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evaluate_flic.py
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evaluate_flic.py
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#!/usr/bin/env python
# -*- coding: utf-8 -*-
# Copyright (c) 2016 Shunta Saito
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
from chainer import cuda
from chainer import serializers
from chainer import Variable
from transform import Transform
import argparse
import cv2 as cv
import glob
import imp
import numpy as np
import os
import re
import sys
def cropping(img, joints, min_dim):
# image cropping
_joints = joints.reshape((len(joints) // 2, 2))
posi_joints = [(j[0], j[1]) for j in _joints if j[0] > 0 and j[1] > 0]
x, y, w, h = cv.boundingRect(np.asarray([posi_joints]))
if w < min_dim:
w = min_dim
if h < min_dim:
h = min_dim
# bounding rect extending
x -= (w * 1.5 - w) / 2
y -= (h * 1.5 - h) / 2
w *= 1.5
h *= 1.5
# clipping
x, y, w, h = [int(z) for z in [x, y, w, h]]
x = np.clip(x, 0, img.shape[1] - 1)
y = np.clip(y, 0, img.shape[0] - 1)
w = np.clip(w, 1, img.shape[1] - (x + 1))
h = np.clip(h, 1, img.shape[0] - (y + 1))
img = img[y:y + h, x:x + w]
# joint shifting
_joints = np.asarray([(j[0] - x, j[1] - y) for j in _joints])
joints = _joints.flatten()
return img, joints
def resize(img, joints, size):
orig_h, orig_w = img.shape[:2]
joints[0::2] = joints[0::2] / float(orig_w) * size
joints[1::2] = joints[1::2] / float(orig_h) * size
img = cv.resize(img, (size, size), interpolation=cv.INTER_NEAREST)
return img, joints
def contrast(img):
if not img.dtype == np.float32:
img = img.astype(np.float32)
# global contrast normalization
img -= img.reshape(-1, 3).mean(axis=0)
img -= img.reshape(-1, 3).std(axis=0) + 1e-5
return img
def input_transform(datum, datadir, fname_index, joint_index, min_dim, gcn):
img_fn = '%s/images/%s' % (datadir, datum[fname_index])
if not os.path.exists(img_fn):
raise IOError('%s is not exist' % img_fn)
img = cv.imread(img_fn)
joints = np.asarray([int(float(p)) for p in datum[joint_index:]])
img, joints = cropping(img, joints, min_dim)
img, joints = resize(img, joints, size)
if gcn:
img = contrast(img)
else:
img /= 255.0
return img, joints
def load_model(args):
model_fn = os.path.basename(args.model)
model_name = model_fn.split('.')[0]
model = imp.load_source(model_name, args.model)
model = getattr(model, model_name)
model = model(args.joint_num)
serializers.load_npz(args.param, model)
model.train = False
return model
def load_data(trans, args, x):
c = args.channel
s = args.size
d = args.joint_num * 2
# data augmentation
input_data = np.zeros((len(x), c, s, s))
label = np.zeros((len(x), d))
for i, line in enumerate(x):
d, t = trans.transform(line.split(','), args.datadir,
args.fname_index, args.joint_index)
input_data[i] = d.transpose((2, 0, 1))
label[i] = t
return input_data, label
def create_tiled_image(perm, out_dir, result_dir, epoch, suffix, N=25):
fnames = np.array(sorted(glob.glob('%s/*%s.jpg' % (out_dir, suffix))))
tile_fnames = fnames[perm[:N]]
h, w, pad = 220, 220, 2
side = int(np.ceil(np.sqrt(len(tile_fnames))))
canvas = np.zeros((side * h + pad * (side + 1),
side * w + pad * (side + 1), 3))
for i, fname in enumerate(tile_fnames):
img = cv.imread(fname)
x = w * (i % side) + pad * (i % side + 1)
y = h * (i // side) + pad * (i // side + 1)
canvas[y:y + h, x:x + w, :] = img
if args.resize > 0:
canvas = cv.resize(canvas, (args.resize, args.resize))
cv.imwrite('%s/test_%d_tiled_%s.jpg' % (result_dir, epoch, suffix), canvas)
def test(args):
# test data
test_fn = '%s/test_joints.csv' % args.datadir
test_dl = np.array([l.strip() for l in open(test_fn).readlines()])
# load model
if args.gpu >= 0:
cuda.get_device(args.gpu).use()
model = load_model(args)
if args.gpu >= 0:
model.to_gpu(args.gpu)
# create output dir
epoch = int(re.search('epoch-([0-9]+)', args.param).groups()[0])
result_dir = os.path.dirname(args.param)
out_dir = '%s/test_%d' % (result_dir, epoch)
if not os.path.exists(out_dir):
os.makedirs(out_dir)
out_log = '%s.log' % out_dir
fp = open(out_log, 'w')
mean_error = 0.0
N = len(test_dl)
for i in range(0, N, args.batchsize):
lines = test_dl[i:i + args.batchsize]
input_data, labels = load_data(trans, args, lines)
if args.gpu >= 0:
input_data = cuda.to_gpu(input_data.astype(np.float32))
labels = cuda.to_gpu(labels.astype(np.float32))
else:
input_data = input_data.astype(np.float32)
labels = labels.astype(np.float32)
x = Variable(input_data, volatile=True)
t = Variable(labels, volatile=True)
model(x, t)
if args.gpu >= 0:
preds = cuda.to_cpu(model.pred.data)
input_data = cuda.to_cpu(input_data)
labels = cuda.to_cpu(labels)
else:
preds = model.pred.data
for n, line in enumerate(lines):
img_fn = line.split(',')[args.fname_index]
img = input_data[n].transpose((1, 2, 0))
pred = preds[n]
img_pred, pred = trans.revert(img, pred)
# turn label data into image coordinates
label = labels[n]
img_label, label = trans.revert(img, label)
# calc mean_error
error = np.linalg.norm(pred - label) / len(pred)
mean_error += error
# create pred, label tuples
img_pred = np.array(img_pred.copy())
img_label = np.array(img_label.copy())
pred = [tuple(p) for p in pred]
label = [tuple(p) for p in label]
# all limbs
img_label = draw_joints(
img_label, label, args.draw_limb, args.text_scale)
img_pred = draw_joints(
img_pred, pred, args.draw_limb, args.text_scale)
msg = '{:5}/{:5} {}\terror:{}\tmean_error:{}'.format(
i + n, N, img_fn, error, mean_error / (i + n + 1))
print(msg, file=fp)
print(msg)
fn, ext = os.path.splitext(img_fn)
tr_fn = '%s/%d-%d_%s_pred%s' % (out_dir, i, n, fn, ext)
la_fn = '%s/%d-%d_%s_label%s' % (out_dir, i, n, fn, ext)
cv.imwrite(tr_fn, img_pred)
cv.imwrite(la_fn, img_label)
def tile(args):
# create output dir
epoch = int(re.search('epoch-([0-9]+)', args.param).groups()[0])
result_dir = os.path.dirname(args.param)
out_dir = '%s/test_%d' % (result_dir, epoch)
if not os.path.exists(out_dir):
raise Exception('%s is not exist' % out_dir)
# save tiled image of randomly chosen results and labels
n_img = len(glob.glob('%s/*pred*' % (out_dir)))
perm = np.random.permutation(n_img)
create_tiled_image(perm, out_dir, result_dir, epoch, 'pred', args.n_imgs)
create_tiled_image(perm, out_dir, result_dir, epoch, 'label', args.n_imgs)
if __name__ == '__main__':
sys.path.append('tests')
sys.path.append('models')
from test_flic_dataset import draw_joints
parser = argparse.ArgumentParser()
parser.add_argument('--model', type=str,
help='model definition file in models dir')
parser.add_argument('--param', type=str,
help='trained parameters file in result dir')
parser.add_argument('--batchsize', type=int, default=128)
parser.add_argument('--gpu', type=int, default=0)
parser.add_argument('--datadir', type=str, default='data/FLIC-full')
parser.add_argument('--mode', type=str, default='test',
choices=['test', 'tile'],
help='test or create tiled image')
parser.add_argument('--n_imgs', type=int, default=9,
help='how many images will be tiled')
parser.add_argument('--resize', type=int, default=-1,
help='resize the results of tiling')
parser.add_argument('--seed', type=int, default=9,
help='random seed to select images to be tiled')
parser.add_argument('--draw_limb', type=bool, default=True,
help='whether draw limb line to visualize')
parser.add_argument('--text_scale', type=float, default=1.0,
help='text scale when drawing indices of joints')
args = parser.parse_args()
result_dir = os.path.dirname(args.param)
log_fn = grep.grep('{}/log.txt'.format(result_dir))[0]
for line in open(log_fn):
if 'Namespace' in line:
args.joint_num = int(
re.search('joint_num=([0-9]+)', line).groups()[0])
args.fname_index = int(
re.search('fname_index=([0-9]+)', line).groups()[0])
args.joint_index = int(
re.search('joint_index=([0-9]+)', line).groups()[0])
break
if args.mode == 'test':
test(args)
elif args.mode == 'tile':
np.random.seed(args.seed)
tile(args)