aitz/prepare_trajectory_grounding.py

import json
from copy import deepcopy
import os
from pathlib import Path
import re
from PIL import Image
from multiprocessing import Pool
import ast

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

def transform_bbox(bbox, image_x, image_y):
    # transform [y,x,height,width] to [x1,y1,x2,y2]
    y, x, height, width = bbox
    x1 = int(1000 * x / image_x)
    y1 = int(1000 * y / image_y)
    x2 = int(1000 * (x + width) / image_x)
    y2 = int(1000 * (y + height) / image_y)
    bbox_norm = [x1, y1, x2, y2]

    return bbox_norm

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:        
        for i, step in enumerate(episode):
            res_touch_yx = ast.literal_eval(step["result_touch_yx"])
            res_touch_yx = [round(res_touch_yx[0], 3), round(res_touch_yx[1], 3)]
            res_lift_yx = ast.literal_eval(step["result_lift_yx"])
            res_lift_yx = [round(res_lift_yx[0], 3), round(res_lift_yx[1], 3)]

            is_tap = int(res_touch_yx[0]) != -1 and (res_touch_yx[0] == res_lift_yx[0] and res_touch_yx[1] == res_lift_yx[1])

            step["is_tap"] = is_tap

            if "coat_action_desc" not in step or step["coat_action_desc"] is None:
                break

            if not is_tap:
                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=step["instruction"]
                    ),
                },
            ]

            cur_img_list = [Path("./") / Path(step["image_path"])]

            if window_size > 0:
                window_steps = episode[i-window_size:i] if i >= window_size else episode[: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["coat_action_desc"]
                                        for j, prev_step in enumerate(episode[: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["is_tap"]:
                        convs.append(
                            {
                                "from": "human",
                                "value": grounding_step_prompt.format(
                                    instruction=win_step_i["coat_action_desc"], 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["coat_action_desc"], step_idx=i+1-(len(window_steps)-j)
                                ),
                            }
                        )
                        if win_step_i["result_action_text"]:
                            convs.append(
                                {
                                    "from": "human",
                                    "value": act_step_ans.format(
                                        prev_action=f"Type: {win_step_i['result_action_text']}"
                                    ),
                                }
                            )
                        else:
                            convs.append(
                                {
                                    "from": "human",
                                    "value": act_step_ans.format(
                                        prev_action=win_step_i["coat_action_desc"]
                                    ),
                                }
                            )
                win_img_list = [
                    str(Path("./") / Path(win_step["image_path"])) for win_step in window_steps
                ]
                
            else:
                convs.append(
                        {
                            "from": "human",
                            "value": user_history_instr_prompt.format(
                                history_list="\n".join(
                                    [
                                        f"\t{j+1}. " + prev_step["coat_action_desc"]
                                        for j, prev_step in enumerate(episode[:i-window_size])
                                    ]
                                )
                            ),
                        },
                    )

            img_list = cur_img_list + win_img_list if window_size > 0 else cur_img_list

            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["coat_action_desc"], 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,
                }
            )
    
    return instructions


# Example usage
if __name__ == "__main__":
    # Sample data loading (replace with your actual file path)

    data = []

    episode_files = list(Path("./").rglob("*/*/*.json"))
    for episode_file in episode_files:
        with open(episode_file, "r", encoding="utf-8") as file:
            episode_data = json.load(file)
            data.append(episode_data)
    
    img_parent_path = Path("./")

    def preprocess_coord_norm(episode):
        for step in episode:
            if int(ast.literal_eval(step["result_touch_yx"])[0]) != -1:
                if not Path(img_parent_path / step["image_path"]).exists():
                    continue
                image_x, image_y = Image.open(img_parent_path / step["image_path"]).size
                elem_bboxes = ast.literal_eval(step["ui_positions"]) # (y, x, height, width)
                elem_bboxes = [transform_bbox(bbox, image_x, image_y) for bbox in elem_bboxes]
                click_point_yx = ast.literal_eval(step["result_touch_yx"])
                click_point = [1000*click_point_yx[1], 1000*click_point_yx[0]]

                # check which element contains the click point
                bbox = None
                for elem_bbox in elem_bboxes:
                    if elem_bbox[0] <= click_point[0] <= elem_bbox[2] and elem_bbox[1] <= click_point[1] <= elem_bbox[3]:
                        if bbox is None:
                            bbox = elem_bbox
                        else:
                            # calculate area, take the smaller one
                            area = (bbox[2] - bbox[0]) * (bbox[3] - bbox[1])
                            new_area = (elem_bbox[2] - elem_bbox[0]) * (elem_bbox[3] - elem_bbox[1])
                            if new_area < area:
                                bbox = elem_bbox
                if bbox is None:
                    coord_norm = [int(click_point[0]), int(click_point[1])]
                else:
                    coord_norm = [(bbox[0] + bbox[2]) // 2, (bbox[1] + bbox[3]) // 2]
            
                step["coord_norm"] = coord_norm
        return episode

    with Pool() as pool:
        data = pool.map(preprocess_coord_norm, data)

    # Process the episodes with default window_size=3
    # window_size_list = [1, 2, 3]
    window_size_list = [0, 1, 2, 3]

    instructions = []
    for window_size in window_size_list:
        instructions.extend(process_android_episodes(data, window_size=window_size))

    print(f"Number of context aware train instructions: {len(instructions)}")
    
    with open(f"aitz_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)