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infer.py
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infer.py
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import geom
import model
import json
import numpy
import math
import os
import skimage.io, skimage.transform
import sys
import tensorflow as tf
import time
MODEL_PATH = sys.argv[1]
data_path = sys.argv[2]
model.BATCH_SIZE = 1
model.SEQ_LEN = 2
SKIP = 2
MAX_AGE = 10
MODE = 'imsp'
LABELS = ['MOT17-{}-SDP'.format(x) for x in ['01', '03', '06', '07', '08', '12', '14']]
DETECTION_PATH = data_path + '/mot17/test/{}/det/det-filter60.json'
FRAME_PATH = data_path + '/mot17/test/{}/img1/'
OUT_PATH = data_path + '/mot17/test/{}/det/uns20.json'
ORIG_WIDTH = 1920
ORIG_HEIGHT = 1080
DETECTION_SCALE = 1
FRAME_SCALE = 1
CROP_SIZE = 64
HIDDEN_SIZE = 4*64
print('initializing model')
m = model.Model(options={'mode': MODE})
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
session = tf.Session(config=config)
m.saver.restore(session, MODEL_PATH)
def get_frame_fname(frame_idx):
s = str(frame_idx)
while len(s) < 6:
s = '0' + s
return s + '.jpg'
for label in LABELS:
detection_path = DETECTION_PATH.format(label)
print('loading detections from {}'.format(detection_path))
with open(detection_path, 'r') as f:
raw_detections = json.load(f)
# auto-detect im width/height
for frame_idx, dlist in enumerate(raw_detections):
if not dlist or len(dlist) == 0:
continue
im = skimage.io.imread('{}/{}'.format(FRAME_PATH.format(label), get_frame_fname(frame_idx)))
im_bounds = geom.Rectangle(geom.Point(0, 0), geom.Point(im.shape[1]*FRAME_SCALE, im.shape[0]*FRAME_SCALE))
break
detections = [None for _ in range(len(raw_detections))]
for frame_idx, dlist in enumerate(raw_detections):
if not dlist or frame_idx % SKIP != 0:
continue
detections[frame_idx] = []
for i, d in enumerate(dlist):
rect = geom.Rectangle(
geom.Point(d['left']//DETECTION_SCALE, d['top']//DETECTION_SCALE),
geom.Point(d['right']//DETECTION_SCALE, d['bottom']//DETECTION_SCALE)
)
rect = im_bounds.clip_rect(rect)
if rect.lengths().x < 4 or rect.lengths().y < 4:
continue
nd = {
'left': rect.start.x,
'top': rect.start.y,
'right': rect.end.x,
'bottom': rect.end.y,
'frame_idx': d['frame_idx'],
}
detections[frame_idx].append(nd)
def zip_frame_info(detections, frame_idx):
im = skimage.io.imread('{}/{}'.format(FRAME_PATH.format(label), get_frame_fname(frame_idx)))
im_bounds = geom.Rectangle(
geom.Point(0, 0),
geom.Point(im.shape[0], im.shape[1])
)
info = []
for detection in detections:
rect = geom.Rectangle(
geom.Point(detection['top']//FRAME_SCALE, detection['left']//FRAME_SCALE),
geom.Point(detection['bottom']//FRAME_SCALE, detection['right']//FRAME_SCALE)
)
crop = im[rect.start.x:rect.end.x, rect.start.y:rect.end.y, :]
resize_factor = min([float(CROP_SIZE) / crop.shape[0], float(CROP_SIZE) / crop.shape[1]])
crop = (skimage.transform.resize(crop, [int(crop.shape[0] * resize_factor), int(crop.shape[1] * resize_factor)])*255).astype('uint8')
fix_crop = numpy.zeros((CROP_SIZE, CROP_SIZE, 3), dtype='uint8')
fix_crop[0:crop.shape[0], 0:crop.shape[1], :] = crop
detection['width'] = float(detection['right']-detection['left'])/ORIG_WIDTH
detection['height'] = float(detection['bottom']-detection['top'])/ORIG_HEIGHT
info.append((detection, fix_crop))
return info
def get_loc(detection):
cx = (detection['left'] + detection['right']) / 2
cy = (detection['top'] + detection['bottom']) / 2
cx = float(cx) / ORIG_WIDTH
cy = float(cy) / ORIG_HEIGHT
return cx, cy
def get_stuff(infos):
def per_info(info):
images = []
boxes = []
for i, (detection, crop) in enumerate(info):
images.append(crop)
cx, cy = get_loc(detection)
boxes.append([cx, cy, detection['width'], detection['height']])
detections = [get_loc(detection) for detection, _ in info]
return images, boxes, detections, len(info)
all_images = []
all_boxes = []
all_detections = []
all_counts = []
for info in infos:
images, boxes, detections, count = per_info(info)
all_images.extend(images)
all_boxes.extend(boxes)
all_detections.append(detections)
all_counts.append(count)
return all_images, all_boxes, all_detections, all_counts
def softmax(X, theta = 1.0, axis = None):
y = numpy.atleast_2d(X)
if axis is None:
axis = next(j[0] for j in enumerate(y.shape) if j[1] > 1)
y = y * float(theta)
y = y - numpy.expand_dims(numpy.max(y, axis = axis), axis)
y = numpy.exp(y)
ax_sum = numpy.expand_dims(numpy.sum(y, axis = axis), axis)
p = y / ax_sum
if len(X.shape) == 1: p = p.flatten()
return p
# list of objects (id, detection_idx in latest frame, prev_hidden, time since last match)
# note: detection_idx should be len(info)+1 for the terminal vertex
active_objects = None
track_counter = 0
for frame_idx in range(0, len(detections)-SKIP, SKIP):
if not detections[frame_idx] or not detections[frame_idx+SKIP]:
active_objects = None
continue
print(frame_idx, len(detections))
info1 = zip_frame_info(detections[frame_idx], frame_idx)
info2 = zip_frame_info(detections[frame_idx+SKIP], frame_idx+SKIP)
if len(info1) == 0 or len(info2) == 0:
active_objects = None
continue
images1, boxes1, _, counts1 = get_stuff([info1])
images2, boxes2, _, counts2 = get_stuff([info2])
if active_objects is None:
active_objects = []
for left_idx in range(len(info1)):
active_objects.append((
track_counter,
left_idx,
numpy.zeros((HIDDEN_SIZE,), dtype='float32'),
0,
[images1[left_idx]],
))
detections[frame_idx][left_idx]['track_id'] = track_counter
track_counter += 1
'''
outputs_raw, out_mat, cur_hidden, out_logits, mat_finesp, mat_longim = session.run([m.out_mat_reweight, m.out_mat, m.out_hidden, m.out_logits_finesp, m.out_mat_finesp, m.out_mat_longim], feed_dict=feed_dict)
# take maximum in outputs_raw along the active indices
outputs = numpy.zeros((len(active_objects), len(info2)+1), dtype='float32')
for i, obj in enumerate(active_objects):
cur_finesp = mat_finesp[active_indices[i], :].max(axis=0)
cur_longim = mat_longim[active_indices[i], :].max(axis=0)
outputs[i, :] = cur_finesp + cur_longim
#outputs[i, 0:len(info2)] = outputs_raw[active_indices[i], 0:len(info2)].max(axis=0)
#outputs[i, len(info2)] = outputs_raw[active_indices[i], len(info2)].min()
'''
if MODE == 'imsp' or MODE == 'finesp' or MODE == 'longim':
# flatten the active objects since each object may have multiple images
flat_images = []
flat_boxes = []
flat_hidden = []
active_indices = {}
for i, obj in enumerate(active_objects):
active_indices[i] = []
for j in [1, 2, 4, 8, 16]:
#for j in range(1, len(obj[4])+1, len(obj[4])//5+1):
if len(obj[4]) < j:
continue
# use image from stored history, but use current box
active_indices[i].append(len(flat_images))
flat_images.append(obj[4][-j])
if obj[1] < len(info1):
flat_boxes.append(boxes1[obj[1]])
else:
flat_boxes.append(numpy.zeros((4,), dtype='float32'))
flat_hidden.append(obj[2])
feed_dict = {
m.raw_images: flat_images + images2,
m.input_boxes: flat_boxes + boxes2,
m.n_image: [[len(flat_images), len(images2), 0]],
m.is_training: False,
m.infer_sel: range(len(flat_images)),
m.infer_hidden: flat_hidden,
}
longim_logits, finesp_logits, pre_cur_hidden = session.run([m.out_logits_longim, m.out_logits_finesp, m.out_hidden], feed_dict=feed_dict)
longim_out_logits = numpy.zeros((len(active_objects), len(info2)+1), dtype='float32')
finesp_out_logits = numpy.zeros((len(active_objects), len(info2)+1), dtype='float32')
cur_hidden = numpy.zeros((len(active_objects), len(info2)+1, HIDDEN_SIZE), dtype='float32')
for i, obj in enumerate(active_objects):
longim_out_logits[i, 0:len(info2)] = longim_logits[active_indices[i], 0:len(info2)].mean(axis=0)
longim_out_logits[i, len(info2)] = longim_logits[active_indices[i], len(info2)].min()
finesp_out_logits[i, 0:len(info2)] = finesp_logits[active_indices[i], 0:len(info2)].mean(axis=0)
finesp_out_logits[i, len(info2)] = finesp_logits[active_indices[i], len(info2)].min()
cur_hidden[i, :, :] = pre_cur_hidden[active_indices[i][0], :, :]
#longim_mat = softmax(longim_out_logits, axis=1)
#finesp_mat = softmax(finesp_out_logits, axis=1)
longim_mat = numpy.minimum(softmax(longim_out_logits, axis=0), softmax(longim_out_logits, axis=1))
finesp_mat = numpy.minimum(softmax(finesp_out_logits, axis=0), softmax(finesp_out_logits, axis=1))
outputs = numpy.minimum(longim_mat, finesp_mat)
#outputs = numpy.minimum(longim_out_logits, finesp_out_logits)
#outputs = (longim_out_logits+finesp_out_logits)/2
if MODE == 'finesp':
outputs = finesp_mat
elif MODE == 'longim':
outputs = longim_mat
else:
feed_dict = {
m.raw_images: images1 + images2,
m.input_boxes: boxes1 + boxes2,
m.is_training: False,
m.infer_sel: [obj[1] for obj in active_objects],
m.infer_hidden: [obj[2] for obj in active_objects],
}
if MODE == 'occl':
feed_dict[m.a_counts] = [len(images1), len(images2)]
else:
feed_dict[m.n_image] = [[len(images1), len(images2), 0]]
outputs, out_mat, out_logits, cur_hidden = session.run([m.out_mat_reweight, m.out_mat, m.out_logits, m.out_hidden], feed_dict=feed_dict)
outputs = out_mat
# vote on best next frame: idx1->(output,idx2)
votes = {}
for i in range(len(active_objects)):
for j in range(len(info2)+1):
output = outputs[i, j]
#if j == len(info2) and out_logits[active_indices[i][0], :].argmax() == len(info2):
#if j == len(info2) and longim_out_logits[:, outputs[i, :].argmax()].argmax() != i:
#if j == len(info2) and longim_out_logits[i, :].max() < 1:
if MODE == 'imsp' and j != len(info2) and (longim_out_logits[i, j] < 0 or finesp_out_logits[i, j] < 0):
output = -100.0
elif MODE == 'finesp' and j != len(info2) and finesp_out_logits[i, j] < 0:
output = -100.0
#if j == len(info2):
# output = -2
if i not in votes or output > votes[i][0]:
if j < len(info2):
votes[i] = (output, j)
else:
votes[i] = (output, None)
# group by receiver and vote on max idx2->idx1 to eliminate duplicates
votes2 = {}
for idx1, t in votes.items():
output, idx2 = t
if idx2 is not None and (idx2 not in votes2 or output > votes2[idx2][0]):
votes2[idx2] = (output, idx1)
forward_matches = {idx1: idx2 for (idx2, (_, idx1)) in votes2.items()}
def get_hidden(idx1, idx2):
if model.__name__ == 'occl3b_model':
return cur_hidden[idx1, :]
else:
return cur_hidden[idx1, idx2, :]
new_objects = []
used_idx2s = set()
for idx1, obj in enumerate(active_objects):
if idx1 in forward_matches:
idx2 = forward_matches[idx1]
new_objects.append((
obj[0],
idx2,
get_hidden(idx1, idx2),
#numpy.zeros((64,), dtype='float32'),
0,
obj[4] + [images2[idx2]],
))
used_idx2s.add(idx2)
detections[frame_idx+SKIP][idx2]['track_id'] = obj[0]
elif obj[3] < MAX_AGE:
idx2 = votes[idx1][1]
if idx2 is None or True:
idx2 = len(info2)
new_objects.append((
obj[0],
idx2,
get_hidden(idx1, idx2),
#numpy.zeros((64,), dtype='float32'),
obj[3]+1,
obj[4],
))
for idx2 in range(len(info2)):
if idx2 in used_idx2s:
continue
new_objects.append((
track_counter,
idx2,
numpy.zeros((HIDDEN_SIZE,), dtype='float32'),
0,
[images2[idx2]],
))
detections[frame_idx+SKIP][idx2]['track_id'] = track_counter
track_counter += 1
active_objects = new_objects
ndetections = [None for _ in detections]
for frame_idx, dlist in enumerate(detections):
if not dlist:
continue
dlist = [d for d in dlist if 'track_id' in d]
if not dlist:
continue
ndetections[frame_idx] = dlist
detections = ndetections
with open(OUT_PATH.format(label), 'w') as f:
json.dump(detections, f)