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help.py
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help.py
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"""
tfnet secondary (helper) methods
"""
from ..utils.loader import create_loader
from time import time as timer
import tensorflow as tf
import numpy as np
import sys
import cv2
import os
import csv
old_graph_msg = 'Resolving old graph def {} (no guarantee)'
def build_train_op(self):
self.framework.loss(self.out)
self.say('Building {} train op'.format(self.meta['model']))
optimizer = self._TRAINER[self.FLAGS.trainer](self.FLAGS.lr)
gradients = optimizer.compute_gradients(self.framework.loss)
self.train_op = optimizer.apply_gradients(gradients)
def load_from_ckpt(self):
if self.FLAGS.load < 0: # load lastest ckpt
with open(self.FLAGS.backup + 'checkpoint', 'r') as f:
last = f.readlines()[-1].strip()
load_point = last.split(' ')[1]
load_point = load_point.split('"')[1]
load_point = load_point.split('-')[-1]
self.FLAGS.load = int(load_point)
load_point = os.path.join(self.FLAGS.backup, self.meta['name'])
load_point = '{}-{}'.format(load_point, self.FLAGS.load)
self.say('Loading from {}'.format(load_point))
try: self.saver.restore(self.sess, load_point)
except: load_old_graph(self, load_point)
def say(self, *msgs):
if not self.FLAGS.verbalise:
return
msgs = list(msgs)
for msg in msgs:
if msg is None: continue
print(msg)
def load_old_graph(self, ckpt):
ckpt_loader = create_loader(ckpt)
self.say(old_graph_msg.format(ckpt))
for var in tf.global_variables():
name = var.name.split(':')[0]
args = [name, var.get_shape()]
val = ckpt_loader(args)
assert val is not None, \
'Cannot find and load {}'.format(var.name)
shp = val.shape
plh = tf.placeholder(tf.float32, shp)
op = tf.assign(var, plh)
self.sess.run(op, {plh: val})
def _get_fps(self, frame):
elapsed = int()
start = timer()
preprocessed = self.framework.preprocess(frame)
feed_dict = {self.inp: [preprocessed]}
net_out = self.sess.run(self.out, feed_dict)[0]
processed = self.framework.postprocess(net_out, frame, False)
return timer() - start
def camera(self):
file = self.FLAGS.demo
SaveVideo = self.FLAGS.saveVideo
if self.FLAGS.track :
if self.FLAGS.tracker == "deep_sort":
from deep_sort import generate_detections
from deep_sort.deep_sort import nn_matching
from deep_sort.deep_sort.tracker import Tracker
metric = nn_matching.NearestNeighborDistanceMetric(
"cosine", 0.2, 100)
tracker = Tracker(metric)
encoder = generate_detections.create_box_encoder(
os.path.abspath("deep_sort/resources/networks/mars-small128.ckpt-68577"))
elif self.FLAGS.tracker == "sort":
from sort.sort import Sort
encoder = None
tracker = Sort()
if self.FLAGS.BK_MOG and self.FLAGS.track :
fgbg = cv2.bgsegm.createBackgroundSubtractorMOG()
if file == 'camera':
file = 0
else:
assert os.path.isfile(file), \
'file {} does not exist'.format(file)
camera = cv2.VideoCapture(file)
if file == 0:
self.say('Press [ESC] to quit video')
assert camera.isOpened(), \
'Cannot capture source'
if self.FLAGS.csv :
f = open('{}.csv'.format(file),'w')
writer = csv.writer(f, delimiter=',')
writer.writerow(['frame_id', 'track_id' , 'x', 'y', 'w', 'h'])
f.flush()
else :
f =None
writer= None
if file == 0:#camera window
cv2.namedWindow('', 0)
_, frame = camera.read()
height, width, _ = frame.shape
cv2.resizeWindow('', width, height)
else:
_, frame = camera.read()
height, width, _ = frame.shape
if SaveVideo:
fourcc = cv2.VideoWriter_fourcc(*'XVID')
if file == 0:#camera window
fps = 1 / self._get_fps(frame)
if fps < 1:
fps = 1
else:
fps = round(camera.get(cv2.CAP_PROP_FPS))
videoWriter = cv2.VideoWriter(
'output_{}'.format(file), fourcc, fps, (width, height))
# buffers for demo in batch
buffer_inp = list()
buffer_pre = list()
elapsed = 0
start = timer()
self.say('Press [ESC] to quit demo')
#postprocessed = []
# Loop through frames
n = 0
while camera.isOpened():
elapsed += 1
_, frame = camera.read()
if frame is None:
print ('\nEnd of Video')
break
if self.FLAGS.skip != n :
n+=1
continue
n = 0
if self.FLAGS.BK_MOG and self.FLAGS.track :
fgmask = fgbg.apply(frame)
else :
fgmask = None
preprocessed = self.framework.preprocess(frame)
buffer_inp.append(frame)
buffer_pre.append(preprocessed)
# Only process and imshow when queue is full
if elapsed % self.FLAGS.queue == 0:
feed_dict = {self.inp: buffer_pre}
net_out = self.sess.run(self.out, feed_dict)
for img, single_out in zip(buffer_inp, net_out):
if not self.FLAGS.track :
postprocessed = self.framework.postprocess(
single_out, img, save= False)
else :
postprocessed = self.framework.postprocess(
single_out, img,frame_id = elapsed,csv_file=f,csv=writer,mask = fgmask,encoder=encoder,tracker=tracker,save=False)
if SaveVideo:
videoWriter.write(postprocessed)
if self.FLAGS.display :
cv2.imshow('', postprocessed)
# Clear Buffers
buffer_inp = list()
buffer_pre = list()
if elapsed % 5 == 0:
sys.stdout.write('\r')
sys.stdout.write('{0:3.3f} FPS'.format(
elapsed / (timer() - start)))
sys.stdout.flush()
if self.FLAGS.display :
choice = cv2.waitKey(1)
if choice == 27:
break
sys.stdout.write('\n')
if SaveVideo:
videoWriter.release()
if self.FLAGS.csv :
f.close()
camera.release()
if self.FLAGS.display :
cv2.destroyAllWindows()
def to_darknet(self):
darknet_ckpt = self.darknet
with self.graph.as_default() as g:
for var in tf.global_variables():
name = var.name.split(':')[0]
var_name = name.split('-')
l_idx = int(var_name[0])
w_sig = var_name[1].split('/')[-1]
l = darknet_ckpt.layers[l_idx]
l.w[w_sig] = var.eval(self.sess)
for layer in darknet_ckpt.layers:
for ph in layer.h:
layer.h[ph] = None
return darknet_ckpt