import re import gradio as gr from PIL import Image, ImageDraw import math import torch import html from transformers import DonutProcessor, VisionEncoderDecoderModel pretrained_repo_name = 'ivelin/donut-refexp-click' pretrained_revision = 'main' # revision can be git commit hash, branch or tag # use 'main' for latest revision print(f"Loading model checkpoint: {pretrained_repo_name}") processor = DonutProcessor.from_pretrained(pretrained_repo_name, revision=pretrained_revision, use_auth_token="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") model = VisionEncoderDecoderModel.from_pretrained(pretrained_repo_name, use_auth_token="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", revision=pretrained_revision) device = "cuda" if torch.cuda.is_available() else "cpu" model.to(device) 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) # print(f'>>> resized_height={resized_height}') resized_width = int(input_width*ratio) # print(f'>>> resized_width={resized_width}') 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: Image, prompt: str): print(f"(image, prompt): {image}, {prompt}") # trim prompt to 80 characters and normalize to lowercase prompt = prompt[:80].lower() # prepare encoder inputs pixel_values = processor(image, return_tensors="pt").pixel_values # prepare decoder inputs task_prompt = "{user_input}" 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": sequence} print(f"predicted center_point with float coordinates: {center_point}") print(f"image object: {image}") print(f"image size: {image.size}") width, height = image.size print(f"image width, height: {width, height}") print(f"processed prompt: {prompt}") # convert coordinates from tensor image size to input image size out_size = (processor.image_processor.size[1], processor.image_processor.size[0]) translate_point_coords_from_out_to_in(point=center_point, input_image_size=image.size, output_image_size=out_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}") # 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) return image, center_point title = "Demo: Donut 🍩 for UI RefExp - Center Point (by GuardianUI)" 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. See the model training Colab Notebook for this space. Read more at the links below." article = "

Donut: OCR-free Document Understanding Transformer | Github Repo

" examples = [["example_1.jpg", "select the setting icon from top right corner"], ["example_1.jpg", "click on down arrow beside the entertainment"], ["example_1.jpg", "select the down arrow button beside lifestyle"], ["example_1.jpg", "click on the image beside the option traffic"], ["example_3.jpg", "select the third row first image"], ["example_3.jpg", "click the tick mark on the first image"], ["example_3.jpg", "select the ninth image"], ["example_3.jpg", "select the add icon"], ["example_3.jpg", "click the first image"], ["val-image-4.jpg", 'select 4153365454'], ['val-image-4.jpg', 'go to cell'], ['val-image-4.jpg', 'select number above cell'], ["val-image-1.jpg", "select calendar option"], ["val-image-1.jpg", "select photos&videos option"], ["val-image-2.jpg", "click on change store"], ["val-image-2.jpg", "click on shop menu at the bottom"], ["val-image-3.jpg", "click on image above short meow"], ["val-image-3.jpg", "go to cat sounds"], ["example_2.jpg", "click on green color button"], ["example_2.jpg", "click on text which is beside call now"], ["example_2.jpg", "click on more button"], ["example_2.jpg", "enter the text field next to the name"], ] demo = gr.Interface(fn=process_refexp, inputs=[gr.Image(type="pil"), "text"], 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 ) demo.launch()