pre-proccessing.py

from imutils import paths
import numpy as np
import imutils
import cv2
import pickle
import os

#Pathsto models and dataset
dataset = 'Attendence-System-Face-Recognition/datasets'
embeddingFile = 'Attendence-System-Face-Recognition/output/embeddings.pickle'
embeddingModel = 'Attendence-System-Face-Recognition/openface.nn4.small2.v1.t7'
prototxt = 'Attendence-System-Face-Recognition/model/deploy.prototxt.txt'
model = 'Attendence-System-Face-Recognition/model/res10_300x300_ssd_iter_140000.caffemodel'

#Load models
print("[INFO] Loading face detector...")
detector = cv2.dnn.readNetFromCaffe(prototxt, model)
print("[INFO] Loading face embedding model...")
embedder = cv2.dnn.readNetFromTorch(embeddingModel)

imagePaths = list(paths.list_images(dataset))

knownEmbeddings = []
knownNames = []
total = 0
conf = 0.3  

for (i, imagePath) in enumerate(imagePaths):
    print(f"[INFO] Processing image {i+1}/{len(imagePaths)}: {imagePath}")
    name = imagePath.split(os.path.sep)[-2] 

    image = cv2.imread(imagePath)
    image = imutils.resize(image, width=600)
    (h, w) = image.shape[:2]

    imageBlob = cv2.dnn.blobFromImage(
        cv2.resize(image, (300, 300)), 1.0, (300, 300), (104.0, 177.0, 123.0), swapRB=False, crop=False
    )
    
    detector.setInput(imageBlob)
    detections = detector.forward()

    if len(detections) > 0:
        i = np.argmax(detections[0, 0, :, 2])
        confidence = detections[0, 0, i, 2]

        if confidence > conf:
            box = detections[0, 0, i, 3:7] * np.array([w, h, w, h])
            (startX, startY, endX, endY) = box.astype("int")

            startX = max(0, startX)
            startY = max(0, startY)
            endX = min(w, endX)
            endY = min(h, endY)

            face = image[startY:endY, startX:endX]
            (fH, fW) = face.shape[:2]

            if fW < 20 or fH < 20:
                
                continue

            faceBlob = cv2.dnn.blobFromImage(face, 1.0 / 255, (96, 96), (0, 0, 0), swapRB=True, crop=False)
            embedder.setInput(faceBlob)
            vec = embedder.forward()

            knownNames.append(name)
            knownEmbeddings.append(vec.flatten())
            total += 1


            cv2.rectangle(image, (startX, startY), (endX, endY), (0, 255, 0), 2)
            cv2.imshow("Face", face)
            cv2.imshow("Detection", image)
            cv2.waitKey(1)  

print(f"[INFO] Total embeddings created: {total}")

print("[INFO] Saving embeddings...")
data = {"embeddings": knownEmbeddings, "names": knownNames}
with open(embeddingFile, "wb") as f:
    f.write(pickle.dumps(data))

print("[INFO] Process Completed.")