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opencv pseudo batching

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galthran-wq 2025-02-12 15:49:35 +00:00
parent 737ee793dc
commit b2d6178bed
1 changed files with 40 additions and 39 deletions
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27
deepface/models/face_detection/OpenCv.py
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@ -1,6 +1,6 @@
# built-in dependencies
import os
from typing import Any, List
from typing import Any, List, Union
# 3rd party dependencies
import cv2
@ -29,25 +29,26 @@ class OpenCvClient(Detector):
detector["eye_detector"] = self.__build_cascade("haarcascade_eye")
return detector
def detect_faces(self, img: np.ndarray) -> List[FacialAreaRegion]:
def detect_faces(self, imgs: Union[np.ndarray, List[np.ndarray]]) -> Union[List[FacialAreaRegion], List[List[FacialAreaRegion]]]:
"""
Detect and align face with opencv
Args:
img (np.ndarray): pre-loaded image as numpy array
imgs (Union[np.ndarray, List[np.ndarray]]): pre-loaded image as numpy array or a list of those
Returns:
results (List[FacialAreaRegion]): A list of FacialAreaRegion objects
results (Union[List[FacialAreaRegion], List[List[FacialAreaRegion]]]): A list or a list of lists of FacialAreaRegion objects
"""
if isinstance(imgs, np.ndarray):
imgs = [imgs]
batch_results = []
for img in imgs:
resp = []
detected_face = None
faces = []
try:
# faces = detector["face_detector"].detectMultiScale(img, 1.3, 5)
# note that, by design, opencv's haarcascade scores are >0 but not capped at 1
faces, _, scores = self.model["face_detector"].detectMultiScale3(
img, 1.1, 10, outputRejectLevels=True
)
@ -56,11 +57,9 @@ class OpenCvClient(Detector):
if len(faces) > 0:
for (x, y, w, h), confidence in zip(faces, scores):
detected_face = img[int(y) : int(y + h), int(x) : int(x + w)]
detected_face = img[int(y):int(y + h), int(x):int(x + w)]
left_eye, right_eye = self.find_eyes(img=detected_face)
# eyes found in the detected face instead image itself
# detected face's coordinates should be added
if left_eye is not None:
left_eye = (int(x + left_eye[0]), int(y + left_eye[1]))
if right_eye is not None:
@ -77,7 +76,9 @@ class OpenCvClient(Detector):
)
resp.append(facial_area)
return resp
batch_results.append(resp)
return batch_results if len(batch_results) > 1 else batch_results[0]
def find_eyes(self, img: np.ndarray) -> tuple:
"""