File size: 5,161 Bytes
a53be45 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 | import matplotlib.pyplot as plt
import requests
from io import BytesIO
from PIL import Image
import numpy as np
from maskrcnn_benchmark.config import cfg
from maskrcnn_benchmark.engine.predictor_glip import GLIPDemo
import os
from collections import defaultdict
import json
from tqdm import tqdm
import sys
import pdb
sys.path.append("../../")
from utils import *
def blockPrint():
sys.stdout = open(os.devnull, 'w')
# Restore
def enablePrint():
sys.stdout = sys.__stdout__
def load(dir):
"""
Given an url of an image, downloads the image and
returns a PIL image
"""
pil_image = Image.open(dir).convert("RGB")
# convert to BGR format
image = np.array(pil_image)[:, :, [2, 1, 0]]
return image
def imshow(img, caption):
plt.imshow(img[:, :, [2, 1, 0]])
plt.axis("off")
plt.figtext(0.5, 0.09, caption, wrap=True, horizontalalignment='center', fontsize=20)
def list_grid(list1, list2):
return [(l1, l2) for l1 in list1 for l2 in list2]
def eval_image(bbox_by_entities, img_gt):
bbox1 = [coord for _, coord in bbox_by_entities[img_gt['obj1'][0]]]
bbox2 = [coord for _, coord in bbox_by_entities[img_gt['obj2'][0]]]
gt_rel = img_gt['relation']
all_relations = []
if len(bbox1) != 0 and len(bbox2) != 0:
for b1, b2 in list_grid(bbox1, bbox2):
relation = eval_spatial_relation(b1, b2)
all_relations.append(relation)
if gt_rel in all_relations:
return True
if gt_rel == 'next to' and ('left' in all_relations or 'right' in all_relations):
return True
return False
return False
if __name__ == '__main__':
from argparse import ArgumentParser
parser = ArgumentParser()
parser.add_argument("-d", "--dir", type=str)
parser.add_argument("-t", "--thresh", type=float, default=0.7)
parser.add_argument("--annotations", type=str, default="../../dataset/NSR-1K/spatial/spatial.val.json")
parser.add_argument("--output_dir", type=str, default="spatial")
args = parser.parse_args()
with open(args.annotations, "r") as file:
gt = json.load(file)
gt = {d['id']: d for d in gt}
folder = args.output_dir
os.makedirs(f"outputs/{folder}/{args.thresh}", exist_ok=True)
result_file = f"outputs/{folder}/GLIP{args.thresh}_results.json"
image_names = sorted(os.listdir(args.dir))
if not os.path.exists(result_file):
# start loading GLIP
config_file = "configs/pretrain/glip_Swin_L.yaml"
weight_file = "MODEL/glip_large_model.pth"
# update the config options with the config file
# manual override some options
cfg.local_rank = 0
cfg.num_gpus = 1
cfg.merge_from_file(config_file)
cfg.merge_from_list(["MODEL.WEIGHT", weight_file])
cfg.merge_from_list(["MODEL.DEVICE", "cuda"])
glip_demo = GLIPDemo(
cfg,
min_image_size=800,
confidence_threshold=0.7,
show_mask_heatmaps=False
)
plus = 1 if glip_demo.cfg.MODEL.RPN_ARCHITECTURE == "VLDYHEAD" else 0
grounding_results = {}
blockPrint()
n_correct = 0
for file in tqdm(image_names):
image = load(os.path.join(args.dir, file))
image_id, n_iter = [int(x) for x in os.path.splitext(file)[0].split("_")]
caption = f"{gt[image_id]['obj1'][0]}, {gt[image_id]['obj2'][0]}"
result, top_predictions = glip_demo.run_on_web_image(image, caption, args.thresh)
fig = plt.figure(figsize=(5,5))
plt.imshow(result[:, :, [2, 1, 0]])
plt.axis("off")
plt.tight_layout()
plt.savefig(f"outputs/{folder}/{args.thresh}/{file}")
plt.close()
scores = top_predictions.get_field("scores")
labels = top_predictions.get_field("labels")
bbox = top_predictions.bbox
entities = glip_demo.entities
new_labels = []
for i in labels:
if i <= len(entities):
new_labels.append(entities[i-plus])
else:
new_labels.append("object")
bbox_by_entities = defaultdict(list)
for l, score, coord in zip(new_labels, scores, bbox):
bbox_by_entities[l.strip()].append((score.item(), coord.tolist()))
grounding_results[file] = bbox_by_entities
n_correct += 1 if eval_image(bbox_by_entities, gt[image_id]) else 0
with open(result_file, "w") as file:
json.dump(grounding_results, file, indent=4, separators=(",",":"), sort_keys=True)
else:
n_correct = 0
grounding_results = json.load(open(result_file, "r"))
for file in tqdm(image_names):
image_id, n_iter = [int(x) for x in os.path.splitext(file)[0].split("_")]
bbox_by_entities = grounding_results[file]
n_correct += 1 if eval_image(bbox_by_entities, gt[image_id]) else 0
enablePrint()
print(folder, " Spatial Accuracy: {:.04f}".format(n_correct / len(image_names)))
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