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gpt_rewrite_label.py
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import openai
from openai import OpenAI
import argparse
import json
import re
import os
import time
from tqdm import tqdm
from Dataset import ADE20K_GPT, PascalVOC_GPT, PascalContext_GPT,, CocoDetection_GPT
import torch
from argparse import Namespace
import base64
from PIL import Image
import numpy as np
parser = argparse.ArgumentParser()
parser.add_argument("--apikey", type=str, required=True)
# parser.add_argument("--input_path", type=str, required=True)
parser.add_argument("--output_dir", type=str, required=True)
# parser.add_argument("--rewritten_file", type=str, required=True)
parser.add_argument("--data_type", type=str, required=True)
args = parser.parse_args()
client = OpenAI(api_key=args.apikey, timeout=60.0)
def getClassName(class_id, cats):
for i in range(len(cats)):
# print("234 get classname", cats[i]['id']==class_id, type(cats[i]['id']), type(class_id))
if cats[i][0] == class_id:
return cats[i][1]
return "None"
print(args.data_type)
args.rewritten_file = f'{args.data_type}_classification_noboundary'
if args.data_type == "coco_object":
from pycocotools.coco import COCO
cats = [[ 1, 'person'], [ 2, 'bicycle'],[ 3, 'car'], [ 4, 'motorcycle'],
[ 5, 'airplane'], [ 6, 'bus'],[ 7, 'train'], [ 8, 'truck'],
[ 9, 'boat'], [10, 'traffic light'], [ 11,'fire hydrant'], [13, 'stop sign'], [ 14,'parking meter'],
[15, 'bench'], [16, 'bird'], [17, 'cat'], [18, 'dog'], [19, 'horse'], [20, 'sheep'], [21, 'cow'], [22,'elephant'], [23,'bear'],
[24, 'zebra'], [25,'giraffe'], [27, 'backpack'], [28, 'umbrella'], [ 31,'handbag'], [ 32, 'tie'], [33, 'suitcase'],
[ 34,'frisbee'], [ 35, 'skis'], [36, 'snowboard'], [ 37, 'sports ball'], [ 38, 'kite'], [39, 'baseball bat'],
[ 40, 'baseball glove'], [41, 'skateboard'], [ 42, 'surfboard'], [43, 'tennis racket'], [44, 'bottle'], [46,'wine glass'], [ 47, 'cup'],
[48, 'fork'], [49, 'knife'], [50, 'spoon'], [51, 'bowl'], [ 52, 'banana'],
[53, 'apple'], [ 54, 'sandwich'], [55, 'orange'], [ 56, 'broccoli'], [57, 'carrot'], [ 58, 'hot dog'],
[59, 'pizza'], [ 60, 'donut'], [61, 'cake'], [ 62, 'chair'], [63, 'couch'], [ 64, 'potted plant'],
[65, 'bed'], [ 67,'dining table'], [ 70, 'toilet'], [72, 'tv'], [ 73, 'laptop'], [ 74, 'mouse'],
[75, 'remote'], [ 76,'keyboard'], [77, 'cell phone'], [ 78, 'microwave'],
[79, 'oven'], [ 80, 'toaster'], [81, 'sink'], [82, 'refrigerator'], [ 84,'book'], [ 85,'clock'],
[ 86, 'vase'], [ 87,'scissors'], [ 88, 'teddy bear'], [89, 'hair drier'], [ 90, 'toothbrush']]
print(cats)
nms = [cat[1] for cat in cats]
imageDir = '/home/letitiabanana/LAVIS/coco/images/val2017'
dataType_thing = 'val2017'
annFile_thing = '/home/letitiabanana/LAVIS/coco/annotations/instances_{}.json'.format(dataType_thing)
coco_thing = COCO(annFile_thing)
test_data = CocoDetection_GPT(imageDir, args, annFile_thing, cats) ##add arguments
torch.manual_seed(10000)
data_loader_test = torch.utils.data.DataLoader(
test_data,
batch_size=1,
drop_last=False,
shuffle=False,
)
elif args.data_type == "coco_stuff":
from pycocotools.coco import COCO
cats =[[ 1,'person'], [ 2, 'bicycle'], [ 3, 'car'], [ 4, 'motorcycle'], [ 5, 'airplane'], [ 6, 'bus'], [ 7, 'train'], [ 8, 'truck'], [ 9, 'boat'], [ 10, 'traffic light'], [ 11, 'fire hydrant'], [ 13, 'stop sign'], [ 14, 'parking meter'], [ 15, 'bench'], [16, 'bird'], [17, 'cat'], [18, 'dog'], [19, 'horse'], [20, 'sheep'], [21, 'cow'], [22, 'elephant'], [23, 'bear'], [24, 'zebra'], [25, 'giraffe'], [ 27, 'backpack'], [ 28, 'umbrella'], [ 31, 'handbag'], [ 32, 'tie'], [ 33, 'suitcase'], [ 34, 'frisbee'], [ 35, 'skis'], [ 36, 'snowboard'], [ 37, 'sports ball'], [ 38, 'kite'], [ 39, 'baseball bat'], [ 40, 'baseball glove'], [ 41, 'skateboard'], [ 42, 'surfboard'], [ 43, 'tennis racket'], [44, 'bottle'], [46, 'wine glass'], [47, 'cup'], [48, 'fork'], [49, 'knife'], [50, 'spoon'], [51, 'bowl'], [ 52, 'banana'], [ 53, 'apple'], [ 54, 'sandwich'], [ 55, 'orange'], [ 56, 'broccoli'], [ 57, 'carrot'], [ 58, 'hot dog'], [ 59, 'pizza'], [ 60, 'donut'], [ 61, 'cake'], [ 62, 'chair'], [ 63, 'couch'], [ 64, 'potted plant'], [ 65, 'bed'], [ 67, 'dining table'], [ 70, 'toilet'], [ 72, 'tv'], [ 73, 'laptop'], [ 74, 'mouse'], [ 75, 'remote'], [ 76, 'keyboard'], [ 77, 'cell phone'], [ 78, 'microwave'], [ 79, 'oven'], [ 80, 'toaster'], [ 81, 'sink'], [ 82, 'refrigerator'], [ 84, 'book'], [ 85, 'clock'], [ 86, 'vase'], [ 87, 'scissors'], [ 88, 'teddy bear'], [ 89, 'hair drier'], [ 90, 'toothbrush'], [ 92, 'banner'], [ 93, 'blanket'], [94, 'branch'], [ 95, 'bridge'], [ 96, 'building-other'], [97, 'bush'], [ 98, 'cabinet'], [99, 'cage'], [ 100, 'cardboard'], [ 101, 'carpet'], [102, 'ceiling-other'], [103, 'ceiling-tile'], [ 104, 'cloth'], [ 105, 'clothes'], [ 106, 'clouds'], [ 107, 'counter'], [ 108, 'cupboard'], [ 109, 'curtain'], [ 110, 'desk-stuff'], [111, 'dirt'], [ 112, 'door-stuff'], [113, 'fence'], [ 114, 'floor-marble'], [ 115, 'floor-other'], [ 116, 'floor-stone'], [ 117, 'floor-tile'], [ 118, 'floor-wood'], [119, 'flower'], [120, 'fog'], [121, 'food-other'], [122, 'fruit'], [ 123, 'furniture-other'], [124, 'grass'], [125, 'gravel'], [126, 'ground-other'], [ 127, 'hill'], [ 128, 'house'], [129, 'leaves'], [ 130, 'light'], [ 131, 'mat'], [ 132, 'metal'], [ 133, 'mirror-stuff'], [134, 'moss'], [ 135, 'mountain'], [136, 'mud'], [ 137, 'napkin'], [138, 'net'], [ 139, 'paper'], [140, 'pavement'], [ 141, 'pillow'], [142, 'plant-other'], [ 143, 'plastic'], [144, 'platform'], [145, 'playingfield'], [146, 'railing'], [147, 'railroad'], [148, 'river'], [149, 'road'], [ 150, 'rock'], [ 151, 'roof'], [ 152, 'rug'], [153, 'salad'], [154, 'sand'], [155, 'sea'], [ 156, 'shelf'], [ 157, 'sky-other'], [ 158, 'skyscraper'], [159, 'snow'], [ 160, 'solid-other'], [ 161, 'stairs'], [ 162, 'stone'], [163, 'straw'], [164, 'structural-other'], [ 165, 'table'], [ 166, 'tent'], [ 167, 'textile-other'], [ 168, 'towel'], [169, 'tree'], [170, 'vegetable'], [ 171, 'wall-brick'], [ 172, 'wall-concrete'], [ 173, 'wall-other'], [ 174, 'wall-panel'], [ 175, 'wall-stone'], [ 176, 'wall-tile'], [ 177, 'wall-wood'], [178, 'water-other'], [179, 'waterdrops'], [ 180, 'window-blind'], [ 181, 'window-other'], [ 182, 'wood']]
print(cats)
nms = [cat[1] for cat in cats]
imageDir = '/home/letitiabanana/LAVIS/coco/images/val2017'
dataType_thing = 'val2017'
annFile_thing = '/home/letitiabanana/LAVIS/coco/annotations/instances_{}.json'.format(dataType_thing)
coco_thing = COCO(annFile_thing)
test_data = CocoDetection_GPT(imageDir, args, annFile_thing, cats) ##add arguments
torch.manual_seed(10000)
data_loader_test = torch.utils.data.DataLoader(
test_data,
batch_size=1,
drop_last=False,
shuffle=False,
)
elif args.data_type == "psc":
cats = {1: 'aeroplane', 2:'bag',3 :'bed', 4:'bedclothes',
5:'bench', 6:'bicycle', 7:'bird', 8:'boat', 9:'book', 10:'bottle',
11:'building',12 :'bus', 13:'cabinet', 14:'car', 15:'cat', 16:'ceiling',
17:'chair',18 :'cloth', 19:'computer', 20:'cow', 21:'cup', 22:'curtain',23 :'dog',
24:'door', 25:'fence', 26:'floor', 27:'flower', 28:'food', 29:'grass', 30:'ground',
31:'horse', 32:'keyboard', 33:'light', 34:'motorbike', 35:'mountain',
36:'mouse', 37:'person', 38:'plate', 39:'platform', 40:'pottedplant', 41:'road',
42:'rock', 43:'sheep', 44:'shelves', 45:'sidewalk',46: 'sign', 47:'sky', 48:'snow',
49:'sofa', 50:'table', 51:'track', 52:'train', 53:'tree', 54:'truck',
55:'tvmonitor', 56:'wall', 57:'water', 58:'window', 59:'wood'}
nms = ["".join(i.split(" ")) for i in cats.values()]
imageDir = "/home/letitiabanana/LAVIS/mmsegmentation/data/VOCdevkit/VOC2010"
test_data = PascalContext_GPT(imageDir, split="val", args=args) ##add arguments args, annFile_thing, getClassName, cats, vis_processors_clip
torch.manual_seed(10000)
data_loader_test = torch.utils.data.DataLoader(
test_data,
batch_size=1,
drop_last=False,
shuffle=False,
)
elif args.data_type == "voc":
cats = {1: "aeroplane", 2: "bicycle", 3: "bird", 4: "boat", 5: "bottle", 6: "bus", 7: "car", 8: "cat", 9: "chair",
10: "cow", 11: "table", 12: "dog", 13: "horse", 14: "motorbike", 15: "person",
16: "pottedplant", 17: "sheep", 18: "sofa", 19: "train", 20: "tvmonitor"}
nms = ["".join(i.split(" ")) for i in cats.values()]
imageDir = "/home/letitiabanana/LAVIS/VOCdevkit/VOC2012/"
test_data = PascalVOC_GPT(imageDir, split="val", args=args) ##add arguments args, annFile_thing, getClassName, cats, vis_processors_clip
torch.manual_seed(10000)
data_loader_test = torch.utils.data.DataLoader(
test_data,
batch_size=1,
drop_last=False,
shuffle=False,
)
elif args.data_type == "ade20k":
cats = {1: 'wall', 2: 'building;edifice', 3: 'sky', 4: 'floor;flooring', 5: 'tree', 6: 'ceiling', 7: 'road;route',
8: 'bed', 9: 'windowpane;window', 10: 'grass', 11: 'cabinet', 12: 'sidewalk;pavement',
13: 'person;individual;someone;somebody;mortal;soul', 14: 'earth;ground', 15: 'door;double;door', 16: 'table',
17: 'mountain;mount', 18: 'plant;flora;plant;life', 19: 'curtain;drape;drapery;mantle;pall', 20: 'chair',
21: 'car;auto;automobile;machine;motorcar', 22: 'water', 23: 'painting;picture', 24: 'sofa;couch;lounge',
25: 'shelf', 26: 'house', 27: 'sea', 28: 'mirror', 29: 'rug;carpet;carpeting', 30: 'field', 31: 'armchair',
32: 'seat', 33: 'fence;fencing', 34: 'desk', 35: 'rock;stone', 36: 'wardrobe;closet;press', 37: 'lamp',
38: 'bathtub;bathing;tub;bath;tub', 39: 'railing;rail', 40: 'cushion', 41: 'base;pedestal;stand', 42: 'box',
43: 'column;pillar', 44: 'signboard;sign', 45: 'chest;of;drawers;chest;bureau;dresser', 46: 'counter',
47: 'sand',
48: 'sink', 49: 'skyscraper', 50: 'fireplace;hearth;open;fireplace', 51: 'refrigerator;icebox',
52: 'grandstand;covered;stand', 53: 'path', 54: 'stairs;steps', 55: 'runway',
56: 'case;display;case;showcase;vitrine', 57: 'pool;table;billiard;table;snooker;table', 58: 'pillow',
59: 'screen;door;screen', 60: 'stairway;staircase', 61: 'river', 62: 'bridge;span', 63: 'bookcase',
64: 'blind;screen', 65: 'coffee;table;cocktail;table', 66: 'toilet;can;commode;crapper;pot;potty;stool;throne',
67: 'flower', 68: 'book', 69: 'hill', 70: 'bench', 71: 'countertop',
72: 'stove;kitchen;stove;range;kitchen;range;cooking;stove', 73: 'palm;palm;tree', 74: 'kitchen;island',
75: 'computer;computing;machine;computing;device;data;processor;electronic;computer;information;processing;system',
76: 'swivel;chair', 77: 'boat', 78: 'bar', 79: 'arcade;machine', 80: 'hovel;hut;hutch;shack;shanty',
81: 'bus;autobus;coach;charabanc;double-decker;jitney;motorbus;motorcoach;omnibus;passenger;vehicle',
82: 'towel',
83: 'light;light;source', 84: 'truck;motortruck', 85: 'tower', 86: 'chandelier;pendant;pendent',
87: 'awning;sunshade;sunblind', 88: 'streetlight;street;lamp', 89: 'booth;cubicle;stall;kiosk',
90: 'television;television;receiver;television;set;tv;tv;set;idiot;box;boob;tube;telly;goggle;box',
91: 'airplane;aeroplane;plane', 92: 'dirt;track', 93: 'apparel;wearing;apparel;dress;clothes', 94: 'pole',
95: 'land;ground;soil', 96: 'bannister;banister;balustrade;balusters;handrail',
97: 'escalator;moving;staircase;moving;stairway', 98: 'ottoman;pouf;pouffe;puff;hassock', 99: 'bottle',
100: 'buffet;counter;sideboard', 101: 'poster;posting;placard;notice;bill;card', 102: 'stage', 103: 'van',
104: 'ship', 105: 'fountain', 106: 'conveyer;belt;conveyor;belt;conveyer;conveyor;transporter', 107: 'canopy',
108: 'washer;automatic;washer;washing;machine', 109: 'plaything;toy',
110: 'swimming;pool;swimming;bath;natatorium', 111: 'stool', 112: 'barrel;cask', 113: 'basket;handbasket',
114: 'waterfall;falls', 115: 'tent;collapsible;shelter', 116: 'bag', 117: 'minibike;motorbike', 118: 'cradle',
119: 'oven', 120: 'ball', 121: 'food;solid;food', 122: 'step;stair', 123: 'tank;storage;tank',
124: 'trade;name;brand;name;brand;marque', 125: 'microwave;microwave;oven', 126: 'pot;flowerpot',
127: 'animal;animate;being;beast;brute;creature;fauna', 128: 'bicycle;bike;wheel;cycle', 129: 'lake',
130: 'dishwasher;dish;washer;dishwashing;machine', 131: 'screen;silver;screen;projection;screen',
132: 'blanket;cover', 133: 'sculpture', 134: 'hood;exhaust;hood', 135: 'sconce', 136: 'vase',
137: 'traffic;light;traffic;signal;stoplight', 138: 'tray',
139: 'ashcan;trash;can;garbage;can;wastebin;ash;bin;ash-bin;ashbin;dustbin;trash;barrel;trash;bin', 140: 'fan',
141: 'pier;wharf;wharfage;dock', 142: 'crt;screen', 143: 'plate', 144: 'monitor;monitoring;device',
145: 'bulletin;board;notice;board', 146: 'shower', 147: 'radiator', 148: 'glass;drinking;glass', 149: 'clock',
150: 'flag'}
nms = ["".join(i.split(" ")) for i in cats.values()]
root_dataset = "./"
list_val = "./semantic-segmentation-pytorch-master/data/validation.odgt"
dataset_config = Namespace(num_class=150, padding_constant=32)
test_data = ADE20K_GPT(root_dataset, list_val, dataset_config)
torch.manual_seed(10000)
data_loader_test = torch.utils.data.DataLoader(
test_data,
batch_size=1,
drop_last=False,
shuffle=False
)
save_path = os.path.join(args.output_dir, args.rewritten_file + '.json')
if os.path.exists(save_path):
with open(save_path, 'r') as f:
exist = json.load(f)
start = len(exist)
else:
start = 0
exist = {}
# Open the image file and encode it as a base64 string
def encode_image(image_path):
with open(image_path, "rb") as image_file:
return base64.b64encode(image_file.read()).decode("utf-8")
gens = {}
for idx, (img_path, segm_path) in tqdm(enumerate(data_loader_test)):
img_id = segm_path[0].split("/")[-1].split(".")[0]
if img_id not in exist.keys() or exist[img_id] =='':
print(217, img_id)
# if idx >= start and idx <=3:
if args.data_type == "coco_object":
img_id = segm_path[0].split("/")[-1].split(".")[0]
id_coco = int(img_id.lstrip("0"))
ann_ids_thing = coco_thing.getAnnIds(imgIds=id_coco)
target = coco_thing.loadAnns(ann_ids_thing)
gt_class_name = []
for i in range(len(target)):
if target[i]["category_id"] == 183:
continue
gt_class = getClassName(target[i]["category_id"], cats)
# print("104 gt cls", target[i]["category_id"], gt_class)
if gt_class not in gt_class_name:
gt_class_name.append(gt_class)
elif args.data_type == "coco_stuff":
img_id = segm_path[0].split("/")[-1].split(".")[0]
segm = np.float32(Image.open(segm_path[0])) #0-182，255
gt_idx = np.unique(segm)+1
gt_class_name = []
for i in gt_idx:
if int(i) != 255 and int(i) != 183:
gt_class_name.append(getClassName(i, cats))
else:
img_id = segm_path[0].split("/")[-1].split(".")[0]
segm = np.float32(Image.open(segm_path[0]))
gt_idx = np.unique(segm)
gt_class_name = []
for i in gt_idx:
if i != 0 and i != 255:
gt_class_name.append(cats[i])
base64_image = encode_image(img_path[0])
attempt = 0
no_timeout = False
while attempt < 3:
try:
response = client.chat.completions.create(
model='gpt-4o',
messages=[
{"role": "user", "content": [
{"type": "text", "text": f"Help me find as much and accurate as possible, categoreis appear in the image among the availble categories {cats}. Double check if there is anything missing. You may output unsure categories and give them lower probability, but at most 25 categories "
f"Ouput strictly in the format [id1: classname1, id2: classname2, ...], [probability of class1 in percentage, probability of class2 in percentage, ...] without other words. you have to at least ouput one cats. "},
{"type": "image_url", "image_url": {
"url": f"data:image/png;base64,{base64_image}"}
}
]}
],
temperature=0.0,
)
gen_content = response.choices[0].message.content
gens[img_id] = gen_content
attempt = 3
no_timeout = True
print(293, img_id, gen_content, gt_class_name)
except openai.BadRequestError:
print("BadRequestError")
attempt = 3
gens[img_id] = ''
error_file = os.path.join(args.output_dir, 'no_output_idx.json')
if os.path.exists(error_file):
with open(error_file, 'r') as f:
bad_idx_list = json.load(f)
bad_idx_list.append(img_id)
else:
bad_idx_list = [img_id]
with open(error_file, 'w') as f:
json.dump(bad_idx_list, f)
no_timeout = True
except:
print("timeout")
time.sleep(20)
attempt += 1
try:
print(293, img_id, gen_content, gt_class_name)
except:
error_file = os.path.join(args.output_dir, 'no_return.json')
if os.path.exists(error_file):
with open(error_file, 'r') as f:
bad_idx_list = json.load(f)
bad_idx_list.append(img_id)
else:
bad_idx_list = [img_id]
with open(error_file, 'w') as f:
json.dump(bad_idx_list, f)
if no_timeout is False:
gens[img_id] = ''
error_file = os.path.join(args.output_dir, 'no_output_idx.json')
if os.path.exists(error_file):
with open(error_file, 'r') as f:
bad_idx_list = json.load(f)
bad_idx_list.append(img_id)
else:
bad_idx_list = [img_id]
with open(error_file, 'w') as f:
json.dump(bad_idx_list, f)
# if len(gens) % 2 == 0:
if not os.path.exists(args.output_dir):
os.mkdir(args.output_dir)
if not os.path.exists(save_path):
with open(save_path, 'w') as f:
json.dump(gens, f)
else:
with open(save_path, 'r') as f:
tmp = json.load(f)
tmp.update(gens)
with open(save_path, 'w') as f:
json.dump(tmp, f)
gens = {}