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---
# For reference on model card metadata, see the spec: https://github.com/huggingface/hub-docs/blob/main/modelcard.md?plain=1
# Doc / guide: https://huggingface.co/docs/hub/model-cards
{}
---
# Kosmos-2: Grounding Multimodal Large Language Models to the World
<a href="https://huggingface.co/ydshieh/kosmos-2-patch14-224/resolve/main/annotated_snowman.jpg" target="_blank"><figure><img src="https://huggingface.co/ydshieh/kosmos-2-patch14-224/resolve/main/annotated_snowman.jpg" width="384"><figcaption><b>[An image of a snowman warming himself by a fire.]</b></figcaption></figure></a>
This Hub repository contains a HuggingFace's `transformers` implementation of [the original Kosmos-2 model](https://github.com/microsoft/unilm/tree/master/kosmos-2) from Microsoft.
## How to Get Started with the Model
Use the code below to get started with the model.
```python
import requests
from PIL import Image
from transformers import AutoProcessor, AutoModelForVision2Seq
model = AutoModelForVision2Seq.from_pretrained("ydshieh/kosmos-2-patch14-224", trust_remote_code=True)
processor = AutoProcessor.from_pretrained("ydshieh/kosmos-2-patch14-224", trust_remote_code=True)
prompt = "<grounding>An image of"
url = "https://huggingface.co/ydshieh/kosmos-2-patch14-224/resolve/main/snowman.jpg"
image = Image.open(requests.get(url, stream=True).raw)
# The original Kosmos-2 demo saves the image first then reload it. For some images, this will give slightly different image input and change the generation outputs.
# Uncomment the following 2 lines if you want to match the original demo's outputs.
# (One example is the `two_dogs.jpg` from the demo)
# image.save("new_image.jpg")
# image = Image.open("new_image.jpg")
inputs = processor(text=prompt, images=image, return_tensors="pt")
generated_ids = model.generate(
pixel_values=inputs["pixel_values"],
input_ids=inputs["input_ids"][:, :-1],
attention_mask=inputs["attention_mask"][:, :-1],
img_features=None,
img_attn_mask=inputs["img_attn_mask"][:, :-1],
use_cache=True,
max_new_tokens=64,
)
generated_text = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
# Specify `cleanup_and_extract=False` in order to see the raw model generation.
processed_text = processor.post_process_generation(generated_text, cleanup_and_extract=False)
print(processed_text)
# `<grounding> An image of<phrase> a snowman</phrase><object><patch_index_0044><patch_index_0863></object> warming himself by<phrase> a fire</phrase><object><patch_index_0005><patch_index_0911></object>.`
# By default, the generated text is cleanup and the entities are extracted.
processed_text, entities = processor.post_process_generation(generated_text)
print(processed_text)
# `An image of a snowman warming himself by a fire.`
print(entities)
# `[('a snowman', (12, 21), [(0.390625, 0.046875, 0.984375, 0.828125)]), ('a fire', (41, 47), [(0.171875, 0.015625, 0.484375, 0.890625)])]`
```
## Draw the bounding bboxes of the entities on the image
Once you have the `entities`, you can use the following helper function to draw their bounding bboxes on the image:
```python
import cv2
import numpy as np
import os
import requests
import torch
import torchvision.transforms as T
from PIL import Image
def is_overlapping(rect1, rect2):
x1, y1, x2, y2 = rect1
x3, y3, x4, y4 = rect2
return not (x2 < x3 or x1 > x4 or y2 < y3 or y1 > y4)
def draw_entity_boxes_on_image(image, entities, show=False, save_path=None):
"""_summary_
Args:
image (_type_): image or image path
collect_entity_location (_type_): _description_
"""
if isinstance(image, Image.Image):
image_h = image.height
image_w = image.width
image = np.array(image)[:, :, [2, 1, 0]]
elif isinstance(image, str):
if os.path.exists(image):
pil_img = Image.open(image).convert("RGB")
image = np.array(pil_img)[:, :, [2, 1, 0]]
image_h = pil_img.height
image_w = pil_img.width
else:
raise ValueError(f"invaild image path, {image}")
elif isinstance(image, torch.Tensor):
# pdb.set_trace()
image_tensor = image.cpu()
reverse_norm_mean = torch.tensor([0.48145466, 0.4578275, 0.40821073])[:, None, None]
reverse_norm_std = torch.tensor([0.26862954, 0.26130258, 0.27577711])[:, None, None]
image_tensor = image_tensor * reverse_norm_std + reverse_norm_mean
pil_img = T.ToPILImage()(image_tensor)
image_h = pil_img.height
image_w = pil_img.width
image = np.array(pil_img)[:, :, [2, 1, 0]]
else:
raise ValueError(f"invaild image format, {type(image)} for {image}")
if len(entities) == 0:
return image
new_image = image.copy()
previous_bboxes = []
# size of text
text_size = 1
# thickness of text
text_line = 1 # int(max(1 * min(image_h, image_w) / 512, 1))
box_line = 3
(c_width, text_height), _ = cv2.getTextSize("F", cv2.FONT_HERSHEY_COMPLEX, text_size, text_line)
base_height = int(text_height * 0.675)
text_offset_original = text_height - base_height
text_spaces = 3
for entity_name, (start, end), bboxes in entities:
for (x1_norm, y1_norm, x2_norm, y2_norm) in bboxes:
orig_x1, orig_y1, orig_x2, orig_y2 = int(x1_norm * image_w), int(y1_norm * image_h), int(x2_norm * image_w), int(y2_norm * image_h)
# draw bbox
# random color
color = tuple(np.random.randint(0, 255, size=3).tolist())
new_image = cv2.rectangle(new_image, (orig_x1, orig_y1), (orig_x2, orig_y2), color, box_line)
l_o, r_o = box_line // 2 + box_line % 2, box_line // 2 + box_line % 2 + 1
x1 = orig_x1 - l_o
y1 = orig_y1 - l_o
if y1 < text_height + text_offset_original + 2 * text_spaces:
y1 = orig_y1 + r_o + text_height + text_offset_original + 2 * text_spaces
x1 = orig_x1 + r_o
# add text background
(text_width, text_height), _ = cv2.getTextSize(f" {entity_name}", cv2.FONT_HERSHEY_COMPLEX, text_size, text_line)
text_bg_x1, text_bg_y1, text_bg_x2, text_bg_y2 = x1, y1 - (text_height + text_offset_original + 2 * text_spaces), x1 + text_width, y1
for prev_bbox in previous_bboxes:
while is_overlapping((text_bg_x1, text_bg_y1, text_bg_x2, text_bg_y2), prev_bbox):
text_bg_y1 += (text_height + text_offset_original + 2 * text_spaces)
text_bg_y2 += (text_height + text_offset_original + 2 * text_spaces)
y1 += (text_height + text_offset_original + 2 * text_spaces)
if text_bg_y2 >= image_h:
text_bg_y1 = max(0, image_h - (text_height + text_offset_original + 2 * text_spaces))
text_bg_y2 = image_h
y1 = image_h
break
alpha = 0.5
for i in range(text_bg_y1, text_bg_y2):
for j in range(text_bg_x1, text_bg_x2):
if i < image_h and j < image_w:
if j < text_bg_x1 + 1.35 * c_width:
# original color
bg_color = color
else:
# white
bg_color = [255, 255, 255]
new_image[i, j] = (alpha * new_image[i, j] + (1 - alpha) * np.array(bg_color)).astype(np.uint8)
cv2.putText(
new_image, f" {entity_name}", (x1, y1 - text_offset_original - 1 * text_spaces), cv2.FONT_HERSHEY_COMPLEX, text_size, (0, 0, 0), text_line, cv2.LINE_AA
)
# previous_locations.append((x1, y1))
previous_bboxes.append((text_bg_x1, text_bg_y1, text_bg_x2, text_bg_y2))
pil_image = Image.fromarray(new_image[:, :, [2, 1, 0]])
if save_path:
pil_image.save(save_path)
if show:
pil_image.show()
return new_image
# (The same image from the previous code example)
url = "https://huggingface.co/ydshieh/kosmos-2-patch14-224/resolve/main/snowman.jpg"
image = Image.open(requests.get(url, stream=True).raw)
# From the previous code example
entities = [('a snowman', (12, 21), [(0.390625, 0.046875, 0.984375, 0.828125)]), ('a fire', (41, 47), [(0.171875, 0.015625, 0.484375, 0.890625)])]
# Draw the bounding bboxes
draw_entity_boxes_on_image(image, entities, show=True)
```
Here is the annotated image:
<a href="https://huggingface.co/ydshieh/kosmos-2-patch14-224/resolve/main/annotated_snowman.jpg" target="_blank"><img src="https://huggingface.co/ydshieh/kosmos-2-patch14-224/resolve/main/annotated_snowman.jpg" width="500"></a>
## Running the Flask Server
_flask_kosmos2.py_ shows the implementation of a Flask server for the model.
It allowes the model to be approached as a REST API.
After starting the server. You can send a POST request to `http://localhost:8005/process_prompt` with the following form data:
- `prompt`: For example `<grounding> an image of`
- `image`: The image file as binary data
This in turn will produce a reply with the following JSON format:
- `message`: The Kosmos-2 generated text
- `entities`: The extracted entities
An easy way to test this is through an application like Postman. Make sure the image field is set to `File`.
```python
from PIL import Image
from transformers import AutoProcessor, AutoModelForVision2Seq
from flask import Flask, request, jsonify
import json
app = Flask(__name__)
model = AutoModelForVision2Seq.from_pretrained("ydshieh/kosmos-2-patch14-224", trust_remote_code=True)
processor = AutoProcessor.from_pretrained("ydshieh/kosmos-2-patch14-224", trust_remote_code=True)
@app.route('/process_prompt', methods=['POST'])
def process_prompt():
try:
# Get the uploaded image data from the POST request
uploaded_file = request.files['image']
prompt = request.form.get('prompt')
image = Image.open(uploaded_file.stream)
print(image.size)
inputs = processor(text=prompt, images=image, return_tensors="pt")
generated_ids = model.generate(
pixel_values=inputs["pixel_values"],
input_ids=inputs["input_ids"][:, :-1],
attention_mask=inputs["attention_mask"][:, :-1],
img_features=None,
img_attn_mask=inputs["img_attn_mask"][:, :-1],
use_cache=True,
max_new_tokens=64,
)
generated_text = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
# By default, the generated text is cleanup and the entities are extracted.
processed_text, entities = processor.post_process_generation(generated_text)
parsed_entities = entities_to_json(entities)
print(generated_text)
print(processed_text)
return jsonify({"message": processed_text, 'entities': parsed_entities})
except Exception as e:
return jsonify({"error": str(e)})
def entities_to_json(entities):
result = []
for e in entities:
label = e[0]
box_coords = e[1]
box_size = e[2][0]
entity_result = {
"label": label,
"boundingBoxPosition": {"x": box_coords[0], "y": box_coords[1]},
"boundingBox": {"x_min": box_size[0], "y_min": box_size[1], "x_max": box_size[2], "y_max": box_size[3]}
}
print(entity_result)
result.append(entity_result)
return result
if __name__ == '__main__':
app.run(host='localhost', port=8005)
``` |