58 lines
1.6 KiB
Python
58 lines
1.6 KiB
Python
import cv2
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import os
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import numpy as np
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from PIL import Image
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import pickle
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BASE_DIR = os.path.dirname(os.path.abspath(__file__))
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image_dir = os.path.join(BASE_DIR, "faces")
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face_cascade = cv2.CascadeClassifier(
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'cascades/data/haarcascade_frontalface_alt2.xml')
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recognizer = cv2.face.LBPHFaceRecognizer_create()
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current_id = 0
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label_ids = {}
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y_labels = []
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x_train = []
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for root, dirs, files in os.walk(image_dir):
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# print(root)
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# print(dirs)
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# print(files)
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for file in files:
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if file.endswith("png") or file.endswith("jpg"):
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path = os.path.join(root, file)
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label = os.path.basename(root).replace(" ", "-").lower()
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#print(label, path)
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if not label in label_ids:
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label_ids[label] = current_id
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# print(label_ids)
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current_id += 1
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id_ = label_ids[label]
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# print(label_ids)
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pil_image = Image.open(path).convert("L") # gray scale
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size = (550, 550)
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final_image = pil_image.resize(size, Image.ANTIALIAS)
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image_array = np.array(final_image, "uint8")
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# print(image_array)
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faces = face_cascade.detectMultiScale(
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image_array, scaleFactor=1.5, minNeighbors=5)
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for (x, y, w, h) in faces:
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roi = image_array[y:y + h, x:x + w]
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x_train.append(roi)
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y_labels.append(id_)
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# print(y_labels)
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# print(x_train)
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with open("labels.pickle", "wb") as f:
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pickle.dump(label_ids, f)
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recognizer.train(x_train, np.array(y_labels))
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recognizer.save("trainner.yml")
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print("done")
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