Create utils.py

utils.py

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+# from ultralytics import YOLO
+import os
+import io
+import base64
+import time
+from PIL import Image, ImageDraw, ImageFont
+import json
+import requests
+# utility function
+import os
+from openai import AzureOpenAI
+
+import json
+import sys
+import os
+import cv2
+import numpy as np
+# %matplotlib inline
+from matplotlib import pyplot as plt
+import easyocr
+from paddleocr import PaddleOCR
+reader = easyocr.Reader(['en'])
+paddle_ocr = PaddleOCR(
+    lang='en',  # other lang also available
+    use_angle_cls=False,
+    use_gpu=False,  # using cuda will conflict with pytorch in the same process
+    show_log=False,
+    max_batch_size=1024,
+    use_dilation=True,  # improves accuracy
+    det_db_score_mode='slow',  # improves accuracy
+    rec_batch_num=1024)
+import time
+import base64
+
+import os
+import ast
+import torch
+from typing import Tuple, List
+from torchvision.ops import box_convert
+import re
+from torchvision.transforms import ToPILImage
+import supervision as sv
+import torchvision.transforms as T
+
+
+def get_caption_model_processor(model_name, model_name_or_path="Salesforce/blip2-opt-2.7b", device=None):
+    if not device:
+        device = "cuda" if torch.cuda.is_available() else "cpu"
+    if model_name == "blip2":
+        from transformers import Blip2Processor, Blip2ForConditionalGeneration
+        processor = Blip2Processor.from_pretrained("Salesforce/blip2-opt-2.7b")
+        if device == 'cpu':
+            model = Blip2ForConditionalGeneration.from_pretrained(
+            model_name_or_path, device_map=None, torch_dtype=torch.float32
+        ) 
+        else:
+            model = Blip2ForConditionalGeneration.from_pretrained(
+            model_name_or_path, device_map=None, torch_dtype=torch.float16
+        ).to(device)
+    elif model_name == "florence2":
+        from transformers import AutoProcessor, AutoModelForCausalLM 
+        processor = AutoProcessor.from_pretrained("microsoft/Florence-2-base", trust_remote_code=True)
+        if device == 'cpu':
+            model = AutoModelForCausalLM.from_pretrained(model_name_or_path, torch_dtype=torch.float32, trust_remote_code=True)
+        else:
+            model = AutoModelForCausalLM.from_pretrained(model_name_or_path, torch_dtype=torch.float16, trust_remote_code=True).to(device)
+    return {'model': model.to(device), 'processor': processor}
+
+
+def get_yolo_model(model_path):
+    from ultralytics import YOLO
+    # Load the model.
+    model = YOLO(model_path)
+    return model
+
+
+@torch.inference_mode()
+def get_parsed_content_icon(filtered_boxes, ocr_bbox, image_source, caption_model_processor, prompt=None):
+    to_pil = ToPILImage()
+    if ocr_bbox:
+        non_ocr_boxes = filtered_boxes[len(ocr_bbox):]
+    else:
+        non_ocr_boxes = filtered_boxes
+    croped_pil_image = []
+    for i, coord in enumerate(non_ocr_boxes):
+        xmin, xmax = int(coord[0]*image_source.shape[1]), int(coord[2]*image_source.shape[1])
+        ymin, ymax = int(coord[1]*image_source.shape[0]), int(coord[3]*image_source.shape[0])
+        cropped_image = image_source[ymin:ymax, xmin:xmax, :]
+        croped_pil_image.append(to_pil(cropped_image))
+
+    model, processor = caption_model_processor['model'], caption_model_processor['processor']
+    if not prompt:
+        if 'florence' in model.config.name_or_path:
+            prompt = "<CAPTION>"
+        else:
+            prompt = "The image shows"
+
+    batch_size = 10  # Number of samples per batch
+    generated_texts = []
+    device = model.device
+
+    for i in range(0, len(croped_pil_image), batch_size):
+        batch = croped_pil_image[i:i+batch_size]
+        if model.device.type == 'cuda':
+            inputs = processor(images=batch, text=[prompt]*len(batch), return_tensors="pt").to(device=device, dtype=torch.float16)
+        else:
+            inputs = processor(images=batch, text=[prompt]*len(batch), return_tensors="pt").to(device=device)
+        if 'florence' in model.config.name_or_path:
+            generated_ids = model.generate(input_ids=inputs["input_ids"],pixel_values=inputs["pixel_values"],max_new_tokens=1024,num_beams=3, do_sample=False)
+        else:
+            generated_ids = model.generate(**inputs, max_length=100, num_beams=5, no_repeat_ngram_size=2, early_stopping=True, num_return_sequences=1) # temperature=0.01, do_sample=True,
+        generated_text = processor.batch_decode(generated_ids, skip_special_tokens=True)
+        generated_text = [gen.strip() for gen in generated_text]
+        generated_texts.extend(generated_text)
+
+    return generated_texts
+
+
+
+def get_parsed_content_icon_phi3v(filtered_boxes, ocr_bbox, image_source, caption_model_processor):
+    to_pil = ToPILImage()
+    if ocr_bbox:
+        non_ocr_boxes = filtered_boxes[len(ocr_bbox):]
+    else:
+        non_ocr_boxes = filtered_boxes
+    croped_pil_image = []
+    for i, coord in enumerate(non_ocr_boxes):
+        xmin, xmax = int(coord[0]*image_source.shape[1]), int(coord[2]*image_source.shape[1])
+        ymin, ymax = int(coord[1]*image_source.shape[0]), int(coord[3]*image_source.shape[0])
+        cropped_image = image_source[ymin:ymax, xmin:xmax, :]
+        croped_pil_image.append(to_pil(cropped_image))
+
+    model, processor = caption_model_processor['model'], caption_model_processor['processor']
+    device = model.device
+    messages = [{"role": "user", "content": "<|image_1|>\ndescribe the icon in one sentence"}] 
+    prompt = processor.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+
+    batch_size = 5  # Number of samples per batch
+    generated_texts = []
+
+    for i in range(0, len(croped_pil_image), batch_size):
+        images = croped_pil_image[i:i+batch_size]
+        image_inputs = [processor.image_processor(x, return_tensors="pt") for x in images]
+        inputs ={'input_ids': [], 'attention_mask': [], 'pixel_values': [], 'image_sizes': []}
+        texts = [prompt] * len(images)
+        for i, txt in enumerate(texts):
+            input = processor._convert_images_texts_to_inputs(image_inputs[i], txt, return_tensors="pt")
+            inputs['input_ids'].append(input['input_ids'])
+            inputs['attention_mask'].append(input['attention_mask'])
+            inputs['pixel_values'].append(input['pixel_values'])
+            inputs['image_sizes'].append(input['image_sizes'])
+        max_len = max([x.shape[1] for x in inputs['input_ids']])
+        for i, v in enumerate(inputs['input_ids']):
+            inputs['input_ids'][i] = torch.cat([processor.tokenizer.pad_token_id * torch.ones(1, max_len - v.shape[1], dtype=torch.long), v], dim=1)
+            inputs['attention_mask'][i] = torch.cat([torch.zeros(1, max_len - v.shape[1], dtype=torch.long), inputs['attention_mask'][i]], dim=1)
+        inputs_cat = {k: torch.concatenate(v).to(device) for k, v in inputs.items()}
+
+        generation_args = { 
+            "max_new_tokens": 25, 
+            "temperature": 0.01, 
+            "do_sample": False, 
+        } 
+        generate_ids = model.generate(**inputs_cat, eos_token_id=processor.tokenizer.eos_token_id, **generation_args) 
+        # # remove input tokens 
+        generate_ids = generate_ids[:, inputs_cat['input_ids'].shape[1]:]
+        response = processor.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)
+        response = [res.strip('\n').strip() for res in response]
+        generated_texts.extend(response)
+
+    return generated_texts
+
+def remove_overlap(boxes, iou_threshold, ocr_bbox=None):
+    assert ocr_bbox is None or isinstance(ocr_bbox, List)
+
+    def box_area(box):
+        return (box[2] - box[0]) * (box[3] - box[1])
+
+    def intersection_area(box1, box2):
+        x1 = max(box1[0], box2[0])
+        y1 = max(box1[1], box2[1])
+        x2 = min(box1[2], box2[2])
+        y2 = min(box1[3], box2[3])
+        return max(0, x2 - x1) * max(0, y2 - y1)
+
+    def IoU(box1, box2):
+        intersection = intersection_area(box1, box2)
+        union = box_area(box1) + box_area(box2) - intersection + 1e-6
+        if box_area(box1) > 0 and box_area(box2) > 0:
+            ratio1 = intersection / box_area(box1)
+            ratio2 = intersection / box_area(box2)
+        else:
+            ratio1, ratio2 = 0, 0
+        return max(intersection / union, ratio1, ratio2)
+
+    boxes = boxes.tolist()
+    filtered_boxes = []
+    if ocr_bbox:
+        filtered_boxes.extend(ocr_bbox)
+    # print('ocr_bbox!!!', ocr_bbox)
+    for i, box1 in enumerate(boxes):
+        # if not any(IoU(box1, box2) > iou_threshold and box_area(box1) > box_area(box2) for j, box2 in enumerate(boxes) if i != j):
+        is_valid_box = True
+        for j, box2 in enumerate(boxes):
+            if i != j and IoU(box1, box2) > iou_threshold and box_area(box1) > box_area(box2):
+                is_valid_box = False
+                break
+        if is_valid_box:
+            # add the following 2 lines to include ocr bbox
+            if ocr_bbox:
+                if not any(IoU(box1, box3) > iou_threshold for k, box3 in enumerate(ocr_bbox)):
+                    filtered_boxes.append(box1)
+            else:
+                filtered_boxes.append(box1)
+    return torch.tensor(filtered_boxes)
+
+def load_image(image_path: str) -> Tuple[np.array, torch.Tensor]:
+    transform = T.Compose(
+        [
+            T.RandomResize([800], max_size=1333),
+            T.ToTensor(),
+            T.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),
+        ]
+    )
+    image_source = Image.open(image_path).convert("RGB")
+    image = np.asarray(image_source)
+    image_transformed, _ = transform(image_source, None)
+    return image, image_transformed
+
+
+def annotate(image_source: np.ndarray, boxes: torch.Tensor, logits: torch.Tensor, phrases: List[str], text_scale: float, 
+             text_padding=5, text_thickness=2, thickness=3) -> np.ndarray:
+    """    
+    This function annotates an image with bounding boxes and labels.
+
+    Parameters:
+    image_source (np.ndarray): The source image to be annotated.
+    boxes (torch.Tensor): A tensor containing bounding box coordinates. in cxcywh format, pixel scale
+    logits (torch.Tensor): A tensor containing confidence scores for each bounding box.
+    phrases (List[str]): A list of labels for each bounding box.
+    text_scale (float): The scale of the text to be displayed. 0.8 for mobile/web, 0.3 for desktop # 0.4 for mind2web
+
+    Returns:
+    np.ndarray: The annotated image.
+    """
+    h, w, _ = image_source.shape
+    boxes = boxes * torch.Tensor([w, h, w, h])
+    xyxy = box_convert(boxes=boxes, in_fmt="cxcywh", out_fmt="xyxy").numpy()
+    xywh = box_convert(boxes=boxes, in_fmt="cxcywh", out_fmt="xywh").numpy()
+    detections = sv.Detections(xyxy=xyxy)
+
+    labels = [f"{phrase}" for phrase in range(boxes.shape[0])]
+
+    from util.box_annotator import BoxAnnotator 
+    box_annotator = BoxAnnotator(text_scale=text_scale, text_padding=text_padding,text_thickness=text_thickness,thickness=thickness) # 0.8 for mobile/web, 0.3 for desktop # 0.4 for mind2web
+    annotated_frame = image_source.copy()
+    annotated_frame = box_annotator.annotate(scene=annotated_frame, detections=detections, labels=labels, image_size=(w,h))
+
+    label_coordinates = {f"{phrase}": v for phrase, v in zip(phrases, xywh)}
+    return annotated_frame, label_coordinates
+
+
+def predict(model, image, caption, box_threshold, text_threshold):
+    """ Use huggingface model to replace the original model
+    """
+    model, processor = model['model'], model['processor']
+    device = model.device
+
+    inputs = processor(images=image, text=caption, return_tensors="pt").to(device)
+    with torch.no_grad():
+        outputs = model(**inputs)
+
+    results = processor.post_process_grounded_object_detection(
+        outputs,
+        inputs.input_ids,
+        box_threshold=box_threshold, # 0.4,
+        text_threshold=text_threshold, # 0.3,
+        target_sizes=[image.size[::-1]]
+    )[0]
+    boxes, logits, phrases = results["boxes"], results["scores"], results["labels"]
+    return boxes, logits, phrases
+
+
+def predict_yolo(model, image_path, box_threshold):
+    """ Use huggingface model to replace the original model
+    """
+    # model = model['model']
+    
+    result = model.predict(
+    source=image_path,
+    conf=box_threshold,
+    # iou=0.5, # default 0.7
+    )
+    boxes = result[0].boxes.xyxy#.tolist() # in pixel space
+    conf = result[0].boxes.conf
+    phrases = [str(i) for i in range(len(boxes))]
+
+    return boxes, conf, phrases
+
+
+def get_som_labeled_img(img_path, model=None, BOX_TRESHOLD = 0.01, output_coord_in_ratio=False, ocr_bbox=None, text_scale=0.4, text_padding=5, draw_bbox_config=None, caption_model_processor=None, ocr_text=[], use_local_semantics=True, iou_threshold=0.9,prompt=None):
+    """ ocr_bbox: list of xyxy format bbox
+    """
+    TEXT_PROMPT = "clickable buttons on the screen"
+    # BOX_TRESHOLD = 0.02 # 0.05/0.02 for web and 0.1 for mobile
+    TEXT_TRESHOLD = 0.01 # 0.9 # 0.01
+    image_source = Image.open(img_path).convert("RGB")
+    w, h = image_source.size
+    # import pdb; pdb.set_trace()
+    if False: # TODO
+        xyxy, logits, phrases = predict(model=model, image=image_source, caption=TEXT_PROMPT, box_threshold=BOX_TRESHOLD, text_threshold=TEXT_TRESHOLD)
+    else:
+        xyxy, logits, phrases = predict_yolo(model=model, image_path=img_path, box_threshold=BOX_TRESHOLD)
+    xyxy = xyxy / torch.Tensor([w, h, w, h]).to(xyxy.device)
+    image_source = np.asarray(image_source)
+    phrases = [str(i) for i in range(len(phrases))]
+
+    # annotate the image with labels
+    h, w, _ = image_source.shape
+    if ocr_bbox:
+        ocr_bbox = torch.tensor(ocr_bbox) / torch.Tensor([w, h, w, h])
+        ocr_bbox=ocr_bbox.tolist()
+    else:
+        print('no ocr bbox!!!')
+        ocr_bbox = None
+    filtered_boxes = remove_overlap(boxes=xyxy, iou_threshold=iou_threshold, ocr_bbox=ocr_bbox)
+    
+    # get parsed icon local semantics
+    if use_local_semantics:
+        caption_model = caption_model_processor['model']
+        if 'phi3_v' in caption_model.config.model_type: 
+            parsed_content_icon = get_parsed_content_icon_phi3v(filtered_boxes, ocr_bbox, image_source, caption_model_processor)
+        else:
+            parsed_content_icon = get_parsed_content_icon(filtered_boxes, ocr_bbox, image_source, caption_model_processor, prompt=prompt)
+        ocr_text = [f"Text Box ID {i}: {txt}" for i, txt in enumerate(ocr_text)]
+        icon_start = len(ocr_text)
+        parsed_content_icon_ls = []
+        for i, txt in enumerate(parsed_content_icon):
+            parsed_content_icon_ls.append(f"Icon Box ID {str(i+icon_start)}: {txt}")
+        parsed_content_merged = ocr_text + parsed_content_icon_ls
+    else:
+        ocr_text = [f"Text Box ID {i}: {txt}" for i, txt in enumerate(ocr_text)]
+        parsed_content_merged = ocr_text
+
+    filtered_boxes = box_convert(boxes=filtered_boxes, in_fmt="xyxy", out_fmt="cxcywh")
+
+    phrases = [i for i in range(len(filtered_boxes))]
+    
+    # draw boxes
+    if draw_bbox_config:
+        annotated_frame, label_coordinates = annotate(image_source=image_source, boxes=filtered_boxes, logits=logits, phrases=phrases, **draw_bbox_config)
+    else:
+        annotated_frame, label_coordinates = annotate(image_source=image_source, boxes=filtered_boxes, logits=logits, phrases=phrases, text_scale=text_scale, text_padding=text_padding)
+    
+    pil_img = Image.fromarray(annotated_frame)
+    buffered = io.BytesIO()
+    pil_img.save(buffered, format="PNG")
+    encoded_image = base64.b64encode(buffered.getvalue()).decode('ascii')
+    if output_coord_in_ratio:
+        # h, w, _ = image_source.shape
+        label_coordinates = {k: [v[0]/w, v[1]/h, v[2]/w, v[3]/h] for k, v in label_coordinates.items()}
+        assert w == annotated_frame.shape[1] and h == annotated_frame.shape[0]
+
+    return encoded_image, label_coordinates, parsed_content_merged
+
+
+def get_xywh(input):
+    x, y, w, h = input[0][0], input[0][1], input[2][0] - input[0][0], input[2][1] - input[0][1]
+    x, y, w, h = int(x), int(y), int(w), int(h)
+    return x, y, w, h
+
+def get_xyxy(input):
+    x, y, xp, yp = input[0][0], input[0][1], input[2][0], input[2][1]
+    x, y, xp, yp = int(x), int(y), int(xp), int(yp)
+    return x, y, xp, yp
+
+def get_xywh_yolo(input):
+    x, y, w, h = input[0], input[1], input[2] - input[0], input[3] - input[1]
+    x, y, w, h = int(x), int(y), int(w), int(h)
+    return x, y, w, h
+    
+
+
+def check_ocr_box(image_path, display_img = True, output_bb_format='xywh', goal_filtering=None, easyocr_args=None, use_paddleocr=False):
+    if use_paddleocr:
+        result = paddle_ocr.ocr(image_path, cls=False)[0]
+        coord = [item[0] for item in result]
+        text = [item[1][0] for item in result]
+    else:  # EasyOCR
+        if easyocr_args is None:
+            easyocr_args = {}
+        result = reader.readtext(image_path, **easyocr_args)
+        # print('goal filtering pred:', result[-5:])
+        coord = [item[0] for item in result]
+        text = [item[1] for item in result]
+    # read the image using cv2
+    if display_img:
+        opencv_img = cv2.imread(image_path)
+        opencv_img = cv2.cvtColor(opencv_img, cv2.COLOR_RGB2BGR)
+        bb = []
+        for item in coord:
+            x, y, a, b = get_xywh(item)
+            # print(x, y, a, b)
+            bb.append((x, y, a, b))
+            cv2.rectangle(opencv_img, (x, y), (x+a, y+b), (0, 255, 0), 2)
+        
+        # Display the image
+        plt.imshow(opencv_img)
+    else:
+        if output_bb_format == 'xywh':
+            bb = [get_xywh(item) for item in coord]
+        elif output_bb_format == 'xyxy':
+            bb = [get_xyxy(item) for item in coord]
+        # print('bounding box!!!', bb)
+    return (text, bb), goal_filtering
+
+