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Author: OfficialBishal

faces-train.py

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+### By Bishal Shrestha
+
+import cv2
+import os
+import numpy as np
+from PIL import Image
+import pickle
+
+BASE_DIR = os.path.dirname(os.path.abspath(__file__))
+image_dir = os.path.join(BASE_DIR, "images")
+
+face_cascade = cv2.CascadeClassifier('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"):
+			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)
+			#y_labels.append(label) # some number
+			#x_train.append(path) # verify this image, turn into a NUMPY arrray, GRAY
+			pil_image = Image.open(path).convert("L") # grayscale
+			size = (550, 550)
+			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("pickles/face-labels.pickle", 'wb') as f:
+	pickle.dump(label_ids, f)
+
+recognizer.train(x_train, np.array(y_labels))
+recognizer.save("recognizers/face-trainner.yml")