opencv-face | 基于python-opencv调用摄像头的实时人脸检测和识别 | Blog library
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kandi X-RAY | opencv-face Summary
基于python-opencv调用摄像头的实时人脸检测和识别
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QUESTION
I have a project where I need to include face recognition
in it. I am referring to this article. This article is using open-face
to get the face embeddings
and its saving all the embeddings in a pickle file. Then its passing the face embeddings data to support vector machine
which generates another pickle file. This file is later used to recognize and predict the face.
This has been working and is giving me more than 80% accuracy. But this article has not explained on how to calculate euclidean distance
. This I needed for my own research work.
I can easily calculate euclidean distance
between the face embedding of test image and face embeddings present in pickle file but I am not able to understand how to set the threshold value so that any distance more than that will be tagged as unknown
.
Can anyone please point me to some article where this has been explained and I can follow up from there. I have tried searching many articles but didnt get much results on this. Please help. Thanks
...ANSWER
Answered 2020-Apr-15 at 15:45You can build 2 ( normal ) distributions.
- distances between same person's faces
- distances between different faces
Intersection of these distributuins will be the threshold.
QUESTION
Can Anyone Help Me to solve this i had error sqlite3.OperationalError: near "<": syntax error i think that from the sql but still stucking there
...ANSWER
Answered 2020-Mar-24 at 10:05I cannot replicate your error in my testing.
While I was testing your code, I notice a problem with
QUESTION
from sklearn.preprocessing import LabelEncoder
from sklearn.svm import SVC
import argparse
import pickle
# construct the argument parser and parse the arguments
ap = argparse.ArgumentParser()
ap.add_argument("-e", "--embeddings",default=r"C:\Users\osama\Desktop\opencv-face-recognition\face_detection_model\output",
help="path to serialized db of facial embeddings")
ap.add_argument("-r", "--recognizer", default=r"C:\Users\osama\Desktop\opencv-face-recognition\face_detection_model\output",
help="path to output model trained to recognize faces")
ap.add_argument("-l", "--le", default=r"C:\Users\osama\Desktop\opencv-face-recognition\face_detection_model\output",
help="path to output label encoder")
args = vars(ap.parse_args())
# load the face embeddings
print("[INFO] loading face embeddings...")
data = pickle.loads(open(args["embeddings"], "rb").read())
# encode the labels
print("[INFO] encoding labels...")
le = LabelEncoder()
labels = le.fit_transform(data["names"])
# train the model used to accept the 128-d embeddings of the face and
# then produce the actual face recognition
print("[INFO] training model...")
recognizer = SVC(C=1.0, kernel="linear", probability=True)
recognizer.fit(data["embeddings"], labels)
# write the actual face recognition model to disk
f = open(args["recognizer"], "wb")
f.write(pickle.dumps(recognizer))
f.close()
#write the label encoder to disk
f = open(args["le"], "wb")
f.write(pickle.dumps(le))
f.close()
...ANSWER
Answered 2020-Jan-04 at 17:47You are unpickling an object here:
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You can use opencv-face like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.
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