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data.py
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data.py
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import numpy as np
import math
import os
from random import randint
from PIL import Image
import param
import util
def load_db_detect_train(dim):
print "Loading positive training db..."
annot_dir = param.db_dir + "AFLW/aflw/data/"
annot_fp = open(annot_dir + "annot", "r")
raw_data = annot_fp.readlines()
#pos image cropping
pos_db_12 = [0 for _ in xrange(len(raw_data))]
pos_db_24 = [0 for _ in xrange(len(raw_data))]
pos_db_48 = [0 for _ in xrange(len(raw_data))]
for i,line in enumerate(raw_data):
parsed_line = line.split(',')
filename = parsed_line[0][3:-1]
xmin = int(parsed_line[1])
ymin = int(parsed_line[2])
xmax = xmin + int(parsed_line[3])
ymax = ymin + int(parsed_line[4][:-2])
img = Image.open(param.pos_dir+filename)
#for debugging
#img.save(str(i) + ".jpg")
#truncated image(error)
if i == 8160 or i == 14884 or i == 14886:
continue
#check if gray
if len(np.shape(img)) != param.input_channel:
img = np.asarray(img)
img = np.reshape(img,(np.shape(img)[0],np.shape(img)[1],1))
img = np.concatenate((img,img,img),axis=2)
img = Image.fromarray(img)
pos_db_line_12 = np.zeros((2,param.img_size_12,param.img_size_12,param.input_channel), np.float32)
pos_db_line_24 = np.zeros((2,param.img_size_24,param.img_size_24,param.input_channel), np.float32)
pos_db_line_48 = np.zeros((2,param.img_size_48,param.img_size_48,param.input_channel), np.float32)
if xmax >= img.size[0]:
xmax = img.size[0]-1
if ymax >= img.size[1]:
ymax = img.size[1]-1
cropped_img = img.crop((xmin, ymin, xmax, ymax))
flipped_img = cropped_img.transpose(Image.FLIP_LEFT_RIGHT)
cropped_arr_12 = util.img2array(cropped_img,param.img_size_12)
flipped_arr_12 = util.img2array(flipped_img,param.img_size_12)
cropped_arr_24 = util.img2array(cropped_img,param.img_size_24)
flipped_arr_24 = util.img2array(flipped_img,param.img_size_24)
cropped_arr_48 = util.img2array(cropped_img,param.img_size_48)
flipped_arr_48 = util.img2array(flipped_img,param.img_size_48)
#for debugging
#cropped_img.save(param.pos_dir + str(i) + ".jpg")
pos_db_line_12[0,:] = cropped_arr_12
pos_db_line_24[0,:] = cropped_arr_24
pos_db_line_48[0,:] = cropped_arr_48
pos_db_line_12[1,:] = flipped_arr_12
pos_db_line_24[1,:] = flipped_arr_24
pos_db_line_48[1,:] = flipped_arr_48
pos_db_12[i] = pos_db_line_12
pos_db_24[i] = pos_db_line_24
pos_db_48[i] = pos_db_line_48
img.close()
pos_db_12 = [elem for elem in pos_db_12 if type(elem) != int]
pos_db_24 = [elem for elem in pos_db_24 if type(elem) != int]
pos_db_48 = [elem for elem in pos_db_48 if type(elem) != int]
pos_db_12 = np.vstack(pos_db_12)
pos_db_24 = np.vstack(pos_db_24)
pos_db_48 = np.vstack(pos_db_48)
print "Loading negative training db..."
if dim == param.img_size_12:
#neg image cropping
nid = 0
neg_file_list = [f for f in os.listdir(param.neg_dir) if f.endswith(".jpg")]
neg_db_12 = [0 for n in xrange(len(neg_file_list))]
for filename in neg_file_list:
img = Image.open(param.neg_dir + filename)
#check if gray
if len(np.shape(np.asarray(img))) != param.input_channel:
img = np.asarray(img)
img = np.reshape(img,(np.shape(img)[0],np.shape(img)[1],1))
img = np.concatenate((img,img,img),axis=2)
img = Image.fromarray(img)
neg_db_line = np.zeros((param.neg_per_img,param.img_size_12,param.img_size_12,param.input_channel), np.float32)
for neg_iter in xrange(param.neg_per_img):
rad_rand = randint(0,min(img.size[0],img.size[1])-1)
while(rad_rand <= param.face_minimum):
rad_rand = randint(0,min(img.size[0],img.size[1])-1)
x_rand = randint(0, img.size[0] - rad_rand - 1)
y_rand = randint(0, img.size[1] - rad_rand - 1)
neg_cropped_img = img.crop((x_rand, y_rand, x_rand + rad_rand, y_rand + rad_rand))
neg_cropped_arr = util.img2array(neg_cropped_img,param.img_size_12)
#for debugging
#neg_cropped_img.save(param.neg_dir + str(fid) + "_" + str(r) + ".jpg")
neg_db_line[neg_iter,:] = neg_cropped_arr
neg_db_12[nid] = neg_db_line
nid += 1
neg_db_12 = [elem for elem in neg_db_12 if type(elem) != int]
neg_db_12 = np.vstack(neg_db_12)
return [pos_db_12,pos_db_24,pos_db_48], neg_db_12
elif dim == param.img_size_24:
neg_db_12 = np.empty((0,param.img_size_12,param.img_size_12,param.input_channel),np.float32)
neg_file_list = [f for f in os.listdir(param.neg_dir + "neg_hard/24/") if f.startswith("12_") and f.endswith(".npy")]
for nid,db_name in enumerate(neg_file_list):
tmp = np.load(param.neg_dir + "neg_hard/24/" + db_name)
neg_db_12 = np.concatenate((neg_db_12,tmp),axis=0)
neg_db_24 = np.empty((0,param.img_size_24,param.img_size_24,param.input_channel),np.float32)
neg_file_list = [f for f in os.listdir(param.neg_dir + "neg_hard/24/") if f.startswith("24_") and f.endswith(".npy")]
for nid,db_name in enumerate(neg_file_list):
tmp = np.load(param.neg_dir + "neg_hard/24/" + db_name)
neg_db_24 = np.concatenate((neg_db_24,tmp),axis=0)
return [pos_db_12,pos_db_24,pos_db_48], neg_db_12, neg_db_24
elif dim == param.img_size_48:
neg_db_12 = np.empty((0,param.img_size_12,param.img_size_12,param.input_channel),np.float32)
neg_file_list = [f for f in os.listdir(param.neg_dir + "neg_hard/48/") if f.startswith("12_") and f.endswith(".npy")]
for nid,db_name in enumerate(neg_file_list):
tmp = np.load(param.neg_dir + "neg_hard/48/" + db_name)
neg_db_12 = np.concatenate((neg_db_12,tmp),axis=0)
neg_db_24 = np.empty((0,param.img_size_24,param.img_size_24,param.input_channel),np.float32)
neg_file_list = [f for f in os.listdir(param.neg_dir + "neg_hard/48/") if f.startswith("24_") and f.endswith(".npy")]
for nid,db_name in enumerate(neg_file_list):
tmp = np.load(param.neg_dir + "neg_hard/48/" + db_name)
neg_db_24 = np.concatenate((neg_db_24,tmp),axis=0)
neg_db_48 = np.empty((0,param.img_size_48,param.img_size_48,param.input_channel),np.float32)
neg_file_list = [f for f in os.listdir(param.neg_dir + "neg_hard/48/") if f.startswith("48_") and f.endswith(".npy")]
for nid,db_name in enumerate(neg_file_list):
tmp = np.load(param.neg_dir + "neg_hard/48/" + db_name)
neg_db_48 = np.concatenate((neg_db_48,tmp),axis=0)
return [pos_db_12,pos_db_24,pos_db_48], neg_db_12, neg_db_24, neg_db_48
def load_db_calib_train(dim):
print "Loading calibration training db..."
annot_dir = param.db_dir + "AFLW/aflw/data/"
annot_fp = open(annot_dir + "annot", "r")
raw_data = annot_fp.readlines()
#pos image cropping
x_db = [0 for _ in xrange(len(raw_data))]
for i,line in enumerate(raw_data):
parsed_line = line.split(',')
filename = parsed_line[0][3:-1]
xmin = int(parsed_line[1])
ymin = int(parsed_line[2])
xmax = xmin + int(parsed_line[3])
ymax = ymin + int(parsed_line[4][:-2])
img = Image.open(param.pos_dir+filename)
#truncated image(error)
if i == 8160 or i == 14884 or i == 14886:
continue
#check if gray
if len(np.shape(np.asarray(img))) != param.input_channel:
img = np.asarray(img)
img = np.reshape(img,(np.shape(img)[0],np.shape(img)[1],1))
img = np.concatenate((img,img,img),axis=2)
img = Image.fromarray(img)
if xmax >= img.size[0]:
xmax = img.size[0]-1
if ymax >= img.size[1]:
ymax = img.size[1]-1
x_db_list = [0 for _ in xrange(param.cali_patt_num)]
for si,s in enumerate(param.cali_scale):
for xi,x in enumerate(param.cali_off_x):
for yi,y in enumerate(param.cali_off_y):
new_xmin = xmin - x*float(xmax-xmin)/s
new_ymin = ymin - y*float(ymax-ymin)/s
new_xmax = new_xmin+float(xmax-xmin)/s
new_ymax = new_ymin+float(ymax-ymin)/s
new_xmin = int(new_xmin)
new_ymin = int(new_ymin)
new_xmax = int(new_xmax)
new_ymax = int(new_ymax)
if new_xmin < 0 or new_ymin < 0 or new_xmax >= img.size[0] or new_ymax >= img.size[1]:
continue
cropped_img = util.img2array(img.crop((new_xmin, new_ymin, new_xmax, new_ymax)),dim)
calib_idx = si*len(param.cali_off_x)*len(param.cali_off_y)+xi*len(param.cali_off_y)+yi
#for debugging
#cropped_img.save(param.pos_dir + str(i) + ".jpg")
x_db_list[calib_idx] = [cropped_img,calib_idx]
x_db_list = [elem for elem in x_db_list if type(elem) != int]
if len(x_db_list) > 0:
x_db[i] = x_db_list
x_db = [elem for elem in x_db if type(elem) != int]
x_db = [x_db[i][j] for i in xrange(len(x_db)) for j in xrange(len(x_db[i]))]
return x_db
def load_db_test():
print "Loading test db..."
annot_dir = param.test_dir + "FDDB-folds/"
test_img_name = [0 for _ in range(param.fold_num)]
test_annot = [0 for _ in range(param.fold_num)]
for fid in xrange(1,param.fold_num+1):
print fid, "/", 10, "folds is loading..."
fold_img_name = []
fold_annot = []
index = str(fid).zfill(2)
annot_file = annot_dir + "FDDB-fold-" + index + "-ellipseList.txt"
fp = open(annot_file)
raw_data = fp.readlines()
stage = 0
for parsed_data in raw_data:
if stage == 0:
file_name = parsed_data.rstrip()
stage = 1
elif stage == 1:
face_num = int(parsed_data)
it = 0
fold_img_name.append(file_name)
fold_annot_line = [0 for r in xrange(face_num)]
stage = 2
elif stage == 2:
splitted = parsed_data.split()
y_rad = max([float(splitted[0]) * math.cos(abs(float(splitted[2]))), float(splitted[0]) * math.sin(abs(float(splitted[2])))])
x_rad = max([float(splitted[1]) * math.sin(abs(float(splitted[2]))), float(splitted[1]) * math.cos(abs(float(splitted[2])))])
x_min = float(splitted[3]) - x_rad
x_max = float(splitted[3]) + x_rad
y_min = float(splitted[4]) - y_rad
y_max = float(splitted[4]) + y_rad
fold_annot_line[it] = [x_min, y_min, x_max, y_max]
it += 1
face_num -= 1
if face_num == 0:
fold_annot.append(fold_annot_line)
stage = 0
fp.close()
test_img_name[fid-1] = fold_img_name
test_annot[fid-1] = fold_annot
return test_img_name, test_annot