#!/usr/bin/python3
# -*- coding: utf-8 -*-
# MIT License
#
# Copyright (c) 2019 Iván de Paz Centeno
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included in all
# copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
# SOFTWARE.
#
# This code is derived from the MTCNN implementation of David Sandberg for Facenet
# (https://github.com/davidsandberg/facenet/)
# It has been rebuilt from scratch, taking the David Sandberg's implementation as a reference.
#
import cv2
import numpy as np
import pkg_resources
from mtcnn.exceptions import InvalidImage
from mtcnn.network.factory import NetworkFactory
__author__ = "Iván de Paz Centeno"
class StageStatus(object):
"""
Keeps status between MTCNN stages
"""
def __init__(self, pad_result: tuple = None, width=0, height=0):
self.width = width
self.height = height
self.dy = self.edy = self.dx = self.edx = self.y = self.ey = self.x = self.ex = self.tmpw = self.tmph = []
if pad_result is not None:
self.update(pad_result)
def update(self, pad_result: tuple):
s = self
s.dy, s.edy, s.dx, s.edx, s.y, s.ey, s.x, s.ex, s.tmpw, s.tmph = pad_result
class MTCNN(object):
"""
Allows to perform MTCNN Detection ->
a) Detection of faces (with the confidence probability)
b) Detection of keypoints (left eye, right eye, nose, mouth_left, mouth_right)
"""
def __init__(self, weights_file: str = None, min_face_size: int = 20, steps_threshold: list = None,
scale_factor: float = 0.709):
"""
Initializes the MTCNN.
:param weights_file: file uri with the weights of the P, R and O networks from MTCNN. By default it will load
the ones bundled with the package.
:param min_face_size: minimum size of the face to detect
:param steps_threshold: step's thresholds values
:param scale_factor: scale factor
"""
if steps_threshold is None:
steps_threshold = [0.6, 0.7, 0.7]
if weights_file is None:
weights_file = pkg_resources.resource_stream('mtcnn', 'data/mtcnn_weights.npy')
self._min_face_size = min_face_size
self._steps_threshold = steps_threshold
self._scale_factor = scale_factor
self._pnet, self._rnet, self._onet = NetworkFactory().build_P_R_O_nets_from_file(weights_file)
@property
def min_face_size(self):
return self._min_face_size
@min_face_size.setter
def min_face_size(self, mfc=20):
try:
self._min_face_size = int(mfc)
except ValueError:
self._min_face_size = 20
def __compute_scale_pyramid(self, m, min_layer):
scales = []
factor_count = 0
while min_layer >= 12:
scales += [m * np.power(self._scale_factor, factor_count)]
min_layer = min_layer * self._scale_factor
factor_count += 1
return scales
@staticmethod
def __scale_image(image, scale: float):
"""
Scales the image to a given scale.
:param image:
:param scale:
:return:
"""
height, width, _ = image.shape
width_scaled = int(np.ceil(width * scale))
height_scaled = int(np.ceil(height * scale))
im_data = cv2.resize(image, (width_scaled, height_scaled), interpolation=cv2.INTER_AREA)
# Normalize the image's pixels
im_data_normalized = (im_data - 127.5) * 0.0078125
return im_data_normalized
@staticmethod
def __generate_bounding_box(imap, reg, scale, t):
# use heatmap to generate bounding boxes
stride = 2
cellsize = 12
imap = np.transpose(imap)
dx1 = np.transpose(reg[:, :, 0])
dy1 = np.transpose(reg[:, :, 1])
dx2 = np.transpose(reg[:, :, 2])
dy2 = np.transpose(reg[:, :, 3])
y, x = np.where(imap >= t)
if y.shape[0] == 1:
dx1 = np.flipud(dx1)
dy1 = np.flipud(dy1)
dx2 = np.flipud(dx2)
dy2 = np.flipud(dy2)
score = imap[(y, x)]
reg = np.transpose(np.vstack([dx1[(y, x)], dy1[(y, x)], dx2[(y, x)], dy2[(y, x)]]))
if reg.size == 0:
reg = np.empty(shape=(0, 3))
bb = np.transpose(np.vstack([y, x]))
q1 = np.fix((stride * bb + 1) / scale)
q2 = np.fix((stride * bb + cellsize) / scale)
boundingbox = np.hstack([q1, q2, np.expand_dims(score, 1), reg])
return boundingbox, reg
@staticmethod
def __nms(boxes, threshold, method):
"""
Non Maximum Suppression.
:param boxes: np array with bounding boxes.
:param threshold:
:param method: NMS method to apply. Available values ('Min', 'Union')
:return:
"""
if boxes.size == 0:
return np.empty((0, 3))
x1 = boxes[:, 0]
y1 = boxes[:, 1]
x2 = boxes[:, 2]
y2 = boxes[:, 3]
s = boxes[:, 4]
area = (x2 - x1 + 1) * (y2 - y1 + 1)
sorted_s = np.argsort(s)
pick = np.zeros_like(s, dtype=np.int16)
counter = 0
while sorted_s.size > 0:
i = sorted_s[-1]
pick[counter] = i
counter += 1
idx = sorted_s[0:-1]
xx1 = np.maximum(x1[i], x1[idx])
yy1 = np.maximum(y1[i], y1[idx])
xx2 = np.minimum(x2[i], x2[idx])
yy2 = np.minimum(y2[i], y2[idx])
w = np.maximum(0.0, xx2 - xx1 + 1)
h = np.maximum(0.0, yy2 - yy1 + 1)
inter = w * h
if method is 'Min':
o = inter / np.minimum(area[i], area[idx])
else:
o = inter / (area[i] + area[idx] - inter)
sorted_s = sorted_s[np.where(o <= threshold)]
pick = pick[0:counter]
return pick
@staticmethod
def __pad(total_boxes, w, h):
# compute the padding coordinates (pad the bounding boxes to square)
tmpw = (total_boxes[:, 2] - total_boxes[:, 0] + 1).astype(np.int32)
tmph = (total_boxes[:, 3] - total_boxes[:, 1] + 1).astype(np.int32)
numbox = total_boxes.shape[0]
dx = np.ones(numbox, dtype=np.int32)
dy = np.ones(numbox, dtype=np.int32)
edx = tmpw.copy().astype(np.int32)
edy = tmph.copy().astype(np.int32)
x = total_boxes[:, 0].copy().astype(np.int32)
y = total_boxes[:, 1].copy().astype(np.int32)
ex = total_boxes[:, 2].copy().astype(np.int32)
ey = total_boxes[:, 3].copy().astype(np.int32)
tmp = np.where(ex > w)
edx.flat[tmp] = np.expand_dims(-ex[tmp] + w + tmpw[tmp], 1)
ex[tmp] = w
tmp = np.where(ey > h)
edy.flat[tmp] = np.expand_dims(-ey[tmp] + h + tmph[tmp], 1)
ey[tmp] = h
tmp = np.where(x < 1)
dx.flat[tmp] = np.expand_dims(2 - x[tmp], 1)
x[tmp] = 1
tmp = np.where(y < 1)
dy.flat[tmp] = np.expand_dims(2 - y[tmp], 1)
y[tmp] = 1
return dy, edy, dx, edx, y, ey, x, ex, tmpw, tmph
@staticmethod
def __rerec(bbox):
# convert bbox to square
height = bbox[:, 3] - bbox[:, 1]
width = bbox[:, 2] - bbox[:, 0]
max_side_length = np.maximum(width, height)
bbox[:, 0] = bbox[:, 0] + width * 0.5 - max_side_length * 0.5
bbox[:, 1] = bbox[:, 1] + height * 0.5 - max_side_length * 0.5
bbox[:, 2:4] = bbox[:, 0:2] + np.transpose(np.tile(max_side_length, (2, 1)))
return bbox
@staticmethod
def __bbreg(boundingbox, reg):
# calibrate bounding boxes
if reg.shape[1] == 1:
reg = np.reshape(reg, (reg.shape[2], reg.shape[3]))
w = boundingbox[:, 2] - boundingbox[:, 0] + 1
h = boundingbox[:, 3] - boundingbox[:, 1] + 1
b1 = boundingbox[:, 0] + reg[:, 0] * w
b2 = boundingbox[:, 1] + reg[:, 1] * h
b3 = boundingbox[:, 2] + reg[:, 2] * w
b4 = boundingbox[:, 3] + reg[:, 3] * h
boundingbox[:, 0:4] = np.transpose(np.vstack([b1, b2, b3, b4]))
return boundingbox
def detect_faces(self, img) -> list:
"""
Detects bounding boxes from the specified image.
:param img: image to process
:return: list containing all the bounding boxes detected with their keypoints.
"""
if img is None or not hasattr(img, "shape"):
raise InvalidImage("Image not valid.")
height, width, _ = img.shape
stage_status = StageStatus(width=width, height=height)
m = 12 / self._min_face_size
min_layer = np.amin([height, width]) * m
scales = self.__compute_scale_pyramid(m, min_layer)
stages = [self.__stage1, self.__stage2, self.__stage3]
result = [scales, stage_status]
# We pipe here each of the stages
for stage in stages:
result = stage(img, result[0], result[1])
[total_boxes, points] = result
bounding_boxes = []
for bounding_box, keypoints in zip(total_boxes, points.T):
x = max(0, int(bounding_box[0]))
y = max(0, int(bounding_box[1]))
width = int(bounding_box[2] - x)
height = int(bounding_box[3] - y)
bounding_boxes.append({
'box': [x, y, width, height],
'confidence': bounding_box[-1],
'keypoints': {
'left_eye': (int(keypoints[0]), int(keypoints[5])),
'right_eye': (int(keypoints[1]), int(keypoints[6])),
'nose': (int(keypoints[2]), int(keypoints[7])),
'mouth_left': (int(keypoints[3]), int(keypoints[8])),
'mouth_right': (int(keypoints[4]), int(keypoints[9])),
}
})
return bounding_boxes
def __stage1(self, image, scales: list, stage_status: StageStatus):
"""
First stage of the MTCNN.
:param image:
:param scales:
:param stage_status:
:return:
"""
total_boxes = np.empty((0, 9))
status = stage_status
for scale in scales:
scaled_image = self.__scale_image(image, scale)
img_x = np.expand_dims(scaled_image, 0)
img_y = np.transpose(img_x, (0, 2, 1, 3))
out = self._pnet.predict(img_y)
out0 = np.transpose(out[0], (0, 2, 1, 3))
out1 = np.transpose(out[1], (0, 2, 1, 3))
boxes, _ = self.__generate_bounding_box(out1[0, :, :, 1].copy(),
out0[0, :, :, :].copy(), scale, self._steps_threshold[0])
# inter-scale nms
pick = self.__nms(boxes.copy(), 0.5, 'Union')
if boxes.size > 0 and pick.size > 0:
boxes = boxes[pick, :]
total_boxes = np.append(total_boxes, boxes, axis=0)
numboxes = total_boxes.shape[0]
if numboxes > 0:
pick = self.__nms(total_boxes.copy(), 0.7, 'Union')
total_boxes = total_boxes[pick, :]
regw = total_boxes[:, 2] - total_boxes[:, 0]
regh = total_boxes[:, 3] - total_boxes[:, 1]
qq1 = total_boxes[:, 0] + total_boxes[:, 5] * regw
qq2 = total_boxes[:, 1] + total_boxes[:, 6] * regh
qq3 = total_boxes[:, 2] + total_boxes[:, 7] * regw
qq4 = total_boxes[:, 3] + total_boxes[:, 8] * regh
total_boxes = np.transpose(np.vstack([qq1, qq2, qq3, qq4, total_boxes[:, 4]]))
total_boxes = self.__rerec(total_boxes.copy())
total_boxes[:, 0:4] = np.fix(total_boxes[:, 0:4]).astype(np.int32)
status = StageStatus(self.__pad(total_boxes.copy(), stage_status.width, stage_status.height),
width=stage_status.width, height=stage_status.height)
return total_boxes, status
def __stage2(self, img, total_boxes, stage_status: StageStatus):
"""
Second stage of the MTCNN.
:param img:
:param total_boxes:
:param stage_status:
:return:
"""
num_boxes = total_boxes.shape[0]
if num_boxes == 0:
return total_boxes, stage_status
# second stage
tempimg = np.zeros(shape=(24, 24, 3, num_boxes))
for k in range(0, num_boxes):
tmp = np.zeros((int(stage_status.tmph[k]), int(stage_status.tmpw[k]), 3))
tmp[stage_status.dy[k] - 1:stage_status.edy[k], stage_status.dx[k] - 1:stage_status.edx[k], :] = \
img[stage_status.y[k] - 1:stage_status.ey[k], stage_status.x[k] - 1:stage_status.ex[k], :]
if tmp.shape[0] > 0 and tmp.shape[1] > 0 or tmp.shape[0] == 0 and tmp.shape[1] == 0:
tempimg[:, :, :, k] = cv2.resize(tmp, (24, 24), interpolation=cv2.INTER_AREA)
else:
return np.empty(shape=(0,)), stage_status
tempimg = (tempimg - 127.5) * 0.0078125
tempimg1 = np.transpose(tempimg, (3, 1, 0, 2))
out = self._rnet.predict(tempimg1)
out0 = np.transpose(out[0])
out1 = np.transpose(out[1])
score = out1[1, :]
ipass = np.where(score > self._steps_threshold[1])
total_boxes = np.hstack([total_boxes[ipass[0], 0:4].copy(), np.expand_dims(score[ipass].copy(), 1)])
mv = out0[:, ipass[0]]
if total_boxes.shape[0] > 0:
pick = self.__nms(total_boxes, 0.7, 'Union')
total_boxes = total_boxes[pick, :]
total_boxes = self.__bbreg(total_boxes.copy(), np.transpose(mv[:, pick]))
total_boxes = self.__rerec(total_boxes.copy())
return total_boxes, stage_status
def __stage3(self, img, total_boxes, stage_status: StageStatus):
"""
Third stage of the MTCNN.
:param img:
:param total_boxes:
:param stage_status:
:return:
"""
num_boxes = total_boxes.shape[0]
if num_boxes == 0:
return total_boxes, np.empty(shape=(0,))
total_boxes = np.fix(total_boxes).astype(np.int32)
status = StageStatus(self.__pad(total_boxes.copy(), stage_status.width, stage_status.height),
width=stage_status.width, height=stage_status.height)
tempimg = np.zeros((48, 48, 3, num_boxes))
for k in range(0, num_boxes):
tmp = np.zeros((int(status.tmph[k]), int(status.tmpw[k]), 3))
tmp[status.dy[k] - 1:status.edy[k], status.dx[k] - 1:status.edx[k], :] = \
img[status.y[k] - 1:status.ey[k], status.x[k] - 1:status.ex[k], :]
if tmp.shape[0] > 0 and tmp.shape[1] > 0 or tmp.shape[0] == 0 and tmp.shape[1] == 0:
tempimg[:, :, :, k] = cv2.resize(tmp, (48, 48), interpolation=cv2.INTER_AREA)
else:
return np.empty(shape=(0,)), np.empty(shape=(0,))
tempimg = (tempimg - 127.5) * 0.0078125
tempimg1 = np.transpose(tempimg, (3, 1, 0, 2))
out = self._onet.predict(tempimg1)
out0 = np.transpose(out[0])
out1 = np.transpose(out[1])
out2 = np.transpose(out[2])
score = out2[1, :]
points = out1
ipass = np.where(score > self._steps_threshold[2])
points = points[:, ipass[0]]
total_boxes = np.hstack([total_boxes[ipass[0], 0:4].copy(), np.expand_dims(score[ipass].copy(), 1)])
mv = out0[:, ipass[0]]
w = total_boxes[:, 2] - total_boxes[:, 0] + 1
h = total_boxes[:, 3] - total_boxes[:, 1] + 1
points[0:5, :] = np.tile(w, (5, 1)) * points[0:5, :] + np.tile(total_boxes[:, 0], (5, 1)) - 1
points[5:10, :] = np.tile(h, (5, 1)) * points[5:10, :] + np.tile(total_boxes[:, 1], (5, 1)) - 1
if total_boxes.shape[0] > 0:
total_boxes = self.__bbreg(total_boxes.copy(), np.transpose(mv))
pick = self.__nms(total_boxes.copy(), 0.7, 'Min')
total_boxes = total_boxes[pick, :]
points = points[:, pick]
return total_boxes, points
