120 lines
3.4 KiB
Python
120 lines
3.4 KiB
Python
import cv2
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import numpy as np
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import os
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import uz_framework.image as uz
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IMAGES_FOLDER = './datam/me/'
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def read_images(path):
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# Get all the path to the images and save them in a list
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image_paths = [os.path.join(path, f) for f in os.listdir(path)]
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images = []
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for image_path in image_paths:
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# Read the image and convert to grayscale
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image_pil = cv2.imread(image_path, 0)
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# Convert the image format into numpy array
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image = np.array(image_pil, 'uint8')
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images.append(image)
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face_cascade = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')
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faces = []
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for image in images:
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# Detect face in the image
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face = face_cascade.detectMultiScale(image, 1.3, 5)
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for (x, y, w, h) in face:
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# Crop the face out
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H = 300
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W = 280
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y_offset = 20
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x_offset = 30
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face = image[y-y_offset:(y-y_offset)+H, x-x_offset:(x-x_offset)+W].copy()
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faces.append(face)
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return np.array(faces)
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def train(faces):
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# Construct PCA of the faces
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# Reshape all the images into a single matrix of size (n, m)
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# Downsample the images
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fcc = np.array([])
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for face in faces:
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face = cv2.pyrDown(face)
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face = face.reshape((face.shape[0] * face.shape[1]), 1)
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if fcc.size == 0:
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fcc = face
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else:
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fcc = np.hstack((fcc, face))
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print(fcc.shape)
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U, _, _, mean = uz.dual_PCA(fcc.T)
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return U, mean
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def recognize(U, mean):
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# Open webcam stream
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cam = cv2.VideoCapture(0)
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face_cascade = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')
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while True:
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# Read the frame
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_, frame = cam.read()
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# Convert to grayscale
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gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
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# Detect faces
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face = face_cascade.detectMultiScale(gray, 1.3, 5)
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for (x, y, w, h) in face:
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# Crop the face
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H = 300
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W = 280
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y_offset = 20
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x_offset = 30
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face = gray[y-y_offset:(y-y_offset)+H, x-x_offset:(x-x_offset)+W].copy()
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if face.shape[0] != H or face.shape[1] != W:
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continue
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# Project the face into the PCA subspace
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# Project images into PCA subspace
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# Downsample the image
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face = cv2.pyrDown(face)
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face = face.reshape(-1) # Reshape it into a vector
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y_i = np.matmul(face - mean, U)
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# Project images back into original subspace
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x_i = np.matmul(y_i, U.T) + mean
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# Compute the L2 norm between the original image and the reconstructed image
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norm = np.linalg.norm(face - x_i)
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# If the norm is less than 1000, then the face is recognized
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if norm < 8000:
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cv2.putText(frame, 'Recognized Gasper', (x, y), cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 255, 0), 2)
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# Plot the rectangle around the face
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cv2.rectangle(frame, (x, y), (x+w, y+h), (255, 0, 0), 2)
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# Display the frame
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cv2.imshow('frame', frame)
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# Exit if 'q' is pressed
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if cv2.waitKey(1) & 0xFF == ord('q'):
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break
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def main():
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faces = read_images(IMAGES_FOLDER)
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U, mean = train(faces)
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recognize(U, mean)
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if __name__ == '__main__':
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main()
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