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Runtime error
Runtime error
byterocker20
commited on
Commit
•
b538d20
1
Parent(s):
3aa769d
Add application file
Browse files- app.py +290 -0
- image1.jpg +0 -0
- image2.jpg +0 -0
- requirements.txt +2 -0
app.py
ADDED
@@ -0,0 +1,290 @@
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1 |
+
import gradio as gr
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2 |
+
from transformers import AutoProcessor, AutoModelForCausalLM
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3 |
+
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4 |
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import requests
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5 |
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import copy
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6 |
+
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7 |
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from PIL import Image, ImageDraw, ImageFont
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8 |
+
import io
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9 |
+
import matplotlib.pyplot as plt
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10 |
+
import matplotlib.patches as patches
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11 |
+
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12 |
+
import random
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13 |
+
import numpy as np
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14 |
+
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15 |
+
import os
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16 |
+
import subprocess
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17 |
+
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18 |
+
from unittest.mock import patch
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19 |
+
from transformers.dynamic_module_utils import get_imports
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20 |
+
def fixed_get_imports(filename: str | os.PathLike) -> list[str]:
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21 |
+
if not str(filename).endswith("modeling_florence2.py"):
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22 |
+
return get_imports(filename)
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23 |
+
imports = get_imports(filename)
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24 |
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imports.remove("flash_attn")
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25 |
+
return imports
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26 |
+
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27 |
+
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28 |
+
models = {
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'microsoft/Florence-2-base-ft': AutoModelForCausalLM.from_pretrained('microsoft/Florence-2-base-ft', trust_remote_code=True, device_map='cpu').eval(),
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30 |
+
'microsoft/Florence-2-base': AutoModelForCausalLM.from_pretrained('microsoft/Florence-2-base', trust_remote_code=True, device_map='cpu').eval(),
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31 |
+
}
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32 |
+
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33 |
+
processors = {
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34 |
+
'microsoft/Florence-2-base-ft': AutoProcessor.from_pretrained('microsoft/Florence-2-base-ft', trust_remote_code=True),
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35 |
+
'microsoft/Florence-2-base': AutoProcessor.from_pretrained('microsoft/Florence-2-base', trust_remote_code=True),
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36 |
+
}
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37 |
+
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38 |
+
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39 |
+
DESCRIPTION = "# [Florence-2 Demo](https://huggingface.co/microsoft/Florence-2-base)"
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40 |
+
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41 |
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colormap = ['blue','orange','green','purple','brown','pink','gray','olive','cyan','red',
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42 |
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'lime','indigo','violet','aqua','magenta','coral','gold','tan','skyblue']
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43 |
+
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44 |
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def fig_to_pil(fig):
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45 |
+
buf = io.BytesIO()
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46 |
+
fig.savefig(buf, format='png')
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47 |
+
buf.seek(0)
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48 |
+
return Image.open(buf)
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49 |
+
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50 |
+
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51 |
+
def run_example(task_prompt, image, text_input=None, model_id='microsoft/Florence-2-large'):
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52 |
+
with patch("transformers.dynamic_module_utils.get_imports", fixed_get_imports):
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53 |
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model = models[model_id]
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54 |
+
processor = processors[model_id]
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55 |
+
if text_input is None:
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56 |
+
prompt = task_prompt
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57 |
+
else:
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58 |
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prompt = task_prompt + text_input
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59 |
+
inputs = processor(text=prompt, images=image, return_tensors="pt").to("cuda")
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60 |
+
generated_ids = model.generate(
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61 |
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input_ids=inputs["input_ids"],
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62 |
+
pixel_values=inputs["pixel_values"],
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63 |
+
max_new_tokens=1024,
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64 |
+
early_stopping=False,
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65 |
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do_sample=False,
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66 |
+
num_beams=3,
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67 |
+
)
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68 |
+
generated_text = processor.batch_decode(generated_ids, skip_special_tokens=False)[0]
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69 |
+
parsed_answer = processor.post_process_generation(
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70 |
+
generated_text,
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71 |
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task=task_prompt,
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72 |
+
image_size=(image.width, image.height)
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73 |
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)
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74 |
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return parsed_answer
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75 |
+
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76 |
+
def plot_bbox(image, data):
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77 |
+
fig, ax = plt.subplots()
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78 |
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ax.imshow(image)
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79 |
+
for bbox, label in zip(data['bboxes'], data['labels']):
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80 |
+
x1, y1, x2, y2 = bbox
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81 |
+
rect = patches.Rectangle((x1, y1), x2-x1, y2-y1, linewidth=1, edgecolor='r', facecolor='none')
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82 |
+
ax.add_patch(rect)
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83 |
+
plt.text(x1, y1, label, color='white', fontsize=8, bbox=dict(facecolor='red', alpha=0.5))
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84 |
+
ax.axis('off')
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85 |
+
return fig
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86 |
+
|
87 |
+
def draw_polygons(image, prediction, fill_mask=False):
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88 |
+
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89 |
+
draw = ImageDraw.Draw(image)
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90 |
+
scale = 1
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91 |
+
for polygons, label in zip(prediction['polygons'], prediction['labels']):
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92 |
+
color = random.choice(colormap)
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93 |
+
fill_color = random.choice(colormap) if fill_mask else None
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94 |
+
for _polygon in polygons:
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95 |
+
_polygon = np.array(_polygon).reshape(-1, 2)
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96 |
+
if len(_polygon) < 3:
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97 |
+
print('Invalid polygon:', _polygon)
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98 |
+
continue
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99 |
+
_polygon = (_polygon * scale).reshape(-1).tolist()
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100 |
+
if fill_mask:
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101 |
+
draw.polygon(_polygon, outline=color, fill=fill_color)
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102 |
+
else:
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103 |
+
draw.polygon(_polygon, outline=color)
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104 |
+
draw.text((_polygon[0] + 8, _polygon[1] + 2), label, fill=color)
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105 |
+
return image
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106 |
+
|
107 |
+
def convert_to_od_format(data):
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108 |
+
bboxes = data.get('bboxes', [])
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109 |
+
labels = data.get('bboxes_labels', [])
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110 |
+
od_results = {
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111 |
+
'bboxes': bboxes,
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112 |
+
'labels': labels
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113 |
+
}
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114 |
+
return od_results
|
115 |
+
|
116 |
+
def draw_ocr_bboxes(image, prediction):
|
117 |
+
scale = 1
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118 |
+
draw = ImageDraw.Draw(image)
|
119 |
+
bboxes, labels = prediction['quad_boxes'], prediction['labels']
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120 |
+
for box, label in zip(bboxes, labels):
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121 |
+
color = random.choice(colormap)
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122 |
+
new_box = (np.array(box) * scale).tolist()
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123 |
+
draw.polygon(new_box, width=3, outline=color)
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124 |
+
draw.text((new_box[0]+8, new_box[1]+2),
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125 |
+
"{}".format(label),
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126 |
+
align="right",
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127 |
+
fill=color)
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128 |
+
return image
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129 |
+
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130 |
+
def process_image(image, task_prompt, text_input=None, model_id='microsoft/Florence-2-large'):
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131 |
+
image = Image.fromarray(image) # Convert NumPy array to PIL Image
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132 |
+
if task_prompt == 'Caption':
|
133 |
+
task_prompt = '<CAPTION>'
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134 |
+
results = run_example(task_prompt, image, model_id=model_id)
|
135 |
+
return results, None
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136 |
+
elif task_prompt == 'Detailed Caption':
|
137 |
+
task_prompt = '<DETAILED_CAPTION>'
|
138 |
+
results = run_example(task_prompt, image, model_id=model_id)
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139 |
+
return results, None
|
140 |
+
elif task_prompt == 'More Detailed Caption':
|
141 |
+
task_prompt = '<MORE_DETAILED_CAPTION>'
|
142 |
+
results = run_example(task_prompt, image, model_id=model_id)
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143 |
+
return results, None
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144 |
+
elif task_prompt == 'Caption + Grounding':
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145 |
+
task_prompt = '<CAPTION>'
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146 |
+
results = run_example(task_prompt, image, model_id=model_id)
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147 |
+
text_input = results[task_prompt]
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148 |
+
task_prompt = '<CAPTION_TO_PHRASE_GROUNDING>'
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149 |
+
results = run_example(task_prompt, image, text_input, model_id)
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150 |
+
results['<CAPTION>'] = text_input
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151 |
+
fig = plot_bbox(image, results['<CAPTION_TO_PHRASE_GROUNDING>'])
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152 |
+
return results, fig_to_pil(fig)
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153 |
+
elif task_prompt == 'Detailed Caption + Grounding':
|
154 |
+
task_prompt = '<DETAILED_CAPTION>'
|
155 |
+
results = run_example(task_prompt, image, model_id=model_id)
|
156 |
+
text_input = results[task_prompt]
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157 |
+
task_prompt = '<CAPTION_TO_PHRASE_GROUNDING>'
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158 |
+
results = run_example(task_prompt, image, text_input, model_id)
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159 |
+
results['<DETAILED_CAPTION>'] = text_input
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160 |
+
fig = plot_bbox(image, results['<CAPTION_TO_PHRASE_GROUNDING>'])
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161 |
+
return results, fig_to_pil(fig)
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162 |
+
elif task_prompt == 'More Detailed Caption + Grounding':
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163 |
+
task_prompt = '<MORE_DETAILED_CAPTION>'
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164 |
+
results = run_example(task_prompt, image, model_id=model_id)
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165 |
+
text_input = results[task_prompt]
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166 |
+
task_prompt = '<CAPTION_TO_PHRASE_GROUNDING>'
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167 |
+
results = run_example(task_prompt, image, text_input, model_id)
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168 |
+
results['<MORE_DETAILED_CAPTION>'] = text_input
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169 |
+
fig = plot_bbox(image, results['<CAPTION_TO_PHRASE_GROUNDING>'])
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170 |
+
return results, fig_to_pil(fig)
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171 |
+
elif task_prompt == 'Object Detection':
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172 |
+
task_prompt = '<OD>'
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173 |
+
results = run_example(task_prompt, image, model_id=model_id)
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174 |
+
fig = plot_bbox(image, results['<OD>'])
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175 |
+
return results, fig_to_pil(fig)
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176 |
+
elif task_prompt == 'Dense Region Caption':
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177 |
+
task_prompt = '<DENSE_REGION_CAPTION>'
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178 |
+
results = run_example(task_prompt, image, model_id=model_id)
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179 |
+
fig = plot_bbox(image, results['<DENSE_REGION_CAPTION>'])
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180 |
+
return results, fig_to_pil(fig)
|
181 |
+
elif task_prompt == 'Region Proposal':
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182 |
+
task_prompt = '<REGION_PROPOSAL>'
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183 |
+
results = run_example(task_prompt, image, model_id=model_id)
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184 |
+
fig = plot_bbox(image, results['<REGION_PROPOSAL>'])
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185 |
+
return results, fig_to_pil(fig)
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186 |
+
elif task_prompt == 'Caption to Phrase Grounding':
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187 |
+
task_prompt = '<CAPTION_TO_PHRASE_GROUNDING>'
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188 |
+
results = run_example(task_prompt, image, text_input, model_id)
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189 |
+
fig = plot_bbox(image, results['<CAPTION_TO_PHRASE_GROUNDING>'])
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190 |
+
return results, fig_to_pil(fig)
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191 |
+
elif task_prompt == 'Referring Expression Segmentation':
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192 |
+
task_prompt = '<REFERRING_EXPRESSION_SEGMENTATION>'
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193 |
+
results = run_example(task_prompt, image, text_input, model_id)
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194 |
+
output_image = copy.deepcopy(image)
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195 |
+
output_image = draw_polygons(output_image, results['<REFERRING_EXPRESSION_SEGMENTATION>'], fill_mask=True)
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196 |
+
return results, output_image
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197 |
+
elif task_prompt == 'Region to Segmentation':
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198 |
+
task_prompt = '<REGION_TO_SEGMENTATION>'
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199 |
+
results = run_example(task_prompt, image, text_input, model_id)
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200 |
+
output_image = copy.deepcopy(image)
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201 |
+
output_image = draw_polygons(output_image, results['<REGION_TO_SEGMENTATION>'], fill_mask=True)
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202 |
+
return results, output_image
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203 |
+
elif task_prompt == 'Open Vocabulary Detection':
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204 |
+
task_prompt = '<OPEN_VOCABULARY_DETECTION>'
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205 |
+
results = run_example(task_prompt, image, text_input, model_id)
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206 |
+
bbox_results = convert_to_od_format(results['<OPEN_VOCABULARY_DETECTION>'])
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207 |
+
fig = plot_bbox(image, bbox_results)
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208 |
+
return results, fig_to_pil(fig)
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209 |
+
elif task_prompt == 'Region to Category':
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210 |
+
task_prompt = '<REGION_TO_CATEGORY>'
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211 |
+
results = run_example(task_prompt, image, text_input, model_id)
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212 |
+
return results, None
|
213 |
+
elif task_prompt == 'Region to Description':
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214 |
+
task_prompt = '<REGION_TO_DESCRIPTION>'
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215 |
+
results = run_example(task_prompt, image, text_input, model_id)
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216 |
+
return results, None
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217 |
+
elif task_prompt == 'OCR':
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218 |
+
task_prompt = '<OCR>'
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219 |
+
results = run_example(task_prompt, image, model_id=model_id)
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220 |
+
return results, None
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221 |
+
elif task_prompt == 'OCR with Region':
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222 |
+
task_prompt = '<OCR_WITH_REGION>'
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223 |
+
results = run_example(task_prompt, image, model_id=model_id)
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224 |
+
output_image = copy.deepcopy(image)
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225 |
+
output_image = draw_ocr_bboxes(output_image, results['<OCR_WITH_REGION>'])
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226 |
+
return results, output_image
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227 |
+
else:
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228 |
+
return "", None # Return empty string and None for unknown task prompts
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229 |
+
|
230 |
+
css = """
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231 |
+
#output {
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232 |
+
height: 500px;
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233 |
+
overflow: auto;
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234 |
+
border: 1px solid #ccc;
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235 |
+
}
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236 |
+
"""
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237 |
+
|
238 |
+
|
239 |
+
single_task_list =[
|
240 |
+
'Caption', 'Detailed Caption', 'More Detailed Caption', 'Object Detection',
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241 |
+
'Dense Region Caption', 'Region Proposal', 'Caption to Phrase Grounding',
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242 |
+
'Referring Expression Segmentation', 'Region to Segmentation',
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243 |
+
'Open Vocabulary Detection', 'Region to Category', 'Region to Description',
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244 |
+
'OCR', 'OCR with Region'
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245 |
+
]
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246 |
+
|
247 |
+
cascased_task_list =[
|
248 |
+
'Caption + Grounding', 'Detailed Caption + Grounding', 'More Detailed Caption + Grounding'
|
249 |
+
]
|
250 |
+
|
251 |
+
|
252 |
+
def update_task_dropdown(choice):
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253 |
+
if choice == 'Cascased task':
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254 |
+
return gr.Dropdown(choices=cascased_task_list, value='Caption + Grounding')
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255 |
+
else:
|
256 |
+
return gr.Dropdown(choices=single_task_list, value='Caption')
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257 |
+
|
258 |
+
|
259 |
+
|
260 |
+
with gr.Blocks(css=css) as demo:
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261 |
+
gr.Markdown(DESCRIPTION)
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262 |
+
with gr.Tab(label="Florence-2 Image Captioning"):
|
263 |
+
with gr.Row():
|
264 |
+
with gr.Column():
|
265 |
+
input_img = gr.Image(label="Input Picture")
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266 |
+
model_selector = gr.Dropdown(choices=list(models.keys()), label="Model", value='microsoft/Florence-2-large')
|
267 |
+
task_type = gr.Radio(choices=['Single task', 'Cascased task'], label='Task type selector', value='Single task')
|
268 |
+
task_prompt = gr.Dropdown(choices=single_task_list, label="Task Prompt", value="Caption")
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269 |
+
task_type.change(fn=update_task_dropdown, inputs=task_type, outputs=task_prompt)
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270 |
+
text_input = gr.Textbox(label="Text Input (optional)")
|
271 |
+
submit_btn = gr.Button(value="Submit")
|
272 |
+
with gr.Column():
|
273 |
+
output_text = gr.Textbox(label="Output Text")
|
274 |
+
output_img = gr.Image(label="Output Image")
|
275 |
+
|
276 |
+
gr.Examples(
|
277 |
+
examples=[
|
278 |
+
["image1.jpg", 'Object Detection'],
|
279 |
+
["image2.jpg", 'OCR with Region']
|
280 |
+
],
|
281 |
+
inputs=[input_img, task_prompt],
|
282 |
+
outputs=[output_text, output_img],
|
283 |
+
fn=process_image,
|
284 |
+
cache_examples=True,
|
285 |
+
label='Try examples'
|
286 |
+
)
|
287 |
+
|
288 |
+
submit_btn.click(process_image, [input_img, task_prompt, text_input, model_selector], [output_text, output_img])
|
289 |
+
|
290 |
+
demo.launch(debug=True)
|
image1.jpg
ADDED
image2.jpg
ADDED
requirements.txt
ADDED
@@ -0,0 +1,2 @@
|
|
|
|
|
|
|
1 |
+
transformers
|
2 |
+
timm
|