150 lines
6.7 KiB
Python
150 lines
6.7 KiB
Python
import numpy as np
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import math
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import cv2
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def matrix_to_quaternion(m):
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t = 0.0
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q = [0.0, 0.0, 0, 0.0]
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if m[2,2] < 0:
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if m[0,0] > m[1,1]:
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t = 1 + m[0,0] - m[1,1] - m[2,2]
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q = [t, m[0,1]+m[1,0], m[2,0]+m[0,2], m[1,2]-m[2,1]]
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else:
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t = 1 - m[0,0] + m[1,1] - m[2,2]
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q = [m[0,1]+m[1,0], t, m[1,2]+m[2,1], m[2,0]-m[0,2]]
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else:
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if m[0,0] < -m[1,1]:
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t = 1 - m[0,0] - m[1,1] + m[2,2]
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q = [m[2,0]+m[0,2], m[1,2]+m[2,1], t, m[0,1]-m[1,0]]
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else:
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t = 1 + m[0,0] + m[1,1] + m[2,2]
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q = [m[1,2]-m[2,1], m[2,0]-m[0,2], m[0,1]-m[1,0], t]
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return np.array(q) / (math.sqrt(t)* 0.5)
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def landmarks(tensor, crop_info):
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crop_x1, crop_y1, scale_x, scale_y = crop_info
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t_main = tensor[0:66].reshape((66,784))
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t_m = t_main.argmax(1)
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indices = np.expand_dims(t_m, 1)
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t_off_x = np.take_along_axis(tensor[66:132].reshape((66,784)), indices, 1).reshape((66,))
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p = np.clip(t_off_x, 0.0000001, 0.9999999)
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t_off_x = 13.9375 * np.log(p / (1 - p))
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t_x = crop_y1 + scale_y * (223. * np.floor(t_m / 28) / 27.+ t_off_x)
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t_off_y = np.take_along_axis(tensor[132:198].reshape((66,784)), indices, 1).reshape((66,))
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p = np.clip(t_off_y, 0.0000001, 0.9999999)
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t_off_y = 13.9375 * np.log(p / (1 - p))
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t_y = crop_x1 + scale_x * (223. * np.floor(np.mod(t_m, 28)) / 27. + t_off_y)
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t_conf = np.take_along_axis(t_main, indices, 1).reshape((66,))
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lms = np.stack([t_x, t_y, t_conf], 1)
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lms[np.isnan(lms).any(axis=1)] = np.array([0.,0.,0.], dtype=np.float32)
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return (np.average(t_conf), lms)
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def run( models, crop, crop_info):
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output = models.landmarks.run([], {"input": crop})[0][0]
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confidence, lms = landmarks(output, crop_info)
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return (confidence, lms)
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def estimate_depth( face_info, width, height):
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camera = np.array([[width, 0, width/2], [0, width, height/2], [0, 0, 1]], np.float32)
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inverse_camera = np.linalg.inv(camera)
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contour_pts = [0,1,8,15,16,27,28,29,30,31,32,33,34,35]
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lms = np.concatenate((face_info.lms, np.array([[face_info.eye_state[0][1], face_info.eye_state[0][2], face_info.eye_state[0][3]], [face_info.eye_state[1][1], face_info.eye_state[1][2], face_info.eye_state[1][3]]], np.float32)), 0)
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image_pts = np.array(lms)[contour_pts, 0:2]
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success = False
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if face_info.rotation is not None:
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success, face_info.rotation, face_info.translation = cv2.solvePnP(face_info.contour, image_pts, camera, np.zeros((4,1)), useExtrinsicGuess=True, rvec=np.transpose(face_info.rotation), tvec=np.transpose(face_info.translation), flags=cv2.SOLVEPNP_ITERATIVE)
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else:
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rvec = np.array([0, 0, 0], np.float32)
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tvec = np.array([0, 0, 0], np.float32)
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success, face_info.rotation, face_info.translation = cv2.solvePnP(face_info.contour, image_pts, camera, np.zeros((4,1)), useExtrinsicGuess=True, rvec=rvec, tvec=tvec, flags=cv2.SOLVEPNP_ITERATIVE)
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if not success:
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face_info.rotation = np.array([0.0, 0.0, 0.0], np.float32)
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face_info.translation = np.array([0.0, 0.0, 0.0], np.float32)
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return False, np.zeros(4), np.zeros(3), 99999., pts_3d, lms
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rotation = face_info.rotation
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translation = face_info.translation
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pts_3d = np.zeros((70,3), np.float32)
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face_info.rotation = np.transpose(face_info.rotation)
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face_info.translation = np.transpose(face_info.translation)
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rmat, _ = cv2.Rodrigues(rotation)
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inverse_rotation = np.linalg.inv(rmat)
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t_reference = face_info.face_3d.dot(rmat.transpose())
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t_reference = t_reference + face_info.translation
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t_reference = t_reference.dot(camera.transpose())
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t_depth = t_reference[:, 2]
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t_depth[t_depth == 0] = 0.000001
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t_depth_e = np.expand_dims(t_depth[:],1)
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t_reference = t_reference[:] / t_depth_e
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pts_3d[0:66] = np.stack([lms[0:66,0], lms[0:66,1], np.ones((66,))], 1) * t_depth_e[0:66]
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pts_3d[0:66] = (pts_3d[0:66].dot(inverse_camera.transpose()) - face_info.translation).dot(inverse_rotation.transpose())
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for i, pt in enumerate(face_info.face_3d[66:70]):
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if i == 2:
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# Right eyeball
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# Eyeballs have an average diameter of 12.5mm and and the distance between eye corners is 30-35mm, so a conversion factor of 0.385 can be applied
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eye_center = (pts_3d[36] + pts_3d[39]) / 2.0
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d_corner = np.linalg.norm(pts_3d[36] - pts_3d[39])
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depth = 0.385 * d_corner
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pt_3d = np.array([eye_center[0], eye_center[1], eye_center[2] - depth])
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pts_3d[68] = pt_3d
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continue
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if i == 3:
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# Left eyeball
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eye_center = (pts_3d[42] + pts_3d[45]) / 2.0
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d_corner = np.linalg.norm(pts_3d[42] - pts_3d[45])
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depth = 0.385 * d_corner
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pt_3d = np.array([eye_center[0], eye_center[1], eye_center[2] - depth])
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pts_3d[69] = pt_3d
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continue
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if i == 0:
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d1 = np.linalg.norm(lms[66,0:2] - lms[36,0:2])
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d2 = np.linalg.norm(lms[66,0:2] - lms[39,0:2])
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d = d1 + d2
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pt = (pts_3d[36] * d1 + pts_3d[39] * d2) / d
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if i == 1:
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d1 = np.linalg.norm(lms[67,0:2] - lms[42,0:2])
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d2 = np.linalg.norm(lms[67,0:2] - lms[45,0:2])
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d = d1 + d2
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pt = (pts_3d[42] * d1 + pts_3d[45] * d2) / d
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if i < 2:
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reference = rmat.dot(pt)
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reference = reference + face_info.translation
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reference = camera.dot(reference)
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depth = reference[2]
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pt_3d = np.array([lms[66+i][0] * depth, lms[66+i][1] * depth, depth], np.float32)
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pt_3d = inverse_camera.dot(pt_3d)
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pt_3d = pt_3d - face_info.translation
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pt_3d = inverse_rotation.dot(pt_3d)
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pts_3d[66+i,:] = pt_3d[:]
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pts_3d[np.isnan(pts_3d).any(axis=1)] = np.array([0.,0.,0.], dtype=np.float32)
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pnp_error = np.power(lms[0:17,0:2] - t_reference[0:17,0:2], 2).sum()
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pnp_error += np.power(lms[30,0:2] - t_reference[30,0:2], 2).sum()
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if np.isnan(pnp_error):
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pnp_error = 9999999.
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pnp_error = math.sqrt(pnp_error / (2.0 * image_pts.shape[0]))
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if pnp_error > 300:
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face_info.fail_count += 1
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if face_info.fail_count > 5:
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# Something went wrong with adjusting the 3D model
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print(f"Detected anomaly when 3D fitting face {0}. Resetting.")
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face_info.rotation = None
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face_info.translation = np.array([0.0, 0.0, 0.0], np.float32)
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face_info.update_counts = np.zeros((66,2))
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face_info.update_contour()
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else:
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face_info.fail_count = 0
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euler = cv2.RQDecomp3x3(rmat)[0]
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return True, matrix_to_quaternion(rmat), euler, pnp_error, pts_3d, lms
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