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import numpy as np
from mtcnn import MTCNN
def to_rgb(img):
w, h = img.shape
ret = np.empty((w, h, 3), dtype=np.uint8)
ret[:, :, 0] = ret[:, :, 1] = ret[:, :, 2] = img
return ret
def aligner():
return MTCNN(min_face_size=30)
def align(orig_img, aligner):
""" run MTCNN face detector """
if orig_img.ndim < 2:
return None
if orig_img.ndim == 2:
orig_img = to_rgb(orig_img)
orig_img = orig_img[:, :, 0:3]
detect_results = aligner.detect_faces(orig_img)
cropped_arr = []
bounding_boxes_arr = []
for dic in detect_results:
if dic['confidence'] < 0.9:
continue
x, y, width, height = dic['box']
if width < 30 or height < 30:
continue
bb = [y, x, y + height, x + width]
cropped = orig_img[bb[0]:bb[2], bb[1]:bb[3], :]
cropped_arr.append(np.copy(cropped))
bounding_boxes_arr.append(bb)
return cropped_arr, bounding_boxes_arr
# if nrof_faces > 0:
# det = bounding_boxes[0]['box']
# det_arr = []
# img_size = np.asarray(orig_img.shape)[0:2]
# if nrof_faces > 1:
# margin = margin / 1.5
# if detect_multiple_faces:
# for i in range(nrof_faces):
# det_arr.append(np.squeeze(bounding_boxes[i]['box']))
# else:
# bounding_box_size = (det[1] + det[3])
# 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_arr.append(det[index, :])
# else:
# det_arr.append(np.squeeze(det))
#
# cropped_arr = []
# bounding_boxes_arr = []
# for i, det in enumerate(det_arr):
# det = np.squeeze(det)
# bb = np.zeros(4, dtype=np.int32)
# # add in margin
# marg1 = int((det[2] - det[0]) * margin)
# marg2 = int((det[3] - det[1]) * margin)
#
# bb[0] = max(det[0] - marg1 / 2, 0)
# bb[1] = max(det[1] - marg2 / 2, 0)
# bb[2] = min(det[0] + det[2] + marg1 / 2, img_size[0])
# bb[3] = min(det[1] + det[3] + marg2 / 2, img_size[1])
# cropped = orig_img[bb[0]:bb[2], bb[1]: bb[3], :]
# cropped_arr.append(cropped)
# bounding_boxes_arr.append([bb[0], bb[1], bb[2], bb[3]])
# return cropped_arr, bounding_boxes_arr
# else:
# return None
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