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from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from scipy import misc
import os
import tensorflow.compat.v1 as tf
import numpy as np
import facenet
import detect_face
import imageio
from PIL import Image
class preprocesses:
def __init__(self, input_datadir, output_datadir):
self.input_datadir = input_datadir
self.output_datadir = output_datadir
def collect_data(self):
output_dir = os.path.expanduser(self.output_datadir)
if not os.path.exists(output_dir):
os.makedirs(output_dir)
dataset = facenet.get_dataset(self.input_datadir)
with tf.Graph().as_default():
gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.5)
sess = tf.Session(config=tf.ConfigProto(gpu_options=gpu_options, log_device_placement=False))
with sess.as_default():
pnet, rnet, onet = detect_face.create_mtcnn(sess, './npy')
minsize = 20 # minimum size of face
threshold = [0.5, 0.6, 0.6] # three steps's threshold
factor = 0.709 # scale factor
margin = 44
image_size = 182
random_key = np.random.randint(0, high=99999)
bounding_boxes_filename = os.path.join(output_dir, 'bounding_boxes_%05d.txt' % random_key)
with open(bounding_boxes_filename, "w") as text_file:
nrof_images_total = 0
nrof_successfully_aligned = 0
for cls in dataset:
output_class_dir = os.path.join(output_dir, cls.name)
if not os.path.exists(output_class_dir):
os.makedirs(output_class_dir)
for image_path in cls.image_paths:
nrof_images_total += 1
filename = os.path.splitext(os.path.split(image_path)[1])[0]
output_filename = os.path.join(output_class_dir, filename + '.png')
print("Image: %s" % image_path)
if not os.path.exists(output_filename):
try:
img = imageio.imread(image_path)
except (IOError, ValueError, IndexError) as e:
errorMessage = '{}: {}'.format(image_path, e)
print(errorMessage)
else:
if img.ndim < 2:
print('Unable to align "%s"' % image_path)
text_file.write('%s\n' % (output_filename))
continue
if img.ndim == 2:
img = facenet.to_rgb(img)
print('to_rgb data dimension: ', img.ndim)
img = img[:, :, 0:3]
bounding_boxes, _ = detect_face.detect_face(img, minsize, pnet, rnet, onet, threshold,
factor)
nrof_faces = bounding_boxes.shape[0]
print('No of Detected Face: %d' % nrof_faces)
if nrof_faces > 0:
det = bounding_boxes[:, 0:4]
img_size = np.asarray(img.shape)[0:2]
if nrof_faces > 1:
bounding_box_size = (det[:, 2] - det[:, 0]) * (det[:, 3] - det[:, 1])
img_center = img_size / 2
offsets = np.vstack([(det[:, 0] + det[:, 2]) / 2 - img_center[1],
(det[:, 1] + det[:, 3]) / 2 - img_center[0]])
offset_dist_squared = np.sum(np.power(offsets, 2.0), 0)
index = np.argmax(
bounding_box_size - offset_dist_squared * 2.0) # some extra weight on the centering
det = det[index, :]
det = np.squeeze(det)
bb_temp = np.zeros(4, dtype=np.int32)
# Ensure bounding box is within image boundaries
bb_temp[0] = np.maximum(det[0], 0)
bb_temp[1] = np.maximum(det[1], 0)
bb_temp[2] = np.minimum(det[2], img_size[1])
bb_temp[3] = np.minimum(det[3], img_size[0])
cropped_temp = img[bb_temp[1]:bb_temp[3], bb_temp[0]:bb_temp[2], :]
# Check if the cropped region has a valid size before resizing
if cropped_temp.shape[0] > 0 and cropped_temp.shape[1] > 0:
scaled_temp = np.array(Image.fromarray(cropped_temp).resize((image_size, image_size)))
nrof_successfully_aligned += 1
imageio.imwrite(output_filename, scaled_temp)
text_file.write('%s %d %d %d %d\n' % (output_filename, bb_temp[0], bb_temp[1], bb_temp[2], bb_temp[3]))
else:
print(f"Skipped resizing for image {image_path} due to invalid crop size")
text_file.write('%s\n' % (output_filename))
else:
print('Unable to align "%s"' % image_path)
text_file.write('%s\n' % (output_filename))
return (nrof_images_total, nrof_successfully_aligned)
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