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import json
from copy import deepcopy
import os
from pathlib import Path
from PIL import Image
from tqdm import tqdm
import multiprocessing
def resize_image(image, scale=0.75):
"""
Resize image to have its shorter edge equal to 720 pixels while maintaining aspect ratio.
Args:
image: PIL Image object
Returns:
Resized PIL Image
"""
# Get current dimensions
width, height = image.size
# Calculate new dimensions
new_width = int(width * scale)
new_height = int(height * scale)
# Resize image
resized_image = image.resize((new_width, new_height), Image.LANCZOS)
return resized_image
def merge_convs(conversations):
"""
Merge all successive 'human' conversations comprehensively.
Args:
conversations (list): List of conversation dictionaries
Returns:
list: Processed conversations with all successive human messages merged
Raises:
ValueError: If input is not a list or contains invalid conversation dictionaries
"""
# Validate input
if not isinstance(conversations, list):
raise ValueError("Input must be a list of conversation dictionaries")
# Validate each conversation dictionary structure
for conv in conversations:
if not isinstance(conv, dict):
raise ValueError("Each conversation must be a dictionary")
if 'from' not in conv or 'value' not in conv:
raise ValueError("Each conversation must have 'from' and 'value' keys")
processed_conversations = []
i = 0
while i < len(conversations):
current_conv = conversations[i]
# If current conversation is 'human', start merging
if current_conv['from'] == 'human':
# Collect all successive human conversations
merged_value = current_conv['value']
j = i + 1
while j < len(conversations) and conversations[j]['from'] == 'human':
merged_value += '\n\n' + conversations[j]['value']
j += 1
# Update current conversation with merged value
current_conv['value'] = merged_value
# Move index to last non-human conversation
i = j
else:
# For non-human conversations, just add to processed list
i += 1
processed_conversations.append(current_conv)
return processed_conversations
grounding_step_prompt = "<|img|>Step {step_idx}. Given a GUI image, what are the relative (0-1000) pixel point coordinates for the element corresponding to the following instruction or description: {instruction}"
grounding_step_ans = "```\n{point_str}\n```"
act_step_prompt = "<|img|>Step {step_idx}. Instruction: {prev_instruction}"
act_step_ans = "The agent's action: {prev_action}"
user_start_prompt = "The agent is performing the ultimate task: {ultimate_task}."
user_history_instr_prompt = "History of the agent's steps:\n{history_list}."
def process_android_episodes(data, window_size=2):
"""
Process Android episodes and extract steps with click or long_press actions.
Args:
data (list): List of episode dictionaries
window_size (int, optional): Number of recent image-included conversations to include.
Defaults to 3 (current image + 2 previous image-included steps).
Returns:
dict: Dictionary with episode_id as key and list of filtered steps as value
"""
instructions = []
for episode in data:
episode_id = episode["episode_id"]
for i, step in enumerate(episode["steps"]):
action = step.get("action", {})
action_type = action.get("action_type")
is_grounding = action_type in ["click", "long_press"] and step["bbox_norm"] is not None
if not is_grounding:
continue
if window_size > 0 and i == 0: # skip the first step if window_size > 0
continue
convs = [
{
"from": "human",
"value": user_start_prompt.format(
ultimate_task=episode["goal"]
),
},
]
cur_img_list = [Path("./images") / Path(step["img_path"]).name]
if window_size > 0:
window_steps = episode["steps"][i-window_size:i] if i >= window_size else episode["steps"][:i]
if i > window_size: # has more history steps larger than window_size
convs.append(
{
"from": "human",
"value": user_history_instr_prompt.format(
history_list="\n".join(
[
f"\t{j+1}. " + prev_step["step_instruction"]
for j, prev_step in enumerate(episode["steps"][:i-window_size])
]
)
),
},
)
convs.append(
{
"from": "human",
"value": "The recent steps with the GUI images are as follows:\n",
}
)
for j, win_step_i in enumerate(window_steps):
if win_step_i["action"]["action_type"] in ["click", "long_press"]:
convs.append(
{
"from": "human",
"value": grounding_step_prompt.format(
instruction=win_step_i["step_instruction"], step_idx=i+1-(len(window_steps)-j)
),
}
)
convs.append(
{
"from": "gpt",
"value": grounding_step_ans.format(point_str=f"({win_step_i['coord_norm'][0]}, {win_step_i['coord_norm'][1]})"),
}
)
else:
convs.append(
{
"from": "human",
"value": act_step_prompt.format(
prev_instruction=win_step_i["step_instruction"], step_idx=i+1-(len(window_steps)-j)
),
}
)
convs.append(
{
"from": "human",
"value": act_step_ans.format(
prev_action=win_step_i["action"]
),
}
)
win_img_list = [
Path("./images") / Path(win_step["img_path"]).name for win_step in window_steps
]
if not all([img_path.exists() for img_path in win_img_list+cur_img_list]):
print(f"Image not found for episode {episode_id}, step {i+1}. Skipping...")
continue
resize_scale = 0.5
win_img_list_resized = []
for img_path in win_img_list:
new_save_name = img_path.stem + f"_{resize_scale}x" + img_path.suffix
new_save_dir = img_path.parent.parent / f"images_resized"
new_save_dir.mkdir(parents=True, exist_ok=True)
new_save_path = new_save_dir / new_save_name
if new_save_path.exists():
try:
Image.open(str(new_save_path))
except Exception as e:
print(f"Error opening image {new_save_path}: {e}")
os.remove(new_save_path)
else:
win_img_list_resized.append(new_save_path)
continue
win_img = Image.open(str(img_path))
win_img = resize_image(win_img, scale=resize_scale)
win_img.save(str(new_save_path))
win_img_list_resized.append(new_save_path)
else: # window_size == 0
convs.append(
{
"from": "human",
"value": user_history_instr_prompt.format(
history_list="\n".join(
[
f"\t{j+1}. " + prev_step["step_instruction"]
for j, prev_step in enumerate(episode["steps"][:i])
]
)
),
},
)
if window_size > 0:
img_list = win_img_list_resized + cur_img_list
else:
img_list = cur_img_list
if not all([img_path.exists() for img_path in img_list]):
print(f"Image not found for episode {episode_id}, step {i+1}. Skipping...")
continue
has_img_broken = False
for img_path in img_list:
try:
Image.open(str(img_path))
except Exception as e:
print(f"Error opening image {img_path}: {e}")
has_img_broken = True
break
if has_img_broken:
print(f"Image broken for episode {episode_id}, step {i+1}. Skipping...")
continue
# Current step details
convs.append(
{
"from": "human",
"value": grounding_step_prompt.format(instruction=step["step_instruction"], step_idx=i+1),
}
)
convs.append(
{
"from": "gpt",
"value": grounding_step_ans.format(point_str=f"({step['coord_norm'][0]}, {step['coord_norm'][1]})"),
}
)
convs = merge_convs(convs)
instructions.append(
{
"image": [str(img_path) for img_path in img_list],
"conversations": convs,
"bbox_norm": step["bbox_norm"],
}
)
return instructions
# Example usage
if __name__ == "__main__":
# Sample data loading (replace with your actual file path)
with open("ac_train_eposides_13603.json", "r") as file:
data = json.load(file)
# Process the episodes with default window_size=3
# Process the episodes with default window_size=3
# window_size_list = [1, 2, 3]
window_size_list = [0, 1, 2, 3]
def process_episode(args):
episode, window_size = args
return process_android_episodes([episode], window_size)
instructions = []
for window_size in window_size_list:
tasks = [(episode, window_size) for episode in data]
with multiprocessing.Pool(processes=multiprocessing.cpu_count()) as pool:
results = list(tqdm(pool.imap(process_episode, tasks), total=len(tasks), desc=f"Window Size {window_size}"))
for result in results:
instructions.extend(result)
print(f"Number of context aware train instructions: {len(instructions)}")
with open(f"ac_train_window_{'-'.join([str(e) for e in window_size_list])}_{len(instructions)//1000}k.json", "w", encoding="utf-8") as file:
json.dump(instructions, file, ensure_ascii=False, indent=4) |