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some cosmetic changes
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@serengil
serengil committed Feb 10, 2024
1 parent adfa54b commit 2d5b56e
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Showing 3 changed files with 98 additions and 93 deletions.
133 changes: 48 additions & 85 deletions retinaface/RetinaFace.py
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Original file line number Diff line number Diff line change
@@ -1,7 +1,7 @@
import os
import warnings
import logging
from typing import Union, Any, Optional, Dict, Tuple
from typing import Union, Any, Optional, Dict

import numpy as np
import tensorflow as tf
Expand Down Expand Up @@ -243,89 +243,52 @@ def extract_faces(
"Align first functionality can be applied only if there is single face in the input"
)

if isinstance(obj, dict):
for _, identity in obj.items():
facial_area = identity["facial_area"]

x = facial_area[0]
y = facial_area[1]
w = facial_area[2]
h = facial_area[3]

# expand the facial area to be extracted and stay within img.shape limits
x1 = max(0, x - int((w * expand_face_area) / 100)) # expand left
y1 = max(0, y - int((h * expand_face_area) / 100)) # expand top
x2 = min(img.shape[1], w + int((w * expand_face_area) / 100)) # expand right
y2 = min(img.shape[0], h + int((h * expand_face_area) / 100)) # expand bottom

if align_first is False or (align_first is True and len(obj) > 1):
facial_img = img[y1:y2, x1:x2]
else:
facial_img = img.copy()

if align is 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, rotate_angle, rotate_direction = postprocess.alignment_procedure(
facial_img, right_eye, left_eye, nose
)

if align_first is True and len(obj) == 1:
facial_area = rotate_facial_area(
facial_area, rotate_angle, rotate_direction, img.shape
)
# Expand the facial area to be extracted and stay within img.shape limits
x1 = max(0, facial_area[0] - int((facial_area[2] * expand_face_area) / 100))
y1 = max(0, facial_area[1] - int((facial_area[3] * expand_face_area) / 100))
x2 = min(
img.shape[1], facial_area[2] + int((facial_area[2] * expand_face_area) / 100)
)
y2 = min(
img.shape[0], facial_area[3] + int((facial_area[3] * expand_face_area) / 100)
)
facial_img = facial_img[y1:y2, x1:x2]

resp.append(facial_img[:, :, ::-1])

return resp

def rotate_facial_area(facial_area: Tuple[int, int, int, int], angle: float, direction:
int, size: Tuple[int, int]) -> Tuple[int, int, int, int]:
"""
Rotate the facial area around its center.
Args:
facial_area (tuple of int): Representing the (x1, y1, x2, y2) of the facial area.
angle (float): Angle of rotation in degrees.
direction (int): Direction of rotation (-1 for clockwise, 1 for counterclockwise).
size (tuple of int): Tuple representing the size of the image (width, height).
Returns:
tuple of int: Representing the new coordinates (x1, y1, x2, y2) of the rotated facial area.
"""
# Angle in radians
angle = angle * np.pi / 180

# Translate the facial area to the center of the image
x = (facial_area[0] + facial_area[2]) / 2 - size[1] / 2
y = (facial_area[1] + facial_area[3]) / 2 - size[0] / 2

# Rotate the facial area
x_new = x * np.cos(angle) + y * direction * np.sin(angle)
y_new = -x * direction * np.sin(angle) + y * np.cos(angle)

# Translate the facial area back to the original position
x_new = x_new + size[1] / 2
y_new = y_new + size[0] / 2
if not isinstance(obj, dict):
return resp

# Calculate the new facial area
x1 = x_new - (facial_area[2] - facial_area[0]) / 2
y1 = y_new - (facial_area[3] - facial_area[1]) / 2
x2 = x_new + (facial_area[2] - facial_area[0]) / 2
y2 = y_new + (facial_area[3] - facial_area[1]) / 2
for _, identity in obj.items():
facial_area = identity["facial_area"]
rotate_angle = 0
rotate_direction = 1

x = facial_area[0]
y = facial_area[1]
w = facial_area[2]
h = facial_area[3]

# expand the facial area to be extracted and stay within img.shape limits
x1 = max(0, x - int((w * expand_face_area) / 100)) # expand left
y1 = max(0, y - int((h * expand_face_area) / 100)) # expand top
x2 = min(img.shape[1], w + int((w * expand_face_area) / 100)) # expand right
y2 = min(img.shape[0], h + int((h * expand_face_area) / 100)) # expand bottom

if align_first is False or (align_first is True and len(obj) > 1):
facial_img = img[y1:y2, x1:x2]
else:
facial_img = img.copy()

if align is 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, rotate_angle, rotate_direction = postprocess.alignment_procedure(
facial_img, right_eye, left_eye, nose
)

if align_first is True and len(obj) == 1:
facial_area = postprocess.rotate_facial_area(
facial_area, rotate_angle, rotate_direction, img.shape
)
# Expand the facial area to be extracted and stay within img.shape limits
x1 = max(0, facial_area[0] - int((facial_area[2] * expand_face_area) / 100))
y1 = max(0, facial_area[1] - int((facial_area[3] * expand_face_area) / 100))
x2 = min(img.shape[1], facial_area[2] + int((facial_area[2] * expand_face_area) / 100))
y2 = min(img.shape[0], facial_area[3] + int((facial_area[3] * expand_face_area) / 100))
facial_img = facial_img[y1:y2, x1:x2]

resp.append(facial_img[:, :, ::-1])

return (int(x1), int(y1), int(x2), int(y2))
return resp
54 changes: 48 additions & 6 deletions retinaface/commons/postprocess.py
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Original file line number Diff line number Diff line change
@@ -1,13 +1,13 @@
import math
from typing import Union
from typing import Union, Tuple
import numpy as np
from PIL import Image


# pylint: disable=unused-argument


def findEuclideanDistance(
def find_euclidean_distance(
source_representation: Union[np.ndarray, list], test_representation: Union[np.ndarray, list]
) -> float:
"""
Expand All @@ -30,7 +30,9 @@ def findEuclideanDistance(
return euclidean_distance


def alignment_procedure(img: np.ndarray, left_eye: tuple, right_eye: tuple, nose: tuple):
def alignment_procedure(
img: np.ndarray, left_eye: tuple, right_eye: tuple, nose: tuple
) -> Tuple[np.ndarray, float, int]:
"""
Alignma given face with respect to the left and right eye coordinates.
Left eye is the eye appearing on the left (right eye of the person). Left top point is (0, 0)
Expand Down Expand Up @@ -58,9 +60,9 @@ def alignment_procedure(img: np.ndarray, left_eye: tuple, right_eye: tuple, nose
# -----------------------
# find length of triangle edges

a = findEuclideanDistance(np.array(left_eye), np.array(point_3rd))
b = findEuclideanDistance(np.array(right_eye), np.array(point_3rd))
c = findEuclideanDistance(np.array(right_eye), np.array(left_eye))
a = find_euclidean_distance(np.array(left_eye), np.array(point_3rd))
b = find_euclidean_distance(np.array(right_eye), np.array(point_3rd))
c = find_euclidean_distance(np.array(right_eye), np.array(left_eye))

# -----------------------
# apply cosine rule
Expand Down Expand Up @@ -91,6 +93,46 @@ def alignment_procedure(img: np.ndarray, left_eye: tuple, right_eye: tuple, nose
return img, angle, direction


def rotate_facial_area(
facial_area: Tuple[int, int, int, int], angle: float, direction: int, size: Tuple[int, int]
) -> Tuple[int, int, int, int]:
"""
Rotate the facial area around its center.
Args:
facial_area (tuple of int): Representing the (x1, y1, x2, y2) of the facial area.
angle (float): Angle of rotation in degrees.
direction (int): Direction of rotation (-1 for clockwise, 1 for counterclockwise).
size (tuple of int): Tuple representing the size of the image (width, height).
Returns:
rotated_facial_area (tuple of int): Representing the new coordinates
(x1, y1, x2, y2) of the rotated facial area.
"""
# Angle in radians
angle = angle * np.pi / 180

# Translate the facial area to the center of the image
x = (facial_area[0] + facial_area[2]) / 2 - size[1] / 2
y = (facial_area[1] + facial_area[3]) / 2 - size[0] / 2

# Rotate the facial area
x_new = x * np.cos(angle) + y * direction * np.sin(angle)
y_new = -x * direction * np.sin(angle) + y * np.cos(angle)

# Translate the facial area back to the original position
x_new = x_new + size[1] / 2
y_new = y_new + size[0] / 2

# Calculate the new facial area
x1 = x_new - (facial_area[2] - facial_area[0]) / 2
y1 = y_new - (facial_area[3] - facial_area[1]) / 2
x2 = x_new + (facial_area[2] - facial_area[0]) / 2
y2 = y_new + (facial_area[3] - facial_area[1]) / 2

return (int(x1), int(y1), int(x2), int(y2))


def bbox_pred(boxes, box_deltas):
"""
This function is copied from the following code snippet:
Expand Down
4 changes: 2 additions & 2 deletions tests/test_expand_face_area.py
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Original file line number Diff line number Diff line change
Expand Up @@ -25,8 +25,8 @@ def test_expand_face_area():
landmarks1 = obj1["face_1"]["landmarks"]
landmarks2 = obj2["face_1"]["landmarks"]

distance1 = postprocess.findEuclideanDistance(landmarks1["right_eye"], landmarks1["left_eye"])
distance2 = postprocess.findEuclideanDistance(landmarks2["right_eye"], landmarks2["left_eye"])
distance1 = postprocess.find_euclidean_distance(landmarks1["right_eye"], landmarks1["left_eye"])
distance2 = postprocess.find_euclidean_distance(landmarks2["right_eye"], landmarks2["left_eye"])

# 2nd one's expand ratio is higher. so, it should be smaller.
assert distance2 < distance1
Expand Down

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