def load_negative_training_db_12(neg_dir):
# neg image cropping
nid = 0
neg_file_list = [f for f in os.listdir(neg_dir) if f.endswith(".jpg")]
print("Loading negative training db: {} images from {}".format(len(neg_file_list),neg_dir))
neg_db_12 = [0 for n in range(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 range(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 neg_db_12
#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)
#12-net
#box: xmin, ymin, xmax, ymax, score, cropped_img, scale
neg_box = util.sliding_window(img, param.thr_12, net_12, input_12_node)
#12-calib
neg_db_tmp = np.zeros((len(neg_box), param.img_size_12, param.img_size_12,
param.input_channel), np.float32)
for id_, box in enumerate(neg_box):
neg_db_tmp[id_, :] = util.img2array(box[5], param.img_size_12)
calib_result = net_12_calib.prediction.eval(
feed_dict={input_12_node: neg_db_tmp})
neg_box = util.calib_box(neg_box, calib_result, img)
#NMS for each scale
scale_cur = 0
scale_box = []
suppressed = []
for id_, box in enumerate(neg_box):
if box[6] == scale_cur:
scale_box.append(box)
if box[6] != scale_cur or id_ == len(neg_box) - 1:
suppressed += util.NMS(scale_box)
scale_cur = box[6]
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)
#12-net
#xmin, ymin, xmax, ymax, score, cropped_img, scale
result_box = util.sliding_window(img, param.thr_12, net_12,
input_12_node)
#12-calib
result_db_tmp = np.zeros((len(result_box), param.img_size_12,
param.img_size_12, param.input_channel),
np.float32)
for id_, box in enumerate(result_box):
result_db_tmp[id_, :] = util.img2array(box[5], param.img_size_12)
calib_result = net_12_calib.prediction.eval(
feed_dict={input_12_node: result_db_tmp})
result_box = util.calib_box(result_box, calib_result, img)
#NMS for each scale
scale_cur = 0
scale_box = []
suppressed = []
for id_, box in enumerate(result_box):
if box[6] == scale_cur:
scale_box.append(box)
if box[6] != scale_cur or id_ == len(result_box) - 1:
suppressed += util.NMS(scale_box)
scale_cur = box[6]
def load_db_calib_train(dim):
print "Loading calibration training db..."
annot_fp = open('/home/the/scratch/label_script.txt', "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])
filename = parsed_line[0]
timeind = int(parsed_line[1])
xmin = int(parsed_line[2])
ymin = int(parsed_line[3])
xmax = int(parsed_line[4])
ymax = int(parsed_line[5])
img = util.open_nc(filename, timeind)
#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(
util.img_crop(img, 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_detect_train(dim):
print "Loading positive training db..."
# annot_dir = param.db_dir + "AFLW/aflw/data/"
annot_fp = open('/home/the/scratch/label_script.txt', "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]
timeind = int(parsed_line[1])
xmin = int(parsed_line[2])
ymin = int(parsed_line[3])
xmax = int(parsed_line[4])
ymax = int(parsed_line[5])
# img = Image.open(param.pos_dir+filename)
img = util.open_nc(filename, timeind)
#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 = util.img_crop(img, xmin, ymin, xmax, ymax)
# flipped_img = cropped_img.transpose(Image.FLIP_LEFT_RIGHT)
flipped_img = util.img_flip(cropped_img)
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(".nc")]
neg_file_list, timeind_list = util.get_nc_db(param.neg_dir)
neg_db_12 = [0 for n in xrange(len(neg_file_list))]
for iii, filename in enumerate(neg_file_list):
# img = Image.open(param.neg_dir + filename)
timeind = timeind_list[iii]
img = util.open_nc(filename, timeind)
#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)
rad_rand = randint(param.face_minimum, 80)
# 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 = util.img_crop(img, 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_positive_training_db(annot_filename):
annot_fp = open(annot_filename, "r")
raw_data = annot_fp.readlines()
print("Loading positive training db: {} images from {}".format(len(raw_data),annot_filename))
# pos image cropping
pos_db_12 = [0 for _ in range(len(raw_data))]
pos_db_24 = [0 for _ in range(len(raw_data))]
pos_db_48 = [0 for _ in range(len(raw_data))]
for i, line in enumerate(raw_data):
parsed_line = line.split(',')
# print(parsed_line)
filename = parsed_line[0]
xmin = int(parsed_line[1])
ymin = int(parsed_line[2])
xmax = xmin + int(parsed_line[3])
ymax = ymin + int(parsed_line[4])
# print((xmin, ymin, xmax, ymax))
# exit()
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)
return pos_db_12, pos_db_24, pos_db_48
def load_db_calib_train(dim):
annot_dir = param.db_dir + "AFLW/aflw/data/"
annot_filename = annot_dir + "annot"
annot_fp = open(annot_filename, "r")
raw_data = annot_fp.readlines()
print("Loading calibration training db: {} from {}".format(len(raw_data),annot_filename))
#pos image cropping
x_db = [0 for _ in range(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 range(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 range(len(x_db)) for j in range(len(x_db[i]))]
return x_db
