File size: 2,589 Bytes
d31a5e4 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 | import torch
from PIL import Image
from transformers import AutoModelForCausalLM
import dataset4eo as eodata
import json
import numpy as np
import pdb
from prompt_utils import generate_prompt_for_segmentation,\
generate_color_coded_segmentation_map, get_significant_classes,\
get_tableau_colors, resize_and_encode_image
from openai import OpenAI
def get_caption_from_id(dataset, class_names, id):
class_colors = get_tableau_colors() # {'blue': (31, 119, 180), 'orange': (255, 127, 14)}
sample = dataset[id]
label = sample["label"]
# save image for debuging
#rgb = (sample["image"]*255).astype(np.uint8)
#rgb = Image.fromarray(rgb)
#rgb.save(f"img_{id}.png")
current_classes = get_significant_classes(label)
unknownId = 12
current_class_names = {ind:class_names[str(ind)] for ind in current_classes if ind!=unknownId}
color_names = list(class_colors.keys())
current_color_names = {ind:color_names[ind] for ind in current_classes}
prompt = generate_prompt_for_segmentation(current_color_names, current_class_names)
image = generate_color_coded_segmentation_map(label, class_colors)
#print(prompt)
#image.save(f"label_color_{id}.png")
base_img = resize_and_encode_image(image)
messages = [
{"role": "user", "content": [
{"type": "image_url", "image_url": {"url": f"data:image/jpeg;base64,{base_img}"}},
{"type": "text", "text": prompt}
]}
]
chat_completion = openai.chat.completions.create(
model="meta-llama/Llama-3.2-90B-Vision-Instruct",
messages=messages,
)
return chat_completion.choices[0].message.content
if __name__=="__main__":
import tqdm #type: ignore
import json
# load model
openai = OpenAI(
api_key="uTu2kzPb6L08aXsmwwRI462UExeUtTBZ",
base_url="https://api.deepinfra.com/v1/openai",
)
# load data
dataset = eodata.StreamingDataset(input_dir="optimized_flair2_test", num_channels=5, channels_to_select=[0,1,2], shuffle=True, drop_last=True)
meta_data = json.load(open("optimized_flair2_test/metadata.json",'r'))
class_names = meta_data["attributes"]["class"]
data = {}
for id in tqdm.tqdm(range(len(dataset))):
caption = get_caption_from_id(dataset, class_names, id)
data[id] = caption
if id%10==0:
with open(f"rgb_captions_{id}.json", "w") as json_file:
json.dump(data, json_file)
# Write to JSON file
with open("rgb_captions.json", "w") as json_file:
json.dump(data, json_file)
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