mirror of
https://github.com/Shawn-Shan/fawkes.git
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e9f1a50653
Former-commit-id: 14c0173d9f573e7ccb275b3e366505057ac2c9b1 [formerly e359682d967212b4b3f27923fd659bbade7880e5] Former-commit-id: a44577686ff64da031231ea323c681185daa8b0d
71 lines
2.6 KiB
Python
71 lines
2.6 KiB
Python
import numpy as np
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from fawkes import create_mtcnn, run_detect_face
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np_load_old = np.load
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np.load = lambda *a, **k: np_load_old(*a, allow_pickle=True, **k)
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def to_rgb(img):
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w, h = img.shape
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ret = np.empty((w, h, 3), dtype=np.uint8)
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ret[:, :, 0] = ret[:, :, 1] = ret[:, :, 2] = img
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return ret
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def aligner(sess):
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pnet, rnet, onet = create_mtcnn(sess, None)
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return [pnet, rnet, onet]
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def align(orig_img, aligner, margin=0.8, detect_multiple_faces=True):
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pnet, rnet, onet = aligner
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minsize = 20 # minimum size of face
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threshold = [0.6, 0.7, 0.7] # three steps's threshold
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factor = 0.709 # scale factor
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if orig_img.ndim < 2:
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return None
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if orig_img.ndim == 2:
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orig_img = to_rgb(orig_img)
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orig_img = orig_img[:, :, 0:3]
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bounding_boxes, _ = run_detect_face(orig_img, minsize, pnet, rnet, onet, threshold, factor)
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nrof_faces = bounding_boxes.shape[0]
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if nrof_faces > 0:
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det = bounding_boxes[:, 0:4]
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det_arr = []
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img_size = np.asarray(orig_img.shape)[0:2]
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if nrof_faces > 1:
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margin = margin / 1.5
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if detect_multiple_faces:
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for i in range(nrof_faces):
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det_arr.append(np.squeeze(det[i]))
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else:
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bounding_box_size = (det[:, 2] - det[:, 0]) * (det[:, 3] - det[:, 1])
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img_center = img_size / 2
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offsets = np.vstack([(det[:, 0] + det[:, 2]) / 2 - img_center[1],
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(det[:, 1] + det[:, 3]) / 2 - img_center[0]])
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offset_dist_squared = np.sum(np.power(offsets, 2.0), 0)
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index = np.argmax(bounding_box_size - offset_dist_squared * 2.0) # some extra weight on the centering
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det_arr.append(det[index, :])
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else:
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det_arr.append(np.squeeze(det))
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cropped_arr = []
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bounding_boxes_arr = []
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for i, det in enumerate(det_arr):
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det = np.squeeze(det)
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bb = np.zeros(4, dtype=np.int32)
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side_1 = int((det[2] - det[0]) * margin)
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side_2 = int((det[3] - det[1]) * margin)
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bb[0] = np.maximum(det[0] - side_1 / 2, 0)
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bb[1] = np.maximum(det[1] - side_1 / 2, 0)
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bb[2] = np.minimum(det[2] + side_2 / 2, img_size[1])
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bb[3] = np.minimum(det[3] + side_2 / 2, img_size[0])
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cropped = orig_img[bb[1]:bb[3], bb[0]:bb[2], :]
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cropped_arr.append(cropped)
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bounding_boxes_arr.append([bb[0], bb[1], bb[2], bb[3]])
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return cropped_arr, bounding_boxes_arr
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else:
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return None
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