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some linting comments after yunet implementation

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Sefik Ilkin Serengil 2023-07-09 12:41:31 +01:00
parent 0879926e51
commit 8bb52c0317
1 changed files with 17 additions and 14 deletions
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31
deepface/detectors/YunetWrapper.py
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@ -1,21 +1,22 @@
import cv2
import os
import cv2
import gdown
from deepface.detectors import FaceDetector
from deepface.commons import functions
def build_model():
url = "https://github.com/opencv/opencv_zoo/raw/main/models/face_detection_yunet/face_detection_yunet_2023mar.onnx"
url = (
"https://github.com/opencv/opencv_zoo/raw/main/models/"
+ "face_detection_yunet/face_detection_yunet_2023mar.onnx"
)
file_name = "face_detection_yunet_2023mar.onnx"
home = functions.get_deepface_home()
if os.path.isfile(home + f"/.deepface/weights/{file_name}") is False:
print(f"{file_name} will be downloaded...")
output = home + f"/.deepface/weights/{file_name}"
gdown.download(url, output, quiet=False)
face_detector = cv2.FaceDetectorYN_create(
home + f"/.deepface/weights/{file_name}", "", (0, 0)
)
face_detector = cv2.FaceDetectorYN_create(home + f"/.deepface/weights/{file_name}", "", (0, 0))
return face_detector
@ -43,14 +44,16 @@ def detect_face(detector, image, align=True, score_threshold=0.9):
if faces is None:
return resp
for face in faces:
"""
The detection output faces is a two-dimension array of type CV_32F,
whose rows are the detected face instances, columns are the location of a face and 5 facial landmarks.
The format of each row is as follows:
x1, y1, w, h, x_re, y_re, x_le, y_le, x_nt, y_nt, x_rcm, y_rcm, x_lcm, y_lcm,
where x1, y1, w, h are the top-left coordinates, width and height of the face bounding box,
{x, y}_{re, le, nt, rcm, lcm} stands for the coordinates of right eye, left eye, nose tip, the right corner and left corner of the mouth respectively.
"""
# The detection output faces is a two-dimension array of type CV_32F,
# whose rows are the detected face instances,
# columns are the location of a face and 5 facial landmarks.
# The format of each row is as follows:
# x1, y1, w, h, x_re, y_re, x_le, y_le, x_nt, y_nt, x_rcm, y_rcm, x_lcm, y_lcm,
# where x1, y1, w, h are the top-left coordinates,
# width and height of the face bounding box,
# {x, y}_{re, le, nt, rcm, lcm} stands for the coordinates of
# right eye, left eye, nose tip, the right corner and left corner
# of the mouth respectively.
(x, y, w, h, x_re, y_re, x_le, y_le) = list(map(int, face[:8]))
if resized:
image = original_image
@ -62,7 +65,7 @@ def detect_face(detector, image, align=True, score_threshold=0.9):
int(y_le / r),
)
confidence = face[-1]
confidence = "{:.2f}".format(confidence)
confidence = f"{confidence:.2f}"
detected_face = image[int(y) : int(y + h), int(x) : int(x + w)]
img_region = [x, y, w, h]
if align: