showpoints.py

def showpoints(image,keypoints):
    
    plt.figure()
    
    keypoints = keypoints.data.numpy()
    keypoints = keypoints * 60.0 + 68
    keypoints = np.reshape(keypoints, (68, -1))
    
    plt.imshow(image, cmap='gray')
    plt.scatter(keypoints[:, 0], keypoints[:, 1], s=50, marker='.', c='r')
    


from torch.autograd import Variable
image_copy = np.copy(image)


# loop over the detected faces from your haar cascade
for (x,y,w,h) in faces:
    
    # Select the region of interest that is the face in the image 
    roi = image_copy[y:y+h,x:x+w]

    ## TODO: Convert the face region from RGB to grayscale
    roi = cv2.cvtColor(roi, cv2.COLOR_RGB2GRAY)
    image = roi

    ## TODO: Normalize the grayscale image so that its color range falls in [0,1] instead of [0,255]
    roi = roi/255.0
    
    ## TODO: Rescale the detected face to be the expected square size for your CNN (224x224, suggested)
    roi = cv2.resize(roi, (224,224))
    
    ## TODO: Reshape the numpy image shape (H x W x C) into a torch image shape (C x H x W)
    roi = np.expand_dims(roi, 0)
    roi = np.expand_dims(roi, 0)
    
    ## TODO: Make facial keypoint predictions using your loaded, trained network 
    roi_torch = Variable(torch.from_numpy(roi))
    
    roi_torch = roi_torch.type(torch.FloatTensor)
    keypoints = net(roi_torch)

    ## TODO: Display each detected face and the corresponding keypoints        
    showpoints(image,keypoints)