| 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() |
| |
| |
| 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") |
|
|
