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from transformers import AutoModel
import numpy as np
from PIL import Image
import torch
import os
current_dir = os.getcwd()
images = [
os.path.join(current_dir, "test", "1.png"),
os.path.join(current_dir, "test", "1.jpg"),
]
def read_image_as_np_array(image_path):
with open(image_path, "rb") as file:
image = Image.open(file).convert("L").convert("RGB")
image = np.array(image)
return image
images = [read_image_as_np_array(image) for image in images]
model = AutoModel.from_pretrained(
"ragavsachdeva/magi", trust_remote_code=True).cuda()
# model = AutoModel.from_pretrained(
# "./magi", trust_remote_code=True).cuda()
with torch.no_grad():
results = model.predict_detections_and_associations(images)
text_bboxes_for_all_images = [x["texts"] for x in results]
ocr_results = model.predict_ocr(images, text_bboxes_for_all_images)
for i in range(len(images)):
model.visualise_single_image_prediction(
images[i], results[i], filename=f"image_{i}.png")
model.generate_transcript_for_single_image(
results[i], ocr_results[i], filename=f"transcript_{i}.txt")
print("Done")
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