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b.py
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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
import cv2 #OpenCV:画像処理系ライブラリ
import dlib #機械学習系ライブラリ
import imutils #OpenCVの補助
from imutils import face_utils
import numpy as np
DEVICE_ID = 0 # 使用するカメラのID 0は標準webカメラ
capture = cv2.VideoCapture(DEVICE_ID)#dlibの学習済みデータの読み込み
predictor_path = "C:/Users/p-user/project/shape_predictor_68_face_landmarks.dat/shape_predictor_68_face_landmarks.dat"
#学習済みdatファイルのパスをコピペ
detector = dlib.get_frontal_face_detector() #顔検出器の呼び出し。ただ顔だけを検出する。
predictor = dlib.shape_predictor(predictor_path) #顔から目鼻などランドマークを出力する
while(True): #カメラから連続で画像を取得する
ret, frame = capture.read() #カメラからキャプチャしてframeに１コマ分の画像データを入れる
frame = cv2.flip(frame, 1) # ミラー表示
frame = imutils.resize(frame, width=1000) #frameの画像の表示サイズを整える
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) #gray scaleに変換する
rects = detector(gray, 0) #grayから顔を検出
image_points = None
for rect in rects:
shape = predictor(gray, rect)
shape = face_utils.shape_to_np(shape)
for (x, y) in shape: #顔全体の68箇所のランドマークをプロット
cv2.circle(frame, (x, y), 1, (255, 255, 255), -1)
image_points = np.array([
tuple(shape[30]),#鼻頭
tuple(shape[21]),
tuple(shape[22]),
tuple(shape[39]),
tuple(shape[42]),
tuple(shape[31]),
tuple(shape[35]),
tuple(shape[48]),
tuple(shape[54]),
tuple(shape[57]),
tuple(shape[8]),
],dtype='double')
if len(rects) > 0:
cv2.FONT_HERSHEY_PLAIN,( 0.7, (0, 0, 255), 2)
model_points = np.array([
(0.0,0.0,0.0), # 30
(-30.0,-125.0,-30.0), # 21
(30.0,-125.0,-30.0), # 22
(-60.0,-70.0,-60.0), # 39
(60.0,-70.0,-60.0), # 42
(-40.0,40.0,-50.0), # 31
(40.0,40.0,-50.0), # 35
(-70.0,130.0,-100.0), # 48
(70.0,130.0,-100.0), # 54
(0.0,158.0,-10.0), # 57
(0.0,250.0,-50.0) # 8
])
size = frame.shape
focal_length = size[1]
center = (size[1] // 2, size[0] // 2) #顔の中心座標
camera_matrix = np.array([
[focal_length, 0, center[0]],
[0, focal_length, center[1]],
[0, 0, 1]
], dtype='double')
dist_coeffs = np.zeros((4, 1))
(success, rotation_vector, translation_vector) = cv2.solvePnP(model_points, image_points, camera_matrix,
dist_coeffs, flags=cv2.SOLVEPNP_ITERATIVE)
#回転行列とヤコビアン
(rotation_matrix, jacobian) = cv2.Rodrigues(rotation_vector)
mat = np.hstack((rotation_matrix, translation_vector))
#yaw,pitch,rollの取り出し
(_, _, _, _, _, _, eulerAngles) = cv2.decomposeProjectionMatrix(mat)
yaw = eulerAngles[1]
pitch = eulerAngles[0]
roll = eulerAngles[2]
print("yaw",int(yaw),"pitch",int(pitch),"roll",int(roll))#頭部姿勢データの取り出し
# 顔の向きを分類する
direction = "Forward"
if yaw > 15:
direction = "Right"
elif yaw < -15:
direction = "Left"
if pitch > 15:
direction = "Up"
elif pitch < -15:
direction = "Down"
cv2.putText(frame, 'yaw : ' + str(int(yaw)), (20, 10), cv2.FONT_HERSHEY_PLAIN, 1, (0, 0, 255), 2)
cv2.putText(frame, 'pitch : ' + str(int(pitch)), (20, 25), cv2.FONT_HERSHEY_PLAIN, 1, (0, 0, 255), 2)
cv2.putText(frame, 'roll : ' + str(int(roll)), (20, 40), cv2.FONT_HERSHEY_PLAIN, 1, (0, 0, 255), 2)
cv2.putText(frame, 'Direction : ' + direction, (20, 55), cv2.FONT_HERSHEY_PLAIN, 1, (0, 255, 0), 2)
(nose_end_point2D, _) = cv2.projectPoints(np.array([(0.0, 0.0, 500.0)]), rotation_vector,
translation_vector, camera_matrix, dist_coeffs)
#計算に使用した点のプロット/顔方向のベクトルの表示
for p in image_points:
cv2.drawMarker(frame, (int(p[0]), int(p[1])), (0.0, 1.409845, 255),markerType=cv2.MARKER_CROSS, thickness=1)
p1 = (int(image_points[0][0]), int(image_points[0][1]))
p2 = (int(nose_end_point2D[0][0][0]), int(nose_end_point2D[0][0][1]))
cv2.arrowedLine(frame, p1, p2, (255, 0, 0), 2)
cv2.imshow('frame',frame) # 画像を表示する
if cv2.waitKey(1) & 0xFF == ord('q'): #qを押すとbreakしてwhileから抜ける
break
capture.release() #video captureを終了する
cv2.destroyAllWindows() #windowを閉じる