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plant_instances2panoptic.py
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plant_instances2panoptic.py
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import cv2
import numpy as np
import os
import json
from PIL import Image
from pycocotools import mask
import datetime
ROOT_DIR = '/data/PhenoBench/'
OUT_DIR = '/data/PhenoBench/annotations/'
INFO = {
"description": "plant panoptic segmentation dataset",
"url": "http",
"version" : "0.2.0",
"year": 2023,
"contributor": "little bai",
"date_created": datetime.datetime.utcnow().isoformat(' ')
}
LICENSES = [
{
"id": 1,
"name": "license_001",
"url" : "http://ipiu.xidian.edu.cn"
},
{
"id": 2,
"name": "license_002",
"url" : "http://ipiu.xidian.edu.cn"
}
]
CATEGORIES = [
{
'supercategory':"crop_parent", 'isthing':1, 'id':1, 'name': 'crop'
},
{
'supercategory':"weed_parent", 'isthing':0, 'id':2, 'name':'weed'
}
]
# create img info for annotations
def create_image_info(image_id, file_name, image_size,
date_captured=datetime.datetime.utcnow().isoformat(' '),
license_id=1, coco_url="", flickr_url=""):
image_info = {
"id": image_id,
"file_name": file_name,
"width": image_size[0],
"height": image_size[1],
"date_captured": date_captured,
"license": license_id,
"coco_url": coco_url,
"flickr_url": flickr_url
}
return image_info
# create segmentation_info fo annotation
def create_segmentation_info(segmentation_id, category_id, iscrowd, bounding_box=None, area=None):
segmentation_info = {
"id": segmentation_id,
"category_id":category_id,
"iscrowd": iscrowd,
"bbox": list(map(int, bounding_box)),
'area': int(area)
}
# print(type(segmentation_info["bbox"]))
# print(type(segmentation_info["bbox"][0]))
# print(type(segmentation_info["area"]))
return segmentation_info
def ins_sem_2_panoptic():
set_names = ['train', 'val']
for setname in set_names:
coco_output = {
"info" : INFO,
"licenses": LICENSES,
"images":[],
"annotations":[],
"categories":CATEGORIES
}
save_panoptic_dir = os.path.join(OUT_DIR, 'panoptic_plant_{}'.format(setname))
if not os.path.exists(save_panoptic_dir):
os.makedirs(save_panoptic_dir)
img_id = 1
img_dir = os.path.join(ROOT_DIR, setname, 'images')
img_name_list = sorted(os.listdir(img_dir))
for img_name in img_name_list:
img = Image.open(os.path.join(img_dir, img_name))
img_info = create_image_info(img_id, img_name, img.size)
coco_output["images"].append(img_info)
annotation_info = {
"segments_info": [],
"file_name": img_name,
"image_id": img_id
}
sem_dir = os.path.join(ROOT_DIR, setname, 'semantics', img_name)
ins_dir = os.path.join(ROOT_DIR, setname, 'plant_instances', img_name)
sem_array = np.array(cv2.imread(sem_dir, cv2.CV_16UC1))
ins_array = np.array(cv2.imread(ins_dir, cv2.CV_16UC1))
sem_array[sem_array == 3] = 1
sem_array[sem_array == 4] = 2
panoptic_img = np.zeros((1024, 1024), dtype=np.uint8)
weed = 100
panoptic_img[sem_array == 2] = weed
mask_ins = np.zeros((1024, 1024), dtype=np.uint8)
mask_ins[sem_array == 2] = 1
category_id = 2
binary_mask_encoded = mask.encode(np.asfortranarray(mask_ins))
area = mask.area(binary_mask_encoded)
bounding_box = mask.toBbox(binary_mask_encoded)
segmentation_id = weed + weed * 256 + weed * 256^2
segmentation_info = create_segmentation_info(segmentation_id=segmentation_id, category_id=category_id , iscrowd=0, bounding_box=bounding_box, area=area)
annotation_info["segments_info"].append(segmentation_info)
ins_array[sem_array == 2] = 0
index = np.unique(ins_array)
start = 180
for idx in index:
if idx == 0:
continue
panoptic_img[ins_array == idx] = start
mask_ins = np.zeros((1024, 1024), dtype=np.uint8)
mask_ins[ins_array == idx] = 1
category_id = 1
binary_mask_encoded = mask.encode(np.asfortranarray(mask_ins))
area = mask.area(binary_mask_encoded)
bounding_box = mask.toBbox(binary_mask_encoded)
segmentation_id = start + start * 256 + start * 256^2
segmentation_info = create_segmentation_info(segmentation_id=segmentation_id, category_id=category_id , iscrowd=0, bounding_box=bounding_box, area=area)
annotation_info["segments_info"].append(segmentation_info)
start += 2
coco_output["annotations"].append(annotation_info)
panoptic_img = Image.fromarray(panoptic_img)
panoptic_rgb = panoptic_img.convert(mode='RGB')
panoptic_rgb.save(os.path.join(save_panoptic_dir, img_name), quality=95)
img_id += 1
panoptic_json = json.dumps(coco_output)
save_json_dir = os.path.join(OUT_DIR, 'panoptic_plant_{}.json'.format(setname))
with open(save_json_dir, 'w') as f:
f.write(panoptic_json)
f.close()
if __name__=='__main__':
ins_sem_2_panoptic()