auto; border: 1px solid #ccc; } """ with gr.Blocks(css=css) as demo: gr.HTML("

Florence-2 Demo

") with gr.Tab(label="Florence-2 Image Captioning"): with gr.Row(): with gr.Column(): input_img = gr.Image(label="Input Picture") task_prompt = gr.Dropdown(choices=[ '', '', '', '', '', '', '', '', '', '', '', '', '', '' ], label="Task Prompt") text_input = gr.Textbox(label="Text Input (optional)") submit_btn = gr.Button(value="Submit") with gr.Column(): output_text = gr.Textbox(label="Output Text") output_img = gr.Image(label="Output Image") gr.Examples( examples=[ ["image1.jpg", ''], ["image1.jpg", ''], ["image1.jpg", ''] ], inputs=[input_img, task_prompt], outputs=[output_text, output_img], fn=process_image, cache_examples=True, label='Try examples' ) submit_btn.click(process_image, [input_img, task_prompt, text_input], [output_text, output_img]) demo.launch(debug=True)