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util.py
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util.py
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#!/usr/bin/env python
# -*- coding: utf-8 -*-
import cv2
import pdb
import numpy as np
from os.path import join
from ops import l2_dist_360
from MeanOverlap import MeanOverlap
def catData(totalData, newData):
""" Concat data from scratch """
if totalData is None:
totalData = newData[np.newaxis].copy()
else:
totalData = np.concatenate((totalData, newData[np.newaxis]))
return totalData
def score(Agent, seq1, seq2, _full=True):
""" Calculate IoU """
acc = 0.0
total_num = 0
MO = MeanOverlap(Agent.W, Agent.H)
for batch in xrange(Agent.batch_size):
for i in xrange(Agent.n_frames):
if not _full and np.sum(seq2[batch][i]) == 0:
continue
acc += MO.IOU((seq1[batch, i, 0], seq1[batch, i, 1]), (seq2[batch, i, 0], seq2[batch, i, 1]))
total_num += 1
return (acc / total_num) if total_num != 0 else 0 #(n_frames*batch_size)
def printAcc(threshold, targetFrNum, totalFrNum):
""" Fetch accuracy and print out """
print "Acc is:"
for th in threshold:
print ("%d" %(th)),
print
for i, types in enumerate(targetFrNum):
print i if i < 4 else (i-4),
for j, th in enumerate(threshold):
print ("%.5f" %(types[j] / (totalFrNum if totalFrNum > 0 else 1))),
print
def cal_accuracy(Agent, pred, gt, targetFrNum, totalFrNum):
""" Calculate and return accuracy """
if np.sum(gt) == 0:
return targetFrNum, totalFrNum
l2_dist = l2_dist_360(pred, gt, Agent.W)
l2_dist = np.tile(l2_dist,(len(Agent.threshold), 1))
""" if l2_dist(10 x 50) <= thres(1 x 10), then targetFrNum(8types x 10thres) += 1 """
thres = np.sum(l2_dist <= np.tile(np.reshape(Agent.threshold, (-1, 1)), (1, l2_dist.shape[-1])), axis=1)
center = np.array([Agent.W/2, Agent.H/2])
for th, i in enumerate(thres):
if np.min(np.linalg.norm(gt - center, axis=1)) > 100:
targetFrNum[i,th] += 1
else:
targetFrNum[i+4,th] += 1
totalFrNum += 1
return targetFrNum, totalFrNum
def load_batch_data(Agent, path, num_batch, _copy=False, _augment=False):
""" Load batch data from path and normalize them, use copy to preserve raw data """
data = np.load(join(path, 'pruned_roisavg/batch_{}.npy'.format(num_batch))) #[0:Agent.batch_size,0:Agent.n_frames,0:Agent.n_detection,0:Agent.n_input]
oracle_viewangle = np.load(join(path, 'label/batch_{}.npy'.format(num_batch))) #[0:Agent.batch_size,0:Agent.n_frames,0:Agent.n_classes+1]
one_hot_labels = np.load(join(path, 'onehot/batch_{}.npy'.format(num_batch))) #[0:Agent.batch_size,0:Agent.n_frames,0:Agent.n_detection]
hof = np.load(join(path, 'hof/batch_{}.npy'.format(num_batch))) #[0:Agent.batch_size,0:Agent.n_frames,0:Agent.n_detection,0:Agent.n_bin_size]
box_center = np.load(join(path, 'divide_area_pruned_boxes/batch_{}.npy'.format(num_batch))) #[0:Agent.batch_size,0:Agent.n_frames,0:Agent.n_detection,0:]
img = np.zeros((Agent.batch_size), dtype=np.float16)
if _augment is True:
data, oracle_viewangle, box_center = augment_data(data, oracle_viewangle, box_center)
if _copy is True:
box = np.copy(box_center)
gt = np.copy(oracle_viewangle)[:, :, :2]
else:
box = None
gt = None
box_center[:,:,:,0] = (box_center[:,:,:,0]/Agent.W + box_center[:,:,:,2]/Agent.W)/2
box_center[:,:,:,1] = (box_center[:,:,:,1]/Agent.H + box_center[:,:,:,3]/Agent.H)/2
box_center = box_center[:, :, :, :2]
oracle_viewangle[:,:,0] = oracle_viewangle[:,:,0]/Agent.W
oracle_viewangle[:,:,1] = oracle_viewangle[:,:,1]/Agent.H
oracle_viewangle = oracle_viewangle[:, :, :2]
return data, one_hot_labels, oracle_viewangle, box_center, hof, img, box, gt
def visual_gaze(Agent, img_name, gt, pred, alphas, box):
"""
[Deprecated]
Draw and plot visual gaze contains boxes, gt gazes, and prediction
"""
print Agent.img_path + img_name + '.jpg'
img = cv2.imread(Agent.img_path + img_name + '.jpg',3)
if img is None:
print 'No image is found.'
return 1
img = cv2.resize(img, (int(W),int(H)))
W = Agent.W
H = Agent.H
# Box
idx = 0
transparent = 0.90
for xmin, ymin, xmax, ymax in box.astype(np.int32):
if xmax > W: xmax = int(W)
if ymax > H: ymax = int(H)
print xmin, ymin, xmax, ymax, alphas[idx]
#if alphas[idx] > 0.0:
cv2.rectangle(img,(xmin, ymin),(xmax, ymax), (255,255,255), 2)
img[ymin:ymax,xmin:xmax,:] = img[ymin:ymax,xmin:xmax,:]*0.95 + np.ones((ymax-ymin,xmax-xmin,3))*0.05
cv2.putText(img, ("{0:.2f}").format(alphas[idx]), (int((xmax+xmin)/2)+1 , int((ymax+ymin)/2)+1), cv2.FONT_HERSHEY_SIMPLEX, 1.50, (0,0,0), 2)
cv2.putText(img, ("{0:.2f}").format(alphas[idx]), (int((xmax+xmin)/2) , int((ymax+ymin)/2)), cv2.FONT_HERSHEY_SIMPLEX, 1.50, (255,255,255), 2)
idx += 1
# Predicted gaze
ll = 3
# Desire gaze
color = [(255, 0, 0), (0,255,0),(0,255,255),(0,0,255)] # Green, Yellow, Red
i = 2
u, v = gt.astype(np.int32)
img[v-ll:v+ll,u-ll:u+ll,1] = 255
cv2.circle(img,(u,v),10,color[i],2) # desize gaze centers
xmin = u - int(W/4) if u > W/4 else 0
xmax = u + int(W/4) if u < 3*W/4 else int(W)
ymin = v - int(H/4) if v > H/4 else 0
ymax = v + int(H/4) if v < 3*H/4 else int(H)
cv2.rectangle(img,(xmin, ymin),(xmax, ymax), color[i], 2)
img[ymin:ymax,xmin:xmax,:] = img[ymin:ymax,xmin:xmax,:]*transparent + \
np.tile(np.array([clr for clr in color[i]])*(1-transparent),(ymax-ymin,xmax-xmin,1))
print ("gt: ({}, {})").format(u, v)
# Predicted gaze
i = 0
u, v = int(pred[0]), int(pred[1])
img[v-ll:v+ll,u-ll:u+ll,2] = 255
cv2.circle(img,(u,v),10,(255,0,0),2) # predicted gaze center
xmin = u - int(W/4) if u > W/4 else 0
xmax = u + int(W/4) if u < 3*W/4 else int(W)
ymin = v - int(H/4) if v > H/4 else 0
ymax = v + int(H/4) if v < 3*H/4 else int(H)
cv2.rectangle(img,(xmin, ymin),(xmax, ymax), color[i], 2)
img[ymin:ymax,xmin:xmax,:] = img[ymin:ymax,xmin:xmax,:]*transparent + \
np.tile(np.array([clr for clr in color[i]])*(1-transparent),(ymax-ymin,xmax-xmin,1))
print ("pred: ({}, {})").format(u, v)
img = cv2.resize(img, (800,400))
if Agent._save_img:
cv2.imwrite(save_path+img_name+'.jpg', img)
else:
cv2.imshow("gaze", img)
key = cv2.waitKey(0) & 0xFF
if key == 27:
return -1
elif key == ord('q'):
return -2
elif key == ord('s'):
return -3
elif key == ord('c'):
Agent._save_img = not Agent._save_img
return 0
else:
return 0