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| import re | |
| import gradio as gr | |
| from PIL import Image, ImageDraw | |
| import math | |
| import torch | |
| import html | |
| from transformers import DonutProcessor, VisionEncoderDecoderModel | |
| global model, loaded_revision, processor, device | |
| model = None | |
| previous_revision = None | |
| processor = None | |
| device = None | |
| loaded_revision = None | |
| def load_model(pretrained_revision: str = 'main'): | |
| global model, loaded_revision, processor, device | |
| pretrained_repo_name = 'ivelin/donut-refexp-click' | |
| # revision can be git commit hash, branch or tag | |
| # use 'main' for latest revision | |
| print( | |
| f"Loading model checkpoint from repo: {pretrained_repo_name}, revision: {pretrained_revision}") | |
| if processor is None or loaded_revision is None or loaded_revision != pretrained_revision: | |
| loaded_revision = pretrained_revision | |
| processor = DonutProcessor.from_pretrained( | |
| pretrained_repo_name, revision=pretrained_revision) # , use_auth_token="...") | |
| processor.image_processor.do_align_long_axis = False | |
| # do not manipulate image size and position | |
| processor.image_processor.do_resize = False | |
| processor.image_processor.do_thumbnail = False | |
| processor.image_processor.do_pad = False | |
| # processor.image_processor.do_rescale = False | |
| processor.image_processor.do_normalize = True | |
| print(f'processor image size: {processor.image_processor.size}') | |
| model = VisionEncoderDecoderModel.from_pretrained( | |
| pretrained_repo_name, revision=pretrained_revision) # use_auth_token="...", | |
| print(f'model checkpoint loaded') | |
| device = "cuda" if torch.cuda.is_available() else "cpu" | |
| model.to(device) | |
| def prepare_image_for_encoder(image=None, output_image_size=None): | |
| """ | |
| First, resizes the input image to fill as much as possible of the output image size | |
| while preserving aspect ratio. Positions the resized image at (0,0) and fills | |
| the rest of the gap space in the output image with black(0). | |
| Args: | |
| image: PIL image | |
| output_image_size: (width, height) tuple | |
| """ | |
| assert image is not None | |
| assert output_image_size is not None | |
| img2 = image.copy() | |
| img2.thumbnail(output_image_size) | |
| oimg = Image.new(mode=img2.mode, size=output_image_size, color=0) | |
| oimg.paste(img2, box=(0, 0)) | |
| return oimg | |
| def translate_point_coords_from_out_to_in(point=None, input_image_size=None, output_image_size=None): | |
| """ | |
| Convert relative prediction coordinates from resized encoder tensor image | |
| to original input image size. | |
| Args: | |
| original_point: x, y coordinates of the point coordinates in [0..1] range in the original image | |
| input_image_size: (width, height) tuple | |
| output_image_size: (width, height) tuple | |
| """ | |
| assert point is not None | |
| assert input_image_size is not None | |
| assert output_image_size is not None | |
| print( | |
| f"point={point}, input_image_size={input_image_size}, output_image_size={output_image_size}") | |
| input_width, input_height = input_image_size | |
| output_width, output_height = output_image_size | |
| ratio = min(output_width/input_width, output_height/input_height) | |
| resized_height = int(input_height*ratio) | |
| resized_width = int(input_width*ratio) | |
| print(f'>>> resized_width={resized_width}') | |
| print(f'>>> resized_height={resized_height}') | |
| if resized_height == input_height and resized_width == input_width: | |
| return | |
| # translation of the relative positioning is only needed for dimentions that have padding | |
| if resized_width < output_width: | |
| # adjust for padding pixels | |
| point['x'] *= (output_width / resized_width) | |
| if resized_height < output_height: | |
| # adjust for padding pixels | |
| point['y'] *= (output_height / resized_height) | |
| print( | |
| f"translated point={point}, resized_image_size: {resized_width, resized_height}") | |
| def process_refexp(image, prompt: str, model_revision: str = 'main', return_annotated_image: bool = True): | |
| print(f"(image, prompt): {image}, {prompt}") | |
| if not model_revision: | |
| model_revision = 'main' | |
| print(f"model checkpoint revision: {model_revision}") | |
| load_model(model_revision) | |
| # trim prompt to 80 characters and normalize to lowercase | |
| prompt = prompt[:80].lower() | |
| # prepare encoder inputs | |
| out_size = ( | |
| processor.image_processor.size['width'], processor.image_processor.size['height']) | |
| in_size = image.size | |
| prepped_image = prepare_image_for_encoder( | |
| image, output_image_size=out_size) | |
| pixel_values = processor(prepped_image, return_tensors="pt").pixel_values | |
| # prepare decoder inputs | |
| task_prompt = "<s_refexp><s_prompt>{user_input}</s_prompt><s_target_center>" | |
| prompt = task_prompt.replace("{user_input}", prompt) | |
| decoder_input_ids = processor.tokenizer( | |
| prompt, add_special_tokens=False, return_tensors="pt").input_ids | |
| # generate answer | |
| outputs = model.generate( | |
| pixel_values.to(device), | |
| decoder_input_ids=decoder_input_ids.to(device), | |
| max_length=model.decoder.config.max_position_embeddings, | |
| early_stopping=True, | |
| pad_token_id=processor.tokenizer.pad_token_id, | |
| eos_token_id=processor.tokenizer.eos_token_id, | |
| use_cache=True, | |
| num_beams=1, | |
| bad_words_ids=[[processor.tokenizer.unk_token_id]], | |
| return_dict_in_generate=True, | |
| ) | |
| # postprocess | |
| sequence = processor.batch_decode(outputs.sequences)[0] | |
| print(fr"predicted decoder sequence: {html.escape(sequence)}") | |
| sequence = sequence.replace(processor.tokenizer.eos_token, "").replace( | |
| processor.tokenizer.pad_token, "") | |
| # remove first task start token | |
| sequence = re.sub(r"<.*?>", "", sequence, count=1).strip() | |
| print( | |
| fr"predicted decoder sequence before token2json: {html.escape(sequence)}") | |
| seqjson = processor.token2json(sequence) | |
| # safeguard in case predicted sequence does not include a target_center token | |
| center_point = seqjson.get('target_center') | |
| if center_point is None: | |
| print( | |
| f"predicted sequence has no target_center, seq:{sequence}") | |
| center_point = {"x": 0, "y": 0} | |
| return center_point | |
| print(f"predicted center_point with text coordinates: {center_point}") | |
| # safeguard in case text prediction is missing some center point coordinates | |
| # or coordinates are not valid numeric values | |
| try: | |
| x = float(center_point.get("x", 0)) | |
| except ValueError: | |
| x = 0 | |
| try: | |
| y = float(center_point.get("y", 0)) | |
| except ValueError: | |
| y = 0 | |
| # replace str with float coords | |
| center_point = {"x": x, "y": y, | |
| "decoder output sequence (before x,y adjustment)": sequence} | |
| print(f"predicted center_point with float coordinates: {center_point}") | |
| print(f"input image size: {in_size}") | |
| print(f"processed prompt: {prompt}") | |
| # convert coordinates from tensor image size to input image size | |
| out_size = ( | |
| processor.image_processor.size['width'], processor.image_processor.size['height']) | |
| translate_point_coords_from_out_to_in( | |
| point=center_point, input_image_size=in_size, output_image_size=out_size) | |
| width, height = in_size | |
| x = math.floor(width*center_point["x"]) | |
| y = math.floor(height*center_point["y"]) | |
| print( | |
| f"to image pixel values: x, y: {x, y}") | |
| if return_annotated_image: | |
| # draw center point circle | |
| img1 = ImageDraw.Draw(image) | |
| r = 30 | |
| shape = [(x-r, y-r), (x+r, y+r)] | |
| img1.ellipse(shape, outline="green", width=20) | |
| img1.ellipse(shape, outline="white", width=10) | |
| else: | |
| # do not return image if its an API call to save bandwidth | |
| image = None | |
| return image, center_point | |
| title = "Demo: GuardianUI RefExp Click" | |
| description = "Gradio Demo for Donut RefExp task, an instance of `VisionEncoderDecoderModel` fine-tuned on [UIBert RefExp](https://huggingface.co/datasets/ivelin/ui_refexp_saved) Dataset (UI Referring Expression). To use it, simply upload your image and type a prompt and click 'submit', or click one of the examples to load them. Optionally enter value for model git revision; latest checkpoint will be used by default." | |
| article = "<p style='text-align: center'><a href='https://arxiv.org/abs/2111.15664' target='_blank'>Donut: OCR-free Document Understanding Transformer</a> | <a href='https://github.com/clovaai/donut' target='_blank'>Github Repo</a></p>" | |
| examples = [["example_1.jpg", "select the menu icon right of cloud icon at the top", "", True], | |
| ["example_1.jpg", "click on down arrow beside the entertainment", "", True], | |
| ["example_1.jpg", "select the down arrow button beside lifestyle", "", True], | |
| ["example_1.jpg", "click on the image beside the option traffic", "", True], | |
| ["example_3.jpg", "select the third row first image", "", True], | |
| ["example_3.jpg", "click the tick mark on the first image", "", True], | |
| ["example_3.jpg", "select the ninth image", "", True], | |
| ["example_3.jpg", "select the add icon", "", True], | |
| ["example_3.jpg", "click the first image", "", True], | |
| ["val-image-4.jpg", 'select 4153365454', "", True], | |
| ['val-image-4.jpg', 'go to cell', "", True], | |
| ['val-image-4.jpg', 'select number above cell', "", True], | |
| ["val-image-1.jpg", "select calendar option", "", True], | |
| ["val-image-1.jpg", "select photos&videos option", "", True], | |
| ["val-image-2.jpg", "click on change store", "", True], | |
| ["val-image-2.jpg", "click on shop menu at the bottom", "", True], | |
| ["val-image-3.jpg", "click on image above short meow", "", True], | |
| ["val-image-3.jpg", "go to cat sounds", "", True], | |
| ["example_2.jpg", "click on green color button", "", True], | |
| ["example_2.jpg", "click on text which is beside call now", "", True], | |
| ["example_2.jpg", "click on more button", "", True], | |
| ["example_2.jpg", "enter the text field next to the name", "", True], | |
| ] | |
| demo = gr.Interface(fn=process_refexp, | |
| inputs=[gr.Image(type="pil"), "text", "text", gr.Checkbox( | |
| value=True, label="Return Annotated Image", visible=False)], | |
| outputs=[gr.Image(type="pil"), "json"], | |
| title=title, | |
| description=description, | |
| article=article, | |
| examples=examples, | |
| # caching examples inference takes too long to start space after app change commit | |
| cache_examples=False | |
| ) | |
| # share=True when running in a Jupyter Notebook | |
| demo.launch(server_name="0.0.0.0") | |