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Linting + optimizations

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Josh 2024-08-26 14:34:00 +01:00
parent cc8c3f0461
commit f4d164e0aa
1 changed files with 28 additions and 18 deletions
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44
deepface/modules/detection.py
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@ -2,9 +2,9 @@
from typing import Any, Dict, List, Tuple, Union, Optional from typing import Any, Dict, List, Tuple, Union, Optional
# 3rd part dependencies # 3rd part dependencies
from heapq import nlargest
import numpy as np import numpy as np
import cv2 import cv2
from PIL import Image
# project dependencies # project dependencies
from deepface.modules import modeling from deepface.modules import modeling
@ -179,7 +179,9 @@ def extract_faces(
def detect_faces( def detect_faces(
detector_backend: str, img: np.ndarray, align: bool = True, expand_percentage: int = 0, max_faces: Optional[int] = None detector_backend: str, img: np.ndarray,
align: bool = True, expand_percentage: int = 0,
max_faces: Optional[int] = None
) -> List[DetectedFace]: ) -> List[DetectedFace]:
""" """
Detect face(s) from a given image Detect face(s) from a given image
@ -205,7 +207,6 @@ def detect_faces(
- confidence (float): The confidence score associated with the detected face. - confidence (float): The confidence score associated with the detected face.
""" """
height, width, _ = img.shape height, width, _ = img.shape
face_detector: Detector = modeling.build_model( face_detector: Detector = modeling.build_model(
task="face_detector", model_name=detector_backend task="face_detector", model_name=detector_backend
) )
@ -237,23 +238,28 @@ def detect_faces(
facial_areas = face_detector.detect_faces(img) facial_areas = face_detector.detect_faces(img)
if max_faces is not None and max_faces < len(facial_areas): if max_faces is not None and max_faces < len(facial_areas):
# sort as largest facial areas come first facial_areas = nlargest(
facial_areas = sorted( max_faces,
facial_areas, facial_areas,
key=lambda facial_area: facial_area.w * facial_area.h, key=lambda facial_area: facial_area.w * facial_area.h
reverse=True,
) )
# discard rest of the items
facial_areas = facial_areas[0:max_faces]
results = [] return [
expand_and_align_face(
facial_area=facial_area,
img=img,
align=align,
expand_percentage=expand_percentage,
width_border=width_border,
height_border=height_border
)
for facial_area in facial_areas
]
for facial_area in facial_areas: def expand_and_align_face(
results.append(expand_and_align_face(facial_area=facial_area, img=img, align=align, expand_percentage=expand_percentage, width_border=width_border, height_border=height_border)) facial_area: FacialAreaRegion, img: np.ndarray,
align: bool, expand_percentage: int, width_border: int,
return results height_border: int) -> DetectedFace:
def expand_and_align_face(facial_area: FacialAreaRegion, img: np.ndarray, align: bool, expand_percentage: int, width_border: int, height_border: int) -> DetectedFace:
x = facial_area.x x = facial_area.x
y = facial_area.y y = facial_area.y
w = facial_area.w w = facial_area.w
@ -330,7 +336,11 @@ def align_img_wrt_eyes(
(h, w) = img.shape[:2] (h, w) = img.shape[:2]
center = (w // 2, h // 2) center = (w // 2, h // 2)
M = cv2.getRotationMatrix2D(center, angle, 1.0) M = cv2.getRotationMatrix2D(center, angle, 1.0)
img = cv2.warpAffine(img, M, (w, h), flags=cv2.INTER_CUBIC, borderMode=cv2.BORDER_CONSTANT, borderValue=(0,0,0)) img = cv2.warpAffine(
img, M, (w, h),
flags=cv2.INTER_CUBIC, borderMode=cv2.BORDER_CONSTANT,
borderValue=(0,0,0)
)
return img, angle return img, angle