utils.py
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import os.path
import time
import json
import warnings
import signal
from datetime import datetime
from contextlib import contextmanager
from PIL import Image, ImageDraw
import requests
import numpy as np
import math
import torch
def order_points(pts):
pts_reorder = []
for idx, pt in enumerate(pts):
idx = torch.argsort(pt[:, 0])
xSorted = pt[idx, :]
leftMost = xSorted[:2, :]
rightMost = xSorted[2:, :]
leftMost = leftMost[torch.argsort(leftMost[:, 1]), :]
(tl, bl) = leftMost
D = torch.cdist(tl[np.newaxis], rightMost)[0]
(br, tr) = rightMost[torch.argsort(D, descending=True), :]
pts_reorder.append(torch.stack([tl, tr, br, bl]))
return torch.stack([p for p in pts_reorder])
def rotate_boxes(boxes, points=False):
'''
Rotate target bounding boxes
Input:
Target boxes (xmin_ymin, width_height, theta)
Output:
boxes_axis (xmin_ymin, xmax_ymax, theta)
boxes_rotated (xy0, xy1, xy2, xy3)
'''
u = torch.stack([torch.cos(boxes[:,4]), torch.sin(boxes[:,4])], dim=1)
l = torch.stack([-torch.sin(boxes[:,4]), torch.cos(boxes[:,4])], dim=1)
R = torch.stack([u, l], dim=1)
if points:
cents = torch.stack([(boxes[:,0]+boxes[:,2])/2, (boxes[:,1]+boxes[:,3])/2],1).transpose(1,0)
boxes_rotated = torch.stack([boxes[:,0],boxes[:,1],
boxes[:,2], boxes[:,1],
boxes[:,2], boxes[:,3],
boxes[:,0], boxes[:,3],
boxes[:,-2],
boxes[:,-1]],1)
else:
cents = torch.stack([boxes[:,0]+(boxes[:,2])/2, boxes[:,1]+(boxes[:,3])/2],1).transpose(1,0)
boxes_rotated = torch.stack([boxes[:,0],boxes[:,1],
(boxes[:,0]+boxes[:,2]), boxes[:,1],
(boxes[:,0]+boxes[:,2]), (boxes[:,1]+boxes[:,3]),
boxes[:,0], (boxes[:,1]+boxes[:,3]),
boxes[:,-2],
boxes[:,-1]],1)
xy0R = torch.matmul(R,boxes_rotated[:,:2].transpose(1,0) - cents) + cents
xy1R = torch.matmul(R,boxes_rotated[:,2:4].transpose(1,0) - cents) + cents
xy2R = torch.matmul(R,boxes_rotated[:,4:6].transpose(1,0) - cents) + cents
xy3R = torch.matmul(R,boxes_rotated[:,6:8].transpose(1,0) - cents) + cents
xy0R = torch.stack([xy0R[i,:,i] for i in range(xy0R.size(0))])
xy1R = torch.stack([xy1R[i,:,i] for i in range(xy1R.size(0))])
xy2R = torch.stack([xy2R[i,:,i] for i in range(xy2R.size(0))])
xy3R = torch.stack([xy3R[i,:,i] for i in range(xy3R.size(0))])
boxes_axis = torch.cat([boxes[:, :2], boxes[:, :2] + boxes[:, 2:4] - 1,
torch.sin(boxes[:,-1, None]), torch.cos(boxes[:,-1, None])], 1)
boxes_rotated = order_points(torch.stack([xy0R,xy1R,xy2R,xy3R],dim = 1)).view(-1,8)
return boxes_axis, boxes_rotated
def rotate_box(bbox):
xmin, ymin, width, height, theta = bbox
xy1 = xmin, ymin
xy2 = xmin, ymin + height - 1
xy3 = xmin + width - 1, ymin + height - 1
xy4 = xmin + width - 1, ymin
cents = np.array([xmin + (width - 1) / 2, ymin + (height - 1) / 2])
corners = np.stack([xy1, xy2, xy3, xy4])
u = np.stack([np.cos(theta), -np.sin(theta)])
l = np.stack([np.sin(theta), np.cos(theta)])
R = np.vstack([u, l])
corners = np.matmul(R, (corners - cents).transpose(1, 0)).transpose(1, 0) + cents
return corners.reshape(-1).tolist()
def show_detections(detections):
'Show image with drawn detections'
for image, detections in detections.items():
im = Image.open(image).convert('RGBA')
overlay = Image.new('RGBA', im.size, (255, 255, 255, 0))
draw = ImageDraw.Draw(overlay)
detections.sort(key=lambda d: d['score'])
for detection in detections:
box = detection['bbox']
alpha = int(detection['score'] * 255)
draw.rectangle(box, outline=(255, 255, 255, alpha))
draw.text((box[0] + 2, box[1]), '[{}]'.format(detection['class']),
fill=(255, 255, 255, alpha))
draw.text((box[0] + 2, box[1] + 10), '{:.2}'.format(detection['score']),
fill=(255, 255, 255, alpha))
im = Image.alpha_composite(im, overlay)
im.show()
def save_detections(path, detections):
print('Writing detections to {}...'.format(os.path.basename(path)))
with open(path, 'w') as f:
json.dump(detections, f)
@contextmanager
def ignore_sigint():
handler = signal.getsignal(signal.SIGINT)
signal.signal(signal.SIGINT, signal.SIG_IGN)
try:
yield
finally:
signal.signal(signal.SIGINT, handler)
class Profiler(object):
def __init__(self, names=['main']):
self.names = names
self.lasts = {k: 0 for k in names}
self.totals = self.lasts.copy()
self.counts = self.lasts.copy()
self.means = self.lasts.copy()
self.reset()
def reset(self):
last = time.time()
for name in self.names:
self.lasts[name] = last
self.totals[name] = 0
self.counts[name] = 0
self.means[name] = 0
def start(self, name='main'):
self.lasts[name] = time.time()
def stop(self, name='main'):
self.totals[name] += time.time() - self.lasts[name]
self.counts[name] += 1
self.means[name] = self.totals[name] / self.counts[name]
def bump(self, name='main'):
self.stop(name)
self.start(name)
def post_metrics(url, metrics):
try:
for k, v in metrics.items():
requests.post(url,
data={'time': int(datetime.now().timestamp() * 1e9),
'metric': k, 'value': v})
except Exception as e:
warnings.warn('Warning: posting metrics failed: {}'.format(e))