Jetson Nano 【15】安装Dlib以及一个demo

本文主要是介绍Jetson Nano 【15】安装Dlib以及一个demo，希望对大家解决编程问题提供一定的参考价值，需要的开发者们随着小编来一起学习吧！

文章目录

- - - Dlib介绍
    - Nano 安装过程
    - - Nano基本环境安装
      - 下载压缩包、解压
      - 修改源码
      - 安装
      - 安装face_recognition 库
    - Demo
    - - 代码

Dlib的Github入口
Nano的安装文档

Dlib介绍

关于Dlib的介绍可以看：这位老哥的转载
我有时间会认真学习，整理他知识点（坑位预定）

Nano 安装过程

安装过程参考上面Nano安装文档，本文基本就是上面安装文档截取、翻译与解释。当然，安装过程是我亲自经历的。
Nano基本环境安装
- 这里默认系统什么都装好了，基本环境也是。

sudo apt-get update
sudo apt-get install python3-pip cmake libopenblas-dev liblapack-dev libjpeg-dev

installSwapfile安装，这里由于本人之前玩过目标识别，所以装过交换空间这个，就不装了。
下载压缩包、解压

wget http://dlib.net/files/dlib-19.17.tar.bz2 
tar jxvf dlib-19.17.tar.bz2 
cd dlib-19.17

这里官方下载用的是wget，本人使用的是浏览器直接下载（这个是个静态文件，链接就可以直接下载），发现开个科#学#商务办公会快点，当然本身也不大。
修改源码
- 这里是关键点，关于这个，Github上的ReadME也提到了，是Nano上cudnn的问题，只需注释854行的代码即可正常编译，关于编译问题本人遇到很多次了，贴心指出来还是很好评的，毕竟可以省下很多时间。

//不用gedit，其他编辑器也是可以的，本人用的是vim，找到对应文件即可
gedit dlib/cuda/cudnn_dlibapi.cpp
//注释掉这行
forward_algo = forward_best_algo;

安装
- 退到第一层（有setup.py那一页），开始安装
- sudo python3 setup.py install
- 这里文档里有个有意思的东西：This will take around 30–60 minutes to finish and your Jetson Nano might get hot, but just let it run.实测是10分钟,而且发热量不大，可以接受，安装过程及其顺利。
安装face_recognition 库
- 文档提示需要再安装face_recognition 库，装完就可以使用了。
- sudo pip3 install face_recognition去不去sudo没测试过，本人加上了。

Demo

先来张效果图，用的树莓派的摄像头，光线问题拍的比较黑，但是能识别~
代码
- 这个源码网址是：传送
- 官方是wget下的wget -O doorcam.py tiny.cc/doorcam,不过似乎有神秘的力量阻挡了链接。
- 接下来是源码,连注释都没改，放心食用

import face_recognition
import cv2
from datetime import datetime, timedelta
import numpy as np
import platform
import pickle# Our list of known face encodings and a matching list of metadata about each face.
known_face_encodings = []
known_face_metadata = []def save_known_faces():with open("known_faces.dat", "wb") as face_data_file:face_data = [known_face_encodings, known_face_metadata]pickle.dump(face_data, face_data_file)print("Known faces backed up to disk.")def load_known_faces():global known_face_encodings, known_face_metadatatry:with open("known_faces.dat", "rb") as face_data_file:known_face_encodings, known_face_metadata = pickle.load(face_data_file)print("Known faces loaded from disk.")except FileNotFoundError as e:print("No previous face data found - starting with a blank known face list.")passdef running_on_jetson_nano():# To make the same code work on a laptop or on a Jetson Nano, we'll detect when we are running on the Nano# so that we can access the camera correctly in that case.# On a normal Intel laptop, platform.machine() will be "x86_64" instead of "aarch64"return platform.machine() == "aarch64"def get_jetson_gstreamer_source(capture_width=1280, capture_height=720, display_width=1280, display_height=720, framerate=60, flip_method=0):"""Return an OpenCV-compatible video source description that uses gstreamer to capture video from the camera on a Jetson Nano"""return (f'nvarguscamerasrc ! video/x-raw(memory:NVMM), ' +f'width=(int){capture_width}, height=(int){capture_height}, ' +f'format=(string)NV12, framerate=(fraction){framerate}/1 ! ' +f'nvvidconv flip-method={flip_method} ! ' +f'video/x-raw, width=(int){display_width}, height=(int){display_height}, format=(string)BGRx ! ' +'videoconvert ! video/x-raw, format=(string)BGR ! appsink')def register_new_face(face_encoding, face_image):"""Add a new person to our list of known faces"""# Add the face encoding to the list of known facesknown_face_encodings.append(face_encoding)# Add a matching dictionary entry to our metadata list.# We can use this to keep track of how many times a person has visited, when we last saw them, etc.known_face_metadata.append({"first_seen": datetime.now(),"first_seen_this_interaction": datetime.now(),"last_seen": datetime.now(),"seen_count": 1,"seen_frames": 1,"face_image": face_image,})def lookup_known_face(face_encoding):"""See if this is a face we already have in our face list"""metadata = None# If our known face list is empty, just return nothing since we can't possibly have seen this face.if len(known_face_encodings) == 0:return metadata# Calculate the face distance between the unknown face and every face on in our known face list# This will return a floating point number between 0.0 and 1.0 for each known face. The smaller the number,# the more similar that face was to the unknown face.face_distances = face_recognition.face_distance(known_face_encodings, face_encoding)# Get the known face that had the lowest distance (i.e. most similar) from the unknown face.best_match_index = np.argmin(face_distances)# If the face with the lowest distance had a distance under 0.6, we consider it a face match.# 0.6 comes from how the face recognition model was trained. It was trained to make sure pictures# of the same person always were less than 0.6 away from each other.# Here, we are loosening the threshold a little bit to 0.65 because it is unlikely that two very similar# people will come up to the door at the same time.if face_distances[best_match_index] < 0.65:# If we have a match, look up the metadata we've saved for it (like the first time we saw it, etc)metadata = known_face_metadata[best_match_index]# Update the metadata for the face so we can keep track of how recently we have seen this face.metadata["last_seen"] = datetime.now()metadata["seen_frames"] += 1# We'll also keep a total "seen count" that tracks how many times this person has come to the door.# But we can say that if we have seen this person within the last 5 minutes, it is still the same# visit, not a new visit. But if they go away for awhile and come back, that is a new visit.if datetime.now() - metadata["first_seen_this_interaction"] > timedelta(minutes=5):metadata["first_seen_this_interaction"] = datetime.now()metadata["seen_count"] += 1return metadatadef main_loop():# Get access to the webcam. The method is different depending on if this is running on a laptop or a Jetson Nano.if running_on_jetson_nano():# Accessing the camera with OpenCV on a Jetson Nano requires gstreamer with a custom gstreamer source stringvideo_capture = cv2.VideoCapture(get_jetson_gstreamer_source(), cv2.CAP_GSTREAMER)else:# Accessing the camera with OpenCV on a laptop just requires passing in the number of the webcam (usually 0)# Note: You can pass in a filename instead if you want to process a video file instead of a live camera streamvideo_capture = cv2.VideoCapture(0)# Track how long since we last saved a copy of our known faces to disk as a backup.number_of_faces_since_save = 0while True:# Grab a single frame of videoret, frame = video_capture.read()# Resize frame of video to 1/4 size for faster face recognition processingsmall_frame = cv2.resize(frame, (0, 0), fx=0.25, fy=0.25)# Convert the image from BGR color (which OpenCV uses) to RGB color (which face_recognition uses)rgb_small_frame = small_frame[:, :, ::-1]# Find all the face locations and face encodings in the current frame of videoface_locations = face_recognition.face_locations(rgb_small_frame)face_encodings = face_recognition.face_encodings(rgb_small_frame, face_locations)# Loop through each detected face and see if it is one we have seen before# If so, we'll give it a label that we'll draw on top of the video.face_labels = []for face_location, face_encoding in zip(face_locations, face_encodings):# See if this face is in our list of known faces.metadata = lookup_known_face(face_encoding)# If we found the face, label the face with some useful information.if metadata is not None:time_at_door = datetime.now() - metadata['first_seen_this_interaction']face_label = f"At door {int(time_at_door.total_seconds())}s"# If this is a brand new face, add it to our list of known faceselse:face_label = "New visitor!"# Grab the image of the the face from the current frame of videotop, right, bottom, left = face_locationface_image = small_frame[top:bottom, left:right]face_image = cv2.resize(face_image, (150, 150))# Add the new face to our known face dataregister_new_face(face_encoding, face_image)face_labels.append(face_label)# Draw a box around each face and label each facefor (top, right, bottom, left), face_label in zip(face_locations, face_labels):# Scale back up face locations since the frame we detected in was scaled to 1/4 sizetop *= 4right *= 4bottom *= 4left *= 4# Draw a box around the facecv2.rectangle(frame, (left, top), (right, bottom), (0, 0, 255), 2)# Draw a label with a name below the facecv2.rectangle(frame, (left, bottom - 35), (right, bottom), (0, 0, 255), cv2.FILLED)cv2.putText(frame, face_label, (left + 6, bottom - 6), cv2.FONT_HERSHEY_DUPLEX, 0.8, (255, 255, 255), 1)# Display recent visitor imagesnumber_of_recent_visitors = 0for metadata in known_face_metadata:# If we have seen this person in the last minute, draw their imageif datetime.now() - metadata["last_seen"] < timedelta(seconds=10) and metadata["seen_frames"] > 5:# Draw the known face imagex_position = number_of_recent_visitors * 150frame[30:180, x_position:x_position + 150] = metadata["face_image"]number_of_recent_visitors += 1# Label the image with how many times they have visitedvisits = metadata['seen_count']visit_label = f"{visits} visits"if visits == 1:visit_label = "First visit"cv2.putText(frame, visit_label, (x_position + 10, 170), cv2.FONT_HERSHEY_DUPLEX, 0.6, (255, 255, 255), 1)if number_of_recent_visitors > 0:cv2.putText(frame, "Visitors at Door", (5, 18), cv2.FONT_HERSHEY_DUPLEX, 0.8, (255, 255, 255), 1)# Display the final frame of video with boxes drawn around each detected famescv2.imshow('Video', frame)# Hit 'q' on the keyboard to quit!if cv2.waitKey(1) & 0xFF == ord('q'):save_known_faces()break# We need to save our known faces back to disk every so often in case something crashes.if len(face_locations) > 0 and number_of_faces_since_save > 100:save_known_faces()number_of_faces_since_save = 0else:number_of_faces_since_save += 1# Release handle to the webcamvideo_capture.release()cv2.destroyAllWindows()if __name__ == "__main__":load_known_faces()main_loop()