Test of Eyes CNN Locator¶
Introduction¶
This is a test of the fastai CNN model (as shown in an earlier blog post). We load up the model, and the test the model of a completely different database of face image than that used to train the model. I am most interested in the limitations or failure modes of the model we have trained.
The test database is the Yale Face Database.
Implementation¶
Magics¶
%reload_ext autoreload
%autoreload 2
%matplotlib inline
Imports¶
from fastai.vision import *
Access Google Drive Face Database¶
The databases are stored on Google Drive.
from google.colab import drive
drive.mount('/content/gdrive')
Drive already mounted at /content/gdrive; to attempt to forcibly remount, call drive.mount("/content/gdrive", force_remount=True).
Get the path to the training database.
path = Path('/content/gdrive/My Drive/FaceDatabase/BioID-FaceDatabase-V1.2')
Define supporting functions¶
def get_eyes(imagepath):
'''
get_eyes: returns the center of the eyes for the image at imagepath
Parameters:
imagepath: Path object pointing to image file; must have corresponding .eye file
returns
Tensor(y1, x1, y2, x2)
'''
txtpath = imgpath2txtpath(imagepath)
xy_eyes = np.genfromtxt(txtpath)
# NOTE: fastai convention is to have y,x coords (ie row, col)
return tensor([ [xy_eyes[1], xy_eyes[0]],
[xy_eyes[3], xy_eyes[2]] ])
#end get_eyes
def imgpath2txtpath(img_path):
'''
imagpath2txtpath: converts a image Path object into a corresponding path to a file holding text eye coordinates
Parameters:
img_path - Path object for image file in BioID-FaceDatabase-V1.2
Returns:
Path object for corresponding eye coordinate file
Limitations:
No validation is done that the Path object file exists, or is a BioID image file
'''
txt_path = img_path.parent/(img_path.stem+'.eye')
return txt_path
#end imgpath2textpath
def get_ctr(imagepath):
'''
get_ctr: returns the center of the left eye for the image at imagepath
Parameters:
imagepath: Path object pointing to image file; must have corresponding .eye file
returns
Tensor(y, x)
'''
txtpath = imgpath2txtpath(imagepath)
xy_eyes = np.genfromtxt(txtpath)
# NOTE: fastai convention is to have y,x coords (ie row, col)
return tensor([xy_eyes[1], xy_eyes[0] ])
#end get_ctr
def get_ip(img,pts):
return ImagePoints(FlowField(img.size, pts), scale=True)
#end get_ip
Define our model¶
We define our model, and load our previously saved trained model
# Note that image warping sometime moves eye out of frame; if we have remove_out=True(default)
# then we get a wrong size Tensor error
data2 = (PointsItemList.from_folder(path)
.split_by_rand_pct(valid_pct=0.2, seed=42)
.label_from_func(get_eyes)
.transform(get_transforms(), tfm_y=True, size=(120,160), remove_out=False)
.databunch().normalize(imagenet_stats)
)
Here we show examples of the images we used to train our model (in fastai terms, our DataBunches).
data2.show_batch(3, figsize=(9,6))
Define the architecture of our model.
learn2 = cnn_learner(data2, models.resnet34)
Downloading: "https://download.pytorch.org/models/resnet34-333f7ec4.pth" to /root/.cache/torch/checkpoints/resnet34-333f7ec4.pth 100%|██████████| 87306240/87306240 [00:00<00:00, 111621929.67it/s]
Load our previously saved (trained) model.
learn2.load('/content/gdrive/My Drive/FaceDatabase/face2-stage3');
Access Yale Face Database¶
For illustrative purposes, we get the first subject, under centre lighting.
yale_path = Path('/content/gdrive/My Drive/FaceDatabase/yalefaces/subject01.centerlight.jpg')
img_yale = open_image(yale_path)
img_yale.show(figsize=(6,6))
Now we use the fastai CNN model to locate the eyes.
pred_yale = learn2.predict(img_yale)
The prediction we get back from the model is scalled to be between -1 to +1: we define a helper function to support image display.
pred_yale
(ImagePoints (120, 160), tensor([[-0.0766, 0.3662],
[-0.0728, 0.0668]]), tensor([-0.0766, 0.3662, -0.0728, 0.0668]))
Define a helper function to take an image, and show the prediction location of the eyes.
def show_eye_predictions(model, image) -> None:
'''
show_eye_predictions: show the predicted eye locations for input image
Parameters:
model: fastai learner (given image, returns two scaled row,col locations)
image: fastai Image
Returns:
None
Side Effects:
Shows image in Notebook
'''
prediction = model.predict(image)
eye_locations = prediction[1]
eye1 = eye_locations[0]
eye2 = eye_locations[1]
# get row and column coordinates of each eye
r1 = float(eye1[0])
c1 = float(eye1[1])
r2 = float(eye2[0])
c2 = float(eye2[1])
# scale predicted positions from the (-1,-1) to (1,1) square
# to image coordinates
scaled_eye_1 = resize_predicted_point(r1, c1, image.size[0], image.size[1])
scaled_eye_2 = resize_predicted_point(r2, c2, image.size[0], image.size[1])
# create a tensor of floats for get_ip
eye_locs = tensor(
[ [float(scaled_eye_1[0]), float(scaled_eye_1[1]) ],
[float(scaled_eye_2[0]), float(scaled_eye_2[1]) ] ]
)
image.show(y=get_ip(image, eye_locs), figsize=(6,6))
return None
#end show_eye_predictions
Show the predicted eye locations for our first Yale image.
show_eye_predictions(learn2, img_yale)
Count the images in the Yale Face Database.
yale_db_path = Path('/content/gdrive/My Drive/FaceDatabase/yalefaces')
print('Yale face count = ', len(list(yale_db_path.glob('*.jpg'))))
Yale face count = 165
We now run through an example subject under different lighting conditions, and face pose.
faces = list(yale_db_path.glob('*.jpg'))
for f in faces[15:20]:
f_image = open_image(f)
show_eye_predictions(learn2, f_image)
#end for
So far the results are reasonably OK. Now let us look at faces in different poses. First get all images where the subject is winking.
faces_wink = list(yale_db_path.glob('*wink*.jpg'))
for f in faces_wink[0:6]:
f_image = open_image(f)
show_eye_predictions(learn2, f_image)
#end for
The results are considerably worse for some subects (especially the last).
Now try images where the subject is wearing glasses.
faces_glasses = list(yale_db_path.glob('*.glasses*.jpg'))
for f in faces_glasses[0:5]:
f_image = open_image(f)
show_eye_predictions(learn2, f_image)
#end for
Conclusions¶
So we can confidently say that wearing dark glasses completely throws my model off in the X direction, but still works OK in the Y direction. Which is interesting: it seems the the gross overall features of the face are not used, but some fine-grained features of the eye area (to be expected, seeing this is a Convolution NN).
It seems that if this model was to be used in a production setting, we would have to also define a model for winking, or glasses-wearing faces, or train our CNN model on a lot more faces in a variety of poses.