【人臉識別——Dlib學習2】Face Landmark Detection

1. 官網文檔翻譯

http://dlib.net/face_landmark_detection.py.html

1. 這個例子展示如何找到人的正臉，並且估計它的姿態。這個姿態由68個標點描述。人臉上會被標記很多點，例如嘴的邊角，沿着眉毛，眼睛上等等。
2. 我們使用的Face detector是使用經典的HOG特征，結合線性分類器、圖像金字塔和滑動窗口檢測的算法。姿態估計器的建立是基於下文：One Millisecond Face Alignment with an Ensemble of Regression Trees by Vahid Kazemi and Josephine Sullivan, 並且在iBUG 300-W face landmark dataset進行訓練。

 

 

# -*-coding:utf-8-*-
#author: lyp time: 2018/9/10
import sys
import os
import dlib
import glob

# 本例子要求你在cmd中輸入兩個參數
# 參數一是68點文件的路徑，傳給predictor_path
# 參數二是要檢測的圖片的路徑，傳給face_folder_path
# Windows這個方式不太友好，一直提醒沒有dlib模塊。
if len(sys.argv) != 3:
    print(
        "Give the path to the trained shape predictor model as the first "
        "argument and then the directory containing the facial images.\n"
        "For example, if you are in the python_examples folder then "
        "execute this program by running:\n"
        "    ./face_landmark_detection.py shape_predictor_68_face_landmarks.dat ../examples/faces\n"
        "You can download a trained facial shape predictor from:\n"
        "    http://dlib.net/files/shape_predictor_68_face_landmarks.dat.bz2")
    exit()

# 輸入的路徑傳給對應參數
predictor_path = sys.argv[1]
faces_folder_path = sys.argv[2]


detector = dlib.get_frontal_face_detector()  # 人臉檢測器的生成
predictor = dlib.shape_predictor(predictor_path)  # 特征點提取器的生成
win = dlib.image_window()  # dlib提供的圖片窗口

# 獲取指定文件路徑下的所有.jpg文件，'*'是通配符
for f in glob.glob(os.path.join(faces_folder_path, "*.jpg")):
    print("Processing file: {}".format(f))

    img = dlib.load_rgb_image(f)

    win.clear_overlay()
    win.set_image(img)

    # Ask the detector to find the bounding boxes of each face. The 1 in the
    # second argument indicates that we should upsample the image 1 time. This
    # will make everything bigger and allow us to detect more faces.

    # 將圖像進行向上采樣一倍
    dets = detector(img, 1)
    print("Number of faces detected: {}".format(len(dets)))

    # 使用enumerate函數遍歷dets中元素
    
    for k, d in enumerate(dets):
        print("Detection {}: Left: {} Top: {} Right: {} Bottom: {}".format(
            k, d.left(), d.top(), d.right(), d.bottom()))
        
        # Get the landmarks/parts for the face in box d.
        shape = predictor(img, d)
        print("Part 0: {}, Part 1: {} ...".format(shape.part(0),
                                                  shape.part(1)))
        # Draw the face landmarks on the screen.
        win.add_overlay(shape)

    win.add_overlay(dets)
    dlib.hit_enter_to_continue()