change the order of arguments

[glucose6126]

When calling the postprocess.alignment_procedure function within the RetinaFace.extract_faces function, the order of the arguments is incorrect and changed.

[serengil]

can you monitor the results with this change?

I mean that maybe we need to rename the variables instead of changing order. This might cause problems in alignment.

[glucose6126]

The variable name is set appropriately, but the argument order of alignment_procedure function and the argument order of extract_faces function in RetinaFace are different from each other.

extract_faces function in RetinaFace.py
facial_img = postprocess.alignment_procedure(facial_img, right_eye, left_eye, nose)

alignment_procedure function in postprocess.py
def alignment_procedure(img, left_eye, right_eye, nose):

Therefore, I changed the argument order when calling the alignment_procedure function in extract_faces, and the execution code and function execution result to check the result before and after the change are as follows.

original data

before

execution code

from retinaface import RetinaFace
from retinaface.RetinaFace import *
from retinaface.commons import postprocess
import matplotlib.pyplot as plt
import cv2
import numpy as np

def extract_faces(img_path, threshold=0.9, model = None, align = True, allow_upscaling = True):

    resp = []

    #---------------------------

    img = get_image(img_path)

    #---------------------------

    obj = detect_faces(img_path = img, threshold = threshold, model = model, allow_upscaling = allow_upscaling)
    if type(obj) == dict:
        for key in obj:
            identity = obj[key]

            facial_area = identity["facial_area"]
            facial_img = img[facial_area[1]: facial_area[3], facial_area[0]: facial_area[2]]

            if align == True:
                landmarks = identity["landmarks"]
                left_eye = landmarks["left_eye"]
                right_eye = landmarks["right_eye"]
                nose = landmarks["nose"]
                mouth_right = landmarks["mouth_right"]
                mouth_left = landmarks["mouth_left"]

                facial_img = postprocess.alignment_procedure(facial_img, right_eye, left_eye, nose)

            resp.append(facial_img[:, :, ::-1])
    #elif type(obj) == tuple:

    return resp

img = RetinaFace.get_image("./lenna.jpg")
plt.imshow(cv2.cvtColor(img, cv2.COLOR_BGR2RGB))
plt.title('Original')
plt.axis("off")
plt.show()

for face in extract_faces(img) :
    plt.imshow(face)
    plt.title('extract face before code fix')
    plt.axis("off")
    plt.show()

Execution result

after

execution code

from retinaface import RetinaFace
from retinaface.RetinaFace import *
from retinaface.commons import postprocess
import matplotlib.pyplot as plt
import cv2
import numpy as np

def extract_faces(img_path, threshold=0.9, model = None, align = True, allow_upscaling = True):

    resp = []

    #---------------------------

    img = get_image(img_path)

    #---------------------------

    obj = detect_faces(img_path = img, threshold = threshold, model = model, allow_upscaling = allow_upscaling)
    if type(obj) == dict:
        for key in obj:
            identity = obj[key]

            facial_area = identity["facial_area"]
            facial_img = img[facial_area[1]: facial_area[3], facial_area[0]: facial_area[2]]

            if align == True:
                landmarks = identity["landmarks"]
                left_eye = landmarks["left_eye"]
                right_eye = landmarks["right_eye"]
                nose = landmarks["nose"]
                mouth_right = landmarks["mouth_right"]
                mouth_left = landmarks["mouth_left"]

                facial_img = postprocess.alignment_procedure(facial_img, left_eye, right_eye, nose)

            resp.append(facial_img[:, :, ::-1])
    #elif type(obj) == tuple:

    return resp

img = RetinaFace.get_image("./lenna.jpg")
plt.imshow(cv2.cvtColor(img, cv2.COLOR_BGR2RGB))
plt.title('Original')
plt.axis("off")
plt.show()

for face in extract_faces(img) :
    plt.imshow(face)
    plt.title('extract face after code fix')
    plt.axis("off")
    plt.show()

execution result

[serengil]

If you run unit tests, you will see your approach is not working

Before:

After your change:

[serengil]

So, unit tests show that as is approach is totally fine. i am closing this PR.