Datasets:
Tasks:
Question Answering
Modalities:
Image
Formats:
imagefolder
Languages:
English
Size:
1K - 10K
ArXiv:
Tags:
uav
License:
| import json | |
| import os | |
| from typing import Tuple, List, Union, Dict, Any | |
| from util import Visualize | |
| from PIL import Image, ImageDraw, ImageFont | |
| #TODO input your work dir | |
| BASE_DIR = "" | |
| single_img_tasks_no_labels = ["Scene_Classification", "Orientation_Classification", "Environment_State_Classification", "Urban_OCR", "Class_Agnostic_Counting", "Referring_Expression_Counting", "Cross_Object_Reasoning"] | |
| single_img_tasks_w_labels = ["Ground_Target_Planning"] | |
| multi_img_tasks_no_labels = ["Target_Backtracking", "Intent_Analysis_and_Prediction", "Scene_Attribute_Understanding", "Scene_Damage_Assessment", "Scene_Analysis_and_Prediction", "Temporal_Ordering"] | |
| multi_img_tasks_w_labels = ["Air_Ground_Collaborative_Planning", "Swarm_Collaborative_Planning"] | |
| video_task = ["Event_Prediction", "Event_Tracing", "Event_Understanding"] | |
| def process_multi_img_tasks_w_labels(task_list, BASE_DIR): | |
| for task in task_list: | |
| print(f"======Processing {task}======") | |
| qa = json.load(open(os.path.join(BASE_DIR, "tasks", f"{task}.json"), 'r', encoding='utf-8')) | |
| for d in qa: | |
| for item in d["metadata"]["data_resources"]: | |
| raw_img_p = os.path.join(BASE_DIR, item["path"].replace("annotated", "raw")) | |
| annotations = [] | |
| for entity in d["target_entities"]: | |
| if entity["index"] == item["index"]: | |
| annotations.append(entity) | |
| vis_img = Visualize( | |
| image=Image.open(raw_img_p), | |
| annotations=annotations, | |
| show_labels=True, | |
| ) | |
| save_img_p = os.path.join(BASE_DIR, item["path"]) | |
| os.makedirs(os.path.dirname(save_img_p), exist_ok=True) | |
| vis_img.save(save_img_p, quality=100, subsampling=0) | |
| print(f"======Processing End {task}======") | |
| def process_multi_img_tasks_no_labels(task_list, BASE_DIR): | |
| for task in task_list: | |
| print(f"======Processing {task}======") | |
| qa = json.load(open(os.path.join(BASE_DIR, "tasks", f"{task}.json"), 'r', encoding='utf-8')) | |
| for d in qa: | |
| for item in d["metadata"]["data_resources"]: | |
| raw_img_p = os.path.join(BASE_DIR, item["path"].replace("annotated", "raw")) | |
| annotations = [] | |
| for entity in d["target_entities"]: | |
| if entity["index"] == item["index"]: | |
| annotations.append(entity) | |
| vis_img = Visualize( | |
| image=Image.open(raw_img_p), | |
| annotations=annotations, | |
| show_labels=False, | |
| ) | |
| save_img_p = os.path.join(BASE_DIR, item["path"]) | |
| os.makedirs(os.path.dirname(save_img_p), exist_ok=True) | |
| vis_img.save(save_img_p, quality=100, subsampling=0) | |
| print(f"======Processing End {task}======") | |
| def process_single_img_tasks_no_labels(task_list, BASE_DIR): | |
| for task in task_list: | |
| print(f"======Processing {task}======") | |
| qa = json.load(open(os.path.join(BASE_DIR, "tasks", f"{task}.json"), 'r', encoding='utf-8')) | |
| for d in qa: | |
| assert (d["metadata"]["data_type"] == "single_image") and (len(d["metadata"]["data_resources"]) == 1) | |
| raw_img_p = os.path.join(BASE_DIR, d["metadata"]["data_resources"][0]["path"].replace("annotated", "raw")) | |
| vis_img = Visualize( | |
| image=Image.open(raw_img_p), | |
| annotations=d["target_entities"], | |
| show_labels=False, | |
| ) | |
| save_img_p = os.path.join(BASE_DIR, d["metadata"]["data_resources"][0]["path"]) | |
| os.makedirs(os.path.dirname(save_img_p), exist_ok=True) | |
| vis_img.save(save_img_p, quality=100, subsampling=0) | |
| print(f"======Processing End {task}======") | |
| def process_single_img_tasks_w_labels(task_list, BASE_DIR): | |
| for task in task_list: | |
| print(f"======Processing Start {task}======") | |
| qa = json.load(open(os.path.join(BASE_DIR, "tasks", f"{task}.json"), 'r', encoding='utf-8')) | |
| for d in qa: | |
| assert (d["metadata"]["data_type"] == "single_image") and (len(d["metadata"]["data_resources"]) == 1) | |
| raw_img_p = os.path.join(BASE_DIR, d["metadata"]["data_resources"][0]["path"].replace("annotated", "raw")) | |
| vis_img = Visualize( | |
| image=Image.open(raw_img_p), | |
| annotations=d["target_entities"], | |
| show_labels=True, | |
| ) | |
| save_img_p = os.path.join(BASE_DIR, d["metadata"]["data_resources"][0]["path"]) | |
| os.makedirs(os.path.dirname(save_img_p), exist_ok=True) | |
| vis_img.save(save_img_p, quality=100, subsampling=0) | |
| print(f"======Processing End {task}======") | |
| if __name__ == "__main__": | |
| process_single_img_tasks_no_labels(single_img_tasks_no_labels, BASE_DIR) | |
| process_single_img_tasks_w_labels(single_img_tasks_w_labels, BASE_DIR) | |
| process_multi_img_tasks_no_labels(multi_img_tasks_no_labels, BASE_DIR) | |
| process_multi_img_tasks_w_labels(multi_img_tasks_w_labels, BASE_DIR) |