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cheap fix for trainer.yaml #16

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Merged
devqueue merged 1 commit into from
Dec 12, 2020
Merged

cheap fix for trainer.yaml #16

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2 changes: 1 addition & 1 deletion skyhawk/commands/cmd_train.py
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Original file line number Diff line number Diff line change
Expand Up @@ -5,6 +5,6 @@
@click.command()
def cli():
'''Train skyhawk classifier on captured data'''
result = trainer.Facetrainer()
result = trainer.Facerunnner()
return result

58 changes: 58 additions & 0 deletions skyhawk/services/initialize.py
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Original file line number Diff line number Diff line change
Expand Up @@ -13,6 +13,64 @@ def run():
with open('skyhawk/bin/Attendance.csv', 'w') as file:
HEADER = "Name, Date, Time, Day "
file.write(HEADER)
with open('skyhawk/bin/trainer.py','w') as py:
code = '''
import cv2
import os
import numpy as np
from PIL import Image
import pickle

def Facetrainer():
BASE_DIR = os.path.dirname(os.path.abspath(__file__))
direc = "../facedata"
image_dir = os.path.join(BASE_DIR, direc)
face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades+'haarcascade_frontalface_default.xml')
recognizer = cv2.face.LBPHFaceRecognizer_create()
current_id = 0
label_ids = {}
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(root).replace(" ", "-").lower()
#print(label, 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") # grayscale
size = (200, 200)
final_image = pil_image.resize(size, Image.ANTIALIAS)
image_array = np.array(final_image, "uint8")
#print(image_array)
faces = face_cascade.detectMultiScale(image_array, scaleFactor=1.5, 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_)

#print(y_labels)
#print(x_train)

with open("skyhawk/bin/face-labels.pickle", 'wb') as f:
pickle.dump(label_ids, f)

recognizer.train(x_train, np.array(y_labels))
recognizer.save("skyhawk/bin/face-trainner.yml")
print("Training completed")


if __name__ == "__main__":
Facetrainer()
'''
py.write(code)

header = "[[green]] Welcome to skyhawk! Auto-attandance cli tool "
footer = "[[white]]Data directories initialized sucessfully"
Logo = '''[[cyan]]
Expand Down
4 changes: 3 additions & 1 deletion skyhawk/services/trainer.py
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Original file line number Diff line number Diff line change
Expand Up @@ -9,7 +9,7 @@
def Facetrainer():
'''Train skyhawk classifier on captured data'''
BASE_DIR = os.path.dirname(os.path.abspath(__file__))
direc = "skyhawk/facedata"
direc = "../facedata"
image_dir = os.path.join(BASE_DIR, direc)
face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades+'haarcascade_frontalface_default.xml')
recognizer = cv2.face.LBPHFaceRecognizer_create()
Expand Down Expand Up @@ -51,6 +51,8 @@ def Facetrainer():
recognizer.save("skyhawk/bin/face-trainner.yml")
print("Training completed")

def Facerunnner():
os.system('python "skyhawk/bin/trainer.py"')

if __name__ == "__main__":
Facetrainer()