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faces_train.py
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faces_train.py
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import numpy as np
import os
from PIL import Image
import cv2
import pickle
def trainit():
BASE_DIR = os.path.dirname(os.path.abspath(__file__))
image_dir = os.path.join(BASE_DIR, "images")
face_cascade = cv2.CascadeClassifier('C:\\Users\\Admin\\buttonpython\\buttonpython\\cascades\\data\\haarcascade_frontalface_alt2.xml')
recognizer = cv2.face.LBPHFaceRecognizer_create()
current_id = 0
label_ids = {} #dict to store vals
y_labels = []
x_train = []
for root, dirs, files in os.walk(image_dir):
for file in files:
if file.endswith("png") or file.endswith("jpg") or file.endswith("jpeg"):
path = os.path.join(root, file)
label = os.path.basename(os.path.dirname(path)).replace(" ", "-").lower()
#print(label,path)
#y_labels.append(label)
#x_train.append(path)
if not label in label_ids:
label_ids[label] = current_id
current_id += 1
id_ = label_ids[label]
print(label_ids)
pil_image = Image.open(path).convert("L")#greyscale conversion
image_array = np.array(pil_image, "uint8")
print(image_array)
faces = face_cascade.detectMultiScale(image_array, scaleFactor = 1.57, minNeighbors = 5)
for(x,y,w,h) in faces:
roi = image_array[y:y+h, x:x+w]
x_train.append(roi)
y_labels.append(id_)
with open("C:\\Users\\Admin\\buttonpython\\buttonpython\\labels.pickle", 'wb') as f:
pickle.dump(label_ids, f)
recognizer.train(x_train, np.array(y_labels))
recognizer.save("trainner.yml")
trainit()