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cornernet.py
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cornernet.py
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import os
import cv2
import json
import numpy as np
import torch
from tqdm import tqdm
from ..utils import Timer
from ..vis_utils import draw_bboxes
from ..sample.utils import crop_image
from ..external.nms import soft_nms, soft_nms_merge
def rescale_dets_(detections, ratios, borders, sizes):
xs, ys = detections[..., 0:4:2], detections[..., 1:4:2]
xs /= ratios[:, 1][:, None, None]
ys /= ratios[:, 0][:, None, None]
xs -= borders[:, 2][:, None, None]
ys -= borders[:, 0][:, None, None]
np.clip(xs, 0, sizes[:, 1][:, None, None], out=xs)
np.clip(ys, 0, sizes[:, 0][:, None, None], out=ys)
def decode(nnet, images, K, ae_threshold=0.5, kernel=3, num_dets=1000):
detections = nnet.test([images], ae_threshold=ae_threshold, test=True, K=K, kernel=kernel, num_dets=num_dets)[0]
return detections.data.cpu().numpy()
def cornernet(db, nnet, result_dir, debug=False, decode_func=decode):
debug_dir = os.path.join(result_dir, "debug")
if not os.path.exists(debug_dir):
os.makedirs(debug_dir)
if db.split != "trainval2014":
db_inds = db.db_inds[:100] if debug else db.db_inds
else:
db_inds = db.db_inds[:100] if debug else db.db_inds[:5000]
num_images = db_inds.size
categories = db.configs["categories"]
timer = Timer()
top_bboxes = {}
for ind in tqdm(range(0, num_images), ncols=80, desc="locating kps"):
db_ind = db_inds[ind]
image_id = db.image_ids(db_ind)
image_path = db.image_path(db_ind)
image = cv2.imread(image_path)
timer.tic()
top_bboxes[image_id] = cornernet_inference(db, nnet, image)
timer.toc()
if debug:
image_path = db.image_path(db_ind)
image = cv2.imread(image_path)
bboxes = {
db.cls2name(j): top_bboxes[image_id][j]
for j in range(1, categories + 1)
}
image = draw_bboxes(image, bboxes)
debug_file = os.path.join(debug_dir, "{}.jpg".format(db_ind))
cv2.imwrite(debug_file, image)
print('average time: {}'.format(timer.average_time))
result_json = os.path.join(result_dir, "results.json")
detections = db.convert_to_coco(top_bboxes)
with open(result_json, "w") as f:
json.dump(detections, f)
cls_ids = list(range(1, categories + 1))
image_ids = [db.image_ids(ind) for ind in db_inds]
db.evaluate(result_json, cls_ids, image_ids)
return 0
def cornernet_inference(db, nnet, image, decode_func=decode):
K = db.configs["top_k"]
ae_threshold = db.configs["ae_threshold"]
nms_kernel = db.configs["nms_kernel"]
num_dets = db.configs["num_dets"]
test_flipped = db.configs["test_flipped"]
input_size = db.configs["input_size"]
output_size = db.configs["output_sizes"][0]
scales = db.configs["test_scales"]
weight_exp = db.configs["weight_exp"]
merge_bbox = db.configs["merge_bbox"]
categories = db.configs["categories"]
nms_threshold = db.configs["nms_threshold"]
max_per_image = db.configs["max_per_image"]
nms_algorithm = {
"nms": 0,
"linear_soft_nms": 1,
"exp_soft_nms": 2
}[db.configs["nms_algorithm"]]
height, width = image.shape[0:2]
height_scale = (input_size[0] + 1) // output_size[0]
width_scale = (input_size[1] + 1) // output_size[1]
im_mean = torch.cuda.FloatTensor(db.mean).reshape(1, 3, 1, 1)
im_std = torch.cuda.FloatTensor(db.std).reshape(1, 3, 1, 1)
detections = []
for scale in scales:
new_height = int(height * scale)
new_width = int(width * scale)
new_center = np.array([new_height // 2, new_width // 2])
inp_height = new_height | 127
inp_width = new_width | 127
images = np.zeros((1, 3, inp_height, inp_width), dtype=np.float32)
ratios = np.zeros((1, 2), dtype=np.float32)
borders = np.zeros((1, 4), dtype=np.float32)
sizes = np.zeros((1, 2), dtype=np.float32)
out_height, out_width = (inp_height + 1) // height_scale, (inp_width + 1) // width_scale
height_ratio = out_height / inp_height
width_ratio = out_width / inp_width
resized_image = cv2.resize(image, (new_width, new_height))
resized_image, border, offset = crop_image(resized_image, new_center, [inp_height, inp_width])
resized_image = resized_image / 255.
images[0] = resized_image.transpose((2, 0, 1))
borders[0] = border
sizes[0] = [int(height * scale), int(width * scale)]
ratios[0] = [height_ratio, width_ratio]
if test_flipped:
images = np.concatenate((images, images[:, :, :, ::-1]), axis=0)
images = torch.from_numpy(images).cuda()
images -= im_mean
images /= im_std
dets = decode_func(nnet, images, K, ae_threshold=ae_threshold, kernel=nms_kernel, num_dets=num_dets)
if test_flipped:
dets[1, :, [0, 2]] = out_width - dets[1, :, [2, 0]]
dets = dets.reshape(1, -1, 8)
rescale_dets_(dets, ratios, borders, sizes)
dets[:, :, 0:4] /= scale
detections.append(dets)
detections = np.concatenate(detections, axis=1)
classes = detections[..., -1]
classes = classes[0]
detections = detections[0]
# reject detections with negative scores
keep_inds = (detections[:, 4] > -1)
detections = detections[keep_inds]
classes = classes[keep_inds]
top_bboxes = {}
for j in range(categories):
keep_inds = (classes == j)
top_bboxes[j + 1] = detections[keep_inds][:, 0:7].astype(np.float32)
if merge_bbox:
soft_nms_merge(top_bboxes[j + 1], Nt=nms_threshold, method=nms_algorithm, weight_exp=weight_exp)
else:
soft_nms(top_bboxes[j + 1], Nt=nms_threshold, method=nms_algorithm)
top_bboxes[j + 1] = top_bboxes[j + 1][:, 0:5]
scores = np.hstack([top_bboxes[j][:, -1] for j in range(1, categories + 1)])
if len(scores) > max_per_image:
kth = len(scores) - max_per_image
thresh = np.partition(scores, kth)[kth]
for j in range(1, categories + 1):
keep_inds = (top_bboxes[j][:, -1] >= thresh)
top_bboxes[j] = top_bboxes[j][keep_inds]
return top_bboxes