predict.py

[Gaebobman]

import cv2
import face_recognition
import math
import numpy as np
import os
import pickle
import sys
import time
from os import makedirs, listdir
from os.path import isdir, isfile, join
from sklearn import neighbors
from face_recognition import face_recognition_cli
from face_recognition.face_recognition_cli import image_files_in_folder

# https://blog.naver.com/chandong83/221695462391
# function for consecutive capture
def detect_face(frame, classifier):
    grayscale_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
    faces = classifier.detectMultiScale(grayscale_frame, 1.3, 3)   # image, image_scale, max_faces
    if len(faces) == 0:
        return None
    for(x,y,w,h) in faces:
        margin = 0.1 # 배경 비율
        x = int (x - w * margin) # x 좌표를 왼쪽으로 이동
        y = int (y - h * margin) # y 좌표를 위로 이동
        w = int (w * (1 + margin * 2)) # 너비를 늘림
        h = int (h * (1 + margin * 2)) # 높이를 늘림
        cropped_face = frame[y:y+h, x:x+w]

    return cropped_face

# https://blog.naver.com/chandong83/221695462391
def capture_face(user_name):
    classifier = cv2.CascadeClassifier("data/haarcascade_frontalface_default.xml")
    if not isdir(f"data/sub_data/{user_name}"):
        makedirs(f"data/sub_data/{user_name}")
    capture= cv2.VideoCapture('nvarguscamerasrc ! video/x-raw(memory:NVMM), width=640, height=480, format=(string)NV12, framerate=(fraction)20/1 ! nvvidconv ! video/x-raw, format=(string)BGRx ! videoconvert ! video/x-raw, format=(string)BGR ! appsink' , cv2.CAP_GSTREAMER)
    count = 0

    while True:
        result, frame = capture.read()
        if detect_face(frame, classifier) is not None:
            count+=1
            face = cv2.resize(detect_face(frame, classifier),(200,200))
            # face = cv2.cvtColor(face, cv2.COLOR_BGR2GRAY)
            cv2.imwrite(f"data/sub_data/{user_name}/{user_name}-{count}.png",face)
            cv2.putText(face,str(count),(50,50),cv2.FONT_HERSHEY_COMPLEX,1,(255,0,0),2)
            # cv2.imshow('Face Cropper',face)
        else:
            print("Face not Found")
            pass
        # Press Enter or get 50images to break
        if cv2.waitKey(1)==13 or count==50:
            break
        time.sleep(0.1)

    capture.release()
    cv2.destroyAllWindows()

    print('Colleting Samples Complete!!!')

# https://github.com/ageitgey/face_recognition/blob/master/examples/face_recognition_knn.py

def train(train_dir, model_save_path=None, n_neighbors=None, knn_algo='ball_tree', verbose=False):
    """
    Trains a k-nearest neighbors classifier for face recognition.

    :param train_dir: directory that contains a sub-directory for each known person, with its name.

     (View in source code to see train_dir example tree structure)

     Structure:
        <train_dir>/
        ├── <person1>/
        │   ├── <somename1>.jpeg
        │   ├── <somename2>.jpeg
        │   ├── ...
        ├── <person2>/
        │   ├── <somename1>.jpeg
        │   └── <somename2>.jpeg
        └── ...

    :param model_save_path: (optional) path to save model on disk
    :param n_neighbors: (optional) number of neighbors to weigh in classification. Chosen automatically if not specified
    :param knn_algo: (optional) underlying data structure to support knn.default is ball_tree
    :param verbose: verbosity of training
    :return: returns knn classifier that was trained on the given data.
    """
    X = []
    y = []

    # Loop through each person in the training set
    for class_dir in os.listdir(train_dir):
        if not os.path.isdir(os.path.join(train_dir, class_dir)):
            continue

        # Loop through each training image for the current person
        for img_path in image_files_in_folder(os.path.join(train_dir, class_dir)):
            image = face_recognition.load_image_file(img_path)
            face_bounding_boxes = face_recognition.face_locations(image)

            if len(face_bounding_boxes) != 1:
                # If there are no people (or too many people) in a training image, skip the image.
                if verbose:
                    print("Image {} not suitable for training: {}".format(img_path, "Didn't find a face" if len(face_bounding_boxes) < 1 else "Found more than one face"))
            else:
                # Add face encoding for current image to the training set
                X.append(face_recognition.face_encodings(image, known_face_locations=face_bounding_boxes)[0])
                y.append(class_dir)

    # Determine how many neighbors to use for weighting in the KNN classifier
    if n_neighbors is None:
        n_neighbors = int(round(math.sqrt(len(X))))
        if verbose:
            print("Chose n_neighbors automatically:", n_neighbors)

    # Create and train the KNN classifier
    knn_clf = neighbors.KNeighborsClassifier(n_neighbors=n_neighbors, algorithm=knn_algo, weights='distance')
    knn_clf.fit(X, y)

    # Save the trained KNN classifier
    if model_save_path is not None:
        with open(model_save_path, 'wb') as f:
            pickle.dump(knn_clf, f)

    return knn_clf

def main():

    if len(sys.argv) != 2:
        print(f"Insufficient arguments\n\n Usage: python3 {sys.argv[1]} \"user_name\"")
        sys.exit()
    else:
        user_name = sys.argv[1]
    print(f'Please stay still in front of the camera : {user_name}')
    capture_face(user_name)
    print("Captured 50 iamges, Now Train KNN classifier... ")
    # TODO: Use Only 80% of image for train, and user 20% for measure accuracy
    classifier = train("data/sub_data", model_save_path="data/trained_knn_model.clf", n_neighbors=2)
    print("Training complete!")
    # Print User List
    user_list_path = 'data/sub_data'
    user_list = [item for item in os.listdir (user_list_path) if os.path.isdir (os.path.join (user_list_path, item))]
    print(user_list)

if __name__ == "__main__":
    main()

[Gaebobman]

Traceback (most recent call last): File "detect_user.py", line 37, in predict X_img = face_recognition.load_image_file(X_img_path) File "/usr/local/lib/python3.6/dist-packages/face_recognition/api.py", line 86, in load_image_file im = PIL.Image.open(file) File "/usr/local/lib/python3.6/dist-packages/PIL/Image.py", line 3031, in open "cannot identify image file %r" % (filename if filename else fp) PIL.UnidentifiedImageError: cannot identify image file 'data/test_data/visitor.png'

During handling of the above exception, another exception occurred:

Traceback (most recent call last): File "detect_user.py", line 138, in main() File "detect_user.py", line 126, in main predictions = predict("data/test_data/visitor.png", model_path="data/trained_knn_model.clf") File "detect_user.py", line 38, in predict except PIL.UnidentifiedImageError: NameError: name 'PIL' is not defined

[Gaebobman]

아마 사진이 저장 완료 전에 Predict 를 하여 유발되는 예외로 추정됨, Timeout 을걸거나 추론 시도를 하지 않도록 수정해야 하겠음.

[Gaebobman]

다음 Repository 에서 구현됨

https://github.com/Gaebobman/CarbonCheck_homeserver