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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 | |