def coco_eval(result_files, result_types, coco, max_dets=(100, 300, 1000)):
for res_type in result_types:
assert res_type in [
'proposal', 'proposal_fast', 'bbox', 'segm', 'keypoints'
]
if mmcv.is_str(coco):
coco = COCO(coco)
assert isinstance(coco, COCO)
if result_types == ['proposal_fast']:
ar = fast_eval_recall(result_files, coco, np.array(max_dets))
for i, num in enumerate(max_dets):
print('AR@{}\t= {:.4f}'.format(num, ar[i]))
return
for res_type in result_types:
result_file = result_files[res_type]
# assert result_file.endswith('.json')
coco_dets = coco.loadRes(result_file)
img_ids = coco.getImgIds()
iou_type = 'bbox' if res_type == 'proposal' else res_type
cocoEval = COCOeval(coco, coco_dets, iou_type)
cocoEval.params.imgIds = img_ids
if res_type == 'proposal':
cocoEval.params.useCats = 0
cocoEval.params.maxDets = list(max_dets)
cocoEval.evaluate()
cocoEval.accumulate()
cocoEval.summarize()
def coco_process():
"""
Process COCO Images
"""
# Define COCO Data to Gather
dataType = args.dataset + '2017'
annFile = '{}/annotations/person_keypoints_{}.json'.format(
args.dataDir, dataType)
# Remove Current h5 File from Output
if os.path.exists(args.output):
os.remove(args.output)
# initialize COCO api for person keypoints annotations
coco = COCO(annFile)
# get all images containing given categories, select one at random
catIds = coco.getCatIds(catNms=['person'])
imgIds = coco.getImgIds(catIds=catIds)
# Joint names
with open(annFile) as f:
json_data = json.load(f)
keypoint_names = json_data['categories'][0]['keypoints']
# Open h5 File
h5f = h5py.File(args.output, 'a')
start_time = time.time()
# for all iterations
for count in range(min(args.cnt, len(imgIds))):
# load image + keypoints
I, img = load(annFile, coco, imgIds, dataType, count)
# load annotations
annIds = coco.getAnnIds(imgIds=img['id'], catIds=catIds, iscrowd=None)
anns = coco.loadAnns(annIds)
# Create Dataframe
df = keypoints_df(anns)
# If Img has any Keypoints
if df is not None:
# Scale Img and Keypoints
im_scaled, df_scaled = scale(I, df)
# Write to h5
h5_write(img, h5f, df_scaled, im_scaled)
# If testing, test all and exit
if args.testing == True:
h5f.close()
coco_test_img(df, df_scaled, im_scaled, I, coco, anns)
h5_attributes(keypoint_names)
coco_test_h5()
exit()
else:
# Disregard image with no keypoints
pass
if count % 100 == 0:
print('{} Samples Done - {} Taken'.format(
count,
(str(datetime.timedelta(seconds=(time.time() - start_time))))))
return keypoint_names
class CocoDataset(CustomDataset):
CLASSES = ('person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus',
'train', 'truck', 'boat', 'traffic_light', 'fire_hydrant',
'stop_sign', 'parking_meter', 'bench', 'bird', 'cat', 'dog',
'horse', 'sheep', 'cow', 'elephant', 'bear', 'zebra', 'giraffe',
'backpack', 'umbrella', 'handbag', 'tie', 'suitcase', 'frisbee',
'skis', 'snowboard', 'sports_ball', 'kite', 'baseball_bat',
'baseball_glove', 'skateboard', 'surfboard', 'tennis_racket',
'bottle', 'wine_glass', 'cup', 'fork', 'knife', 'spoon', 'bowl',
'banana', 'apple', 'sandwich', 'orange', 'broccoli', 'carrot',
'hot_dog', 'pizza', 'donut', 'cake', 'chair', 'couch',
'potted_plant', 'bed', 'dining_table', 'toilet', 'tv', 'laptop',
'mouse', 'remote', 'keyboard', 'cell_phone', 'microwave',
'oven', 'toaster', 'sink', 'refrigerator', 'book', 'clock',
'vase', 'scissors', 'teddy_bear', 'hair_drier', 'toothbrush')
def load_annotations(self, ann_file):
self.coco = COCO(ann_file)
self.cat_ids = self.coco.getCatIds()
self.cat2label = {
cat_id: i + 1
for i, cat_id in enumerate(self.cat_ids)
}
self.img_ids = self.coco.getImgIds()
img_infos = []
for i in self.img_ids:
info = self.coco.loadImgs([i])[0]
info['filename'] = info['file_name']
img_infos.append(info)
return img_infos
def get_ann_info(self, idx):
img_id = self.img_infos[idx]['id']
ann_ids = self.coco.getAnnIds(imgIds=[img_id])
ann_info = self.coco.loadAnns(ann_ids)
return self._parse_ann_info(ann_info, self.with_mask)
def _filter_imgs(self, min_size=32):
"""Filter images too small or without ground truths."""
valid_inds = []
ids_with_ann = set(_['image_id'] for _ in self.coco.anns.values())
for i, img_info in enumerate(self.img_infos):
if self.img_ids[i] not in ids_with_ann:
continue
if min(img_info['width'], img_info['height']) >= min_size:
valid_inds.append(i)
return valid_inds
def _parse_ann_info(self, ann_info, with_mask=True):
"""Parse bbox and mask annotation.
Args:
ann_info (list[dict]): Annotation info of an image.
with_mask (bool): Whether to parse mask annotations.
Returns:
dict: A dict containing the following keys: bboxes, bboxes_ignore,
labels, masks, mask_polys, poly_lens.
"""
gt_bboxes = []
gt_labels = []
gt_bboxes_ignore = []
# Two formats are provided.
# 1. mask: a binary map of the same size of the image.
# 2. polys: each mask consists of one or several polys, each poly is a
# list of float.
if with_mask:
gt_masks = []
gt_mask_polys = []
gt_poly_lens = []
for i, ann in enumerate(ann_info):
if ann.get('ignore', False):
continue
x1, y1, w, h = ann['bbox']
if ann['area'] <= 0 or w < 1 or h < 1:
continue
bbox = [x1, y1, x1 + w - 1, y1 + h - 1]
if ann['iscrowd']:
gt_bboxes_ignore.append(bbox)
else:
gt_bboxes.append(bbox)
gt_labels.append(self.cat2label[ann['category_id']])
if with_mask:
gt_masks.append(self.coco.annToMask(ann))
mask_polys = [
p for p in ann['segmentation'] if len(p) >= 6
] # valid polygons have >= 3 points (6 coordinates)
poly_lens = [len(p) for p in mask_polys]
gt_mask_polys.append(mask_polys)
gt_poly_lens.extend(poly_lens)
if gt_bboxes:
gt_bboxes = np.array(gt_bboxes, dtype=np.float32)
gt_labels = np.array(gt_labels, dtype=np.int64)
else:
gt_bboxes = np.zeros((0, 4), dtype=np.float32)
gt_labels = np.array([], dtype=np.int64)
if gt_bboxes_ignore:
gt_bboxes_ignore = np.array(gt_bboxes_ignore, dtype=np.float32)
else:
gt_bboxes_ignore = np.zeros((0, 4), dtype=np.float32)
ann = dict(
bboxes=gt_bboxes, labels=gt_labels, bboxes_ignore=gt_bboxes_ignore)
if with_mask:
ann['masks'] = gt_masks
# poly format is not used in the current implementation
ann['mask_polys'] = gt_mask_polys
ann['poly_lens'] = gt_poly_lens
return ann
"""
Created on Sat Feb 22 16:58:58 2020
@author: zhangyiqian
"""
from cocoapi.PythonAPI.pycocotools.coco import COCO
import numpy as np
from skimage.io import imread, imshow, imsave
import matplotlib.pyplot as plt
from skimage import draw
import random
import cv2 as cv
import time
annFile = '/home/yiqian/Documents/dataset/COCO/annotations_valstuff/stuff_val2017.json'
coco = COCO(annFile)
imgIds = coco.getImgIds()
img = coco.loadImgs(imgIds[0])[0]
I = imread(img['coco_url'])
plt.figure()
imshow(I)
# catIds = coco.getCatIds(catNms=['person']);
annIds = coco.getAnnIds(imgIds=img['id'],
catIds=coco.getCatIds(),
iscrowd=False)
# annIds = coco.getAnnIds(imgIds=img['id'], catIds=catIds, iscrowd=False)
anns = coco.loadAnns(annIds)
coco.showAnns(anns)
# sample scribble areas based on uniform distribution
# sample_area_num = np.random.randint(1, len(anns)+1)
def read_data(data_dir, image_size, pixels_per_grid=32, no_label=False):
"""
Load the data and preprocessing for YOLO detector
:param data_dir: str, path to the directory to read. It should include class_map, anchors, annotations
:image_size: tuple, image size for resizing images
:pixels_per_gird: int, the actual size of a grid
:no_label: bool, whetehr to load labels
:return: X_set: np.ndarray, shape: (N, H, W, C).
y_set: np.ndarray, shape: (N, g_H, g_W, anchors, 5 + num_classes).
"""
im_dir = os.path.join(data_dir, 'val2017')
class_map_path = '/home/intern/data/coco/classes_coco.json'
# class_map_path = os.path.join(data_dir, 'classes.json')
#anchors_path = os.path.join(data_dir, 'anchors.json')
class_map = load_json(class_map_path)
anchors = load_json('/home/intern/data/face/train/anchors.json')
#anchors = load_json(anchors_path)
num_classes = len(class_map)
grid_h, grid_w = [image_size[i] // pixels_per_grid for i in range(2)]
im_paths = []
for ext in IM_EXTENSIONS:
im_paths.extend(glob.glob(os.path.join(im_dir, '*.{}'.format(ext))))
#anno_dir = os.path.join(data_dir, 'annotations')
images = []
labels = []
###########################################################################
dataDir='/home/intern/data/coco'
dataType='val2017'
annFile='{}/annotations/instances_{}.json'.format(dataDir,dataType)
coco=COCO(annFile)
all_file_name = coco.getImgIds()#int type
###########################################################################
for im_path in im_paths:
# load image and resize image
im = imread(im_path)
#print(im_path)
im = np.array(im, dtype=np.float32)
im_origina_sizes = im.shape[:2]
im = resize(im, (image_size[1], image_size[0]))
if len(im.shape) == 2:
im = np.expand_dims(im, 2)
im = np.concatenate([im, im, im], -1)
images.append(im)
if no_label:
labels.append(0)
continue
# load bboxes and reshape for yolo model
name = os.path.splitext(os.path.basename(im_path))[0]
###########################################################################
name_nonzero_int = int(name.lstrip('0'))#str type -> int(remove '0')
real_anno = {}
annos_one_img = coco.getAnnIds(imgIds=[name_nonzero_int])#annotations one img
for i in range(0,len(annos_one_img)):
anno_contents = coco.loadAnns(ids=[annos_one_img[i]])#info of one anno->type: [{}]
anno_contents[0]['category_id']
category_name = coco.loadCats(ids=[anno_contents[0]['category_id']])[0]['name']#name
bbbox = anno_contents[0]['bbox']#bbox [x,y,w,h]
trans_bbbox = [bbbox[0], bbbox[1], bbbox[0]+bbbox[2], bbbox[1]+bbbox[3]]#[x_min,y_min,x_max,y_max]
if category_name in real_anno:
real_anno[category_name].append(trans_bbbox)
else:
real_anno[category_name] = [trans_bbbox]
###########################################################################
#anno_path = os.path.join(anno_dir, '{}.anno'.format(name))
#anno = load_json(anno_path)
anno = real_anno
#print(anno)
label = np.zeros((grid_h, grid_w, len(anchors), 5 + num_classes))
for c_idx, c_name in class_map.items():
if c_name not in anno:
continue
#print(anno[c_name])
for x_min, y_min, x_max, y_max in anno[c_name]:
oh, ow = im_origina_sizes
# normalize object coordinates and clip the values
x_min, y_min, x_max, y_max = x_min / ow, y_min / oh, x_max / ow, y_max / oh
x_min, y_min, x_max, y_max = np.clip([x_min, y_min, x_max, y_max], 0, 1)
# assign the values to the best anchor
anchor_boxes = np.array(anchors) / np.array([ow, oh])
best_anchor = get_best_anchor(
anchor_boxes, [x_max - x_min, y_max - y_min])
cx = int(np.floor(0.5 * (x_min + x_max) * grid_w))
cy = int(np.floor(0.5 * (y_min + y_max) * grid_h))
label[cy, cx, best_anchor, 0:4] = [x_min, y_min, x_max, y_max]
label[cy, cx, best_anchor, 4] = 1.0
label[cy, cx, best_anchor, 5 + int(c_idx)] = 1.0
labels.append(label)
X_set = np.array(images, dtype=np.float32)
y_set = np.array(labels, dtype=np.float32)
return X_set, y_set