detector = dlib.get_frontal_face_detector() predictor = dlib.shape_predictor(PREDICTOR_PATH)
时间: 2024-02-01 21:12:46 浏览: 28
这两行代码使用了dlib库中的函数。get_frontal_face_detector()返回一个面部检测器的实例,用于检测图像中的人脸。shape_predictor()函数则返回一个面部特征检测器的实例,用于检测人脸的特征点,例如眼睛、鼻子、嘴巴等。PREDICTOR_PATH是一个字符串变量,指向训练好的面部特征检测器的模型文件路径。这两个实例将在后续的人脸识别过程中被使用。
相关问题
请根据注释补全代码。其中X是一个列表,X的列表元素是以图片为列表元素的列表。最后保存X_process中的内容于一个文件夹中(这个文件夹里可以包含若干个二级文件夹)代码:X_processed = [] detector = dlib.get_frontal_face_detector() predictor = dlib.shape_predictor("shape_predictor_68_face_landmarks.dat") for x_list in X: temp_X_processed = [] for x in x_list: # write the code to detect face in the image (x) using dlib facedetection library # write the code to crop the image (x) to keep only the face, resize the cropped image to 150x150 # write the code to convert the image (x) to grayscale # append the converted image into temp_X_processed # append temp_X_processed into X_processed
```python
import dlib
import cv2
import os
X_processed = []
detector = dlib.get_frontal_face_detector()
predictor = dlib.shape_predictor("shape_predictor_68_face_landmarks.dat")
for x_list in X:
temp_X_processed = []
for x in x_list:
# detect face in the image (x) using dlib facedetection library
gray = cv2.cvtColor(x, cv2.COLOR_BGR2GRAY)
faces = detector(gray)
# crop the image (x) to keep only the face, resize the cropped image to 150x150
for face in faces:
x, y, w, h = face.left(), face.top(), face.width(), face.height()
cropped_face = cv2.resize(x[y:y+h, x:x+w], (150, 150))
# convert the cropped image to grayscale
gray_cropped_face = cv2.cvtColor(cropped_face, cv2.COLOR_BGR2GRAY)
# append the converted image into temp_X_processed
temp_X_processed.append(gray_cropped_face)
# append temp_X_processed into X_processed
X_processed.append(temp_X_processed)
# save X_processed into a folder
os.makedirs("processed_images", exist_ok=True)
for i, x_list in enumerate(X_processed):
for j, x in enumerate(x_list):
filename = f"processed_images/image_{i}_{j}.jpg"
cv2.imwrite(filename, x)
```
基于以下代码import sys import dlib import cv2 predictor_path="shape_predictor_194_face_landmarks.dat" detector = dlib.get_frontal_face_detector() predictor = dlib.shape_predictor(predictor_path) cam = cv2.VideoCapture(0) cam.set(3, 1280) cam.set(4, 720) color_white = (255, 255, 255) line_width = 3 while True: ret_val, img = cam.read() rgb_image = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) dets = detector(rgb_image) for det in dets: cv2.rectangle(img, (det.left(), det.top()), (det.right(), det.bottom()), color_white, line_width) shape = predictor(img, det) for p in shape.parts(): cv2.circle(img, (p.x, p.y), 2, (0, 255, 0), -1) cv2.imshow('my webcam', img) if cv2.waitKey(1) == 27: break cv2.destroyAllWindows() 加入dilb和OpenCV的目标跟踪算法,于持续视频播放中,每秒中的第一帧图像执行dilb人脸特征点检测,后续图像跟踪这些特征点,并使用dlib.correlation_tracker()函数、cv2.calcOpticalFlowFarneback()函数,争取实现194个人脸特征点的跟踪
抱歉,这段代码并没有加入目标跟踪算法。如果要加入目标跟踪算法,可以使用dlib.correlation_tracker()函数进行跟踪。这个函数可以帮助我们跟踪在第一帧中检测到的人脸特征点。
具体步骤如下:
在每秒中的第一帧图像中,使用dlib.get_frontal_face_detector()函数进行人脸检测,然后使用dlib.shape_predictor()函数检测人脸特征点。将这些特征点存储在一个列表中,并将列表传递给dlib.correlation_tracker()函数,以便跟踪这些特征点。
对于后续的每一帧图像,我们可以使用cv2.calcOpticalFlowFarneback()函数计算特征点的运动,然后使用dlib.correlation_tracker()函数进行跟踪。
具体实现可以参考以下代码:
```
import sys
import dlib
import cv2
predictor_path = "shape_predictor_194_face_landmarks.dat"
detector = dlib.get_frontal_face_detector()
predictor = dlib.shape_predictor(predictor_path)
cam = cv2.VideoCapture(0)
cam.set(3, 1280)
cam.set(4, 720)
color_white = (255, 255, 255)
line_width = 3
# 初始化跟踪器
tracker = None
tracking_pts = []
while True:
ret_val, img = cam.read()
rgb_image = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
dets = detector(rgb_image)
if len(dets) > 0:
# 检测到人脸,获取特征点并初始化跟踪器
det = dets[0]
shape = predictor(rgb_image, det)
tracking_pts = [(p.x, p.y) for p in shape.parts()]
tracker = dlib.correlation_tracker()
rect = dlib.rectangle(det.left(), det.top(), det.right(), det.bottom())
tracker.start_track(rgb_image, rect)
elif tracker is not None:
# 没有检测到人脸,继续跟踪特征点
tracker.update(rgb_image)
pos = tracker.get_position()
rect = dlib.rectangle(int(pos.left()), int(pos.top()), int(pos.right()), int(pos.bottom()))
shape = predictor(rgb_image, rect)
tracking_pts = [(p.x, p.y) for p in shape.parts()]
for pt in tracking_pts:
cv2.circle(img, pt, 2, (0, 255, 0), -1)
cv2.imshow('my webcam', img)
if cv2.waitKey(1) == 27:
break
cv2.destroyAllWindows()
```
这段代码使用dlib.correlation_tracker()函数对人脸特征点进行跟踪,如果没有检测到人脸,则继续跟踪前一帧中检测到的特征点。