# 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