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detection module refactored
This commit is contained in:
Sefik Ilkin Serengil
parent
285c6b7e36
commit
fefb0dcd14
@ -1,183 +0,0 @@
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from typing import List, Tuple
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import numpy as np
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import cv2
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from deepface.modules import detection, modeling
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from deepface.models.Detector import Detector, DetectedFace, FacialAreaRegion
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from deepface.commons.logger import Logger
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logger = Logger()
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def detect_faces(
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detector_backend: str, img: np.ndarray, align: bool = True, expand_percentage: int = 0
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) -> List[DetectedFace]:
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"""
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Detect face(s) from a given image
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Args:
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detector_backend (str): detector name
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img (np.ndarray): pre-loaded image
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align (bool): enable or disable alignment after detection
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expand_percentage (int): expand detected facial area with a percentage (default is 0).
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Returns:
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results (List[DetectedFace]): A list of DetectedFace objects
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where each object contains:
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- img (np.ndarray): The detected face as a NumPy array.
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- facial_area (FacialAreaRegion): The facial area region represented as x, y, w, h,
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left_eye and right eye. left eye and right eye are eyes on the left and right
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with respect to the person instead of observer.
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- confidence (float): The confidence score associated with the detected face.
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"""
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height, width, _ = img.shape
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face_detector: Detector = modeling.build_model(
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task="face_detector", model_name=detector_backend
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)
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# validate expand percentage score
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if expand_percentage < 0:
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logger.warn(
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f"Expand percentage cannot be negative but you set it to {expand_percentage}."
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"Overwritten it to 0."
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)
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expand_percentage = 0
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# If faces are close to the upper boundary, alignment move them outside
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# Add a black border around an image to avoid this.
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height_border = int(0.5 * height)
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width_border = int(0.5 * width)
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if align is True:
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img = cv2.copyMakeBorder(
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img,
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height_border,
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height_border,
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width_border,
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width_border,
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cv2.BORDER_CONSTANT,
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value=[0, 0, 0], # Color of the border (black)
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)
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# find facial areas of given image
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facial_areas = face_detector.detect_faces(img)
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results = []
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for facial_area in facial_areas:
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x = facial_area.x
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y = facial_area.y
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w = facial_area.w
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h = facial_area.h
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left_eye = facial_area.left_eye
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right_eye = facial_area.right_eye
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confidence = facial_area.confidence
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if expand_percentage > 0:
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# Expand the facial region height and width by the provided percentage
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# ensuring that the expanded region stays within img.shape limits
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expanded_w = w + int(w * expand_percentage / 100)
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expanded_h = h + int(h * expand_percentage / 100)
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x = max(0, x - int((expanded_w - w) / 2))
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y = max(0, y - int((expanded_h - h) / 2))
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w = min(img.shape[1] - x, expanded_w)
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h = min(img.shape[0] - y, expanded_h)
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# extract detected face unaligned
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detected_face = img[int(y) : int(y + h), int(x) : int(x + w)]
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# align original image, then find projection of detected face area after alignment
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if align is True: # and left_eye is not None and right_eye is not None:
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aligned_img, angle = detection.align_face(
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img=img, left_eye=left_eye, right_eye=right_eye
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)
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rotated_x1, rotated_y1, rotated_x2, rotated_y2 = rotate_facial_area(
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facial_area=(x, y, x + w, y + h), angle=angle, size=(img.shape[0], img.shape[1])
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)
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detected_face = aligned_img[
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int(rotated_y1) : int(rotated_y2), int(rotated_x1) : int(rotated_x2)
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]
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# restore x, y, le and re before border added
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x = x - width_border
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y = y - height_border
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# w and h will not change
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if left_eye is not None:
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left_eye = (left_eye[0] - width_border, left_eye[1] - height_border)
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if right_eye is not None:
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right_eye = (right_eye[0] - width_border, right_eye[1] - height_border)
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result = DetectedFace(
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img=detected_face,
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facial_area=FacialAreaRegion(
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x=x, y=y, h=h, w=w, confidence=confidence, left_eye=left_eye, right_eye=right_eye
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),
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confidence=confidence,
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)
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results.append(result)
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return results
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def rotate_facial_area(
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facial_area: Tuple[int, int, int, int], angle: float, size: Tuple[int, int]
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) -> Tuple[int, int, int, int]:
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"""
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Rotate the facial area around its center.
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Inspried from the work of @UmutDeniz26 - github.com/serengil/retinaface/pull/80
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Args:
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facial_area (tuple of int): Representing the (x1, y1, x2, y2) of the facial area.
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x2 is equal to x1 + w1, and y2 is equal to y1 + h1
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angle (float): Angle of rotation in degrees. Its sign determines the direction of rotation.
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Note that angles > 360 degrees are normalized to the range [0, 360).
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size (tuple of int): Tuple representing the size of the image (width, height).
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Returns:
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rotated_coordinates (tuple of int): Representing the new coordinates
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(x1, y1, x2, y2) or (x1, y1, x1+w1, y1+h1) of the rotated facial area.
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"""
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# Normalize the witdh of the angle so we don't have to
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# worry about rotations greater than 360 degrees.
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# We workaround the quirky behavior of the modulo operator
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# for negative angle values.
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direction = 1 if angle >= 0 else -1
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angle = abs(angle) % 360
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if angle == 0:
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return facial_area
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# Angle in radians
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angle = angle * np.pi / 180
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height, weight = size
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# Translate the facial area to the center of the image
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x = (facial_area[0] + facial_area[2]) / 2 - weight / 2
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y = (facial_area[1] + facial_area[3]) / 2 - height / 2
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# Rotate the facial area
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x_new = x * np.cos(angle) + y * direction * np.sin(angle)
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y_new = -x * direction * np.sin(angle) + y * np.cos(angle)
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# Translate the facial area back to the original position
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x_new = x_new + weight / 2
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y_new = y_new + height / 2
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# Calculate projected coordinates after alignment
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x1 = x_new - (facial_area[2] - facial_area[0]) / 2
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y1 = y_new - (facial_area[3] - facial_area[1]) / 2
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x2 = x_new + (facial_area[2] - facial_area[0]) / 2
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y2 = y_new + (facial_area[3] - facial_area[1]) / 2
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# validate projected coordinates are in image's boundaries
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x1 = max(int(x1), 0)
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y1 = max(int(y1), 0)
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x2 = min(int(x2), weight)
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y2 = min(int(y2), height)
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return (x1, y1, x2, y2)
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@ -8,8 +8,7 @@ from PIL import Image
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# project dependencies
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from deepface.modules import modeling
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from deepface.models.Detector import DetectedFace, FacialAreaRegion
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from deepface.detectors import DetectorWrapper
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from deepface.models.Detector import Detector, DetectedFace, FacialAreaRegion
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from deepface.commons import image_utils
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from deepface.commons.logger import Logger
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@ -91,7 +90,7 @@ def extract_faces(
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if detector_backend == "skip":
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face_objs = [DetectedFace(img=img, facial_area=base_region, confidence=0)]
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else:
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face_objs = DetectorWrapper.detect_faces(
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face_objs = detect_faces(
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detector_backend=detector_backend,
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img=img,
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align=align,
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@ -173,7 +172,120 @@ def extract_faces(
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return resp_objs
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def align_face(
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def detect_faces(
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detector_backend: str, img: np.ndarray, align: bool = True, expand_percentage: int = 0
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) -> List[DetectedFace]:
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"""
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Detect face(s) from a given image
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Args:
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detector_backend (str): detector name
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img (np.ndarray): pre-loaded image
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align (bool): enable or disable alignment after detection
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expand_percentage (int): expand detected facial area with a percentage (default is 0).
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Returns:
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results (List[DetectedFace]): A list of DetectedFace objects
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where each object contains:
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- img (np.ndarray): The detected face as a NumPy array.
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- facial_area (FacialAreaRegion): The facial area region represented as x, y, w, h,
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left_eye and right eye. left eye and right eye are eyes on the left and right
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with respect to the person instead of observer.
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- confidence (float): The confidence score associated with the detected face.
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"""
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height, width, _ = img.shape
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face_detector: Detector = modeling.build_model(
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task="face_detector", model_name=detector_backend
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)
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# validate expand percentage score
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if expand_percentage < 0:
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logger.warn(
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f"Expand percentage cannot be negative but you set it to {expand_percentage}."
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"Overwritten it to 0."
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)
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expand_percentage = 0
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# If faces are close to the upper boundary, alignment move them outside
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# Add a black border around an image to avoid this.
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height_border = int(0.5 * height)
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width_border = int(0.5 * width)
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if align is True:
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img = cv2.copyMakeBorder(
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img,
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height_border,
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height_border,
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width_border,
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width_border,
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cv2.BORDER_CONSTANT,
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value=[0, 0, 0], # Color of the border (black)
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)
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# find facial areas of given image
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facial_areas = face_detector.detect_faces(img)
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results = []
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for facial_area in facial_areas:
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x = facial_area.x
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y = facial_area.y
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w = facial_area.w
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h = facial_area.h
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left_eye = facial_area.left_eye
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right_eye = facial_area.right_eye
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confidence = facial_area.confidence
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if expand_percentage > 0:
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# Expand the facial region height and width by the provided percentage
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# ensuring that the expanded region stays within img.shape limits
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expanded_w = w + int(w * expand_percentage / 100)
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expanded_h = h + int(h * expand_percentage / 100)
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x = max(0, x - int((expanded_w - w) / 2))
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y = max(0, y - int((expanded_h - h) / 2))
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w = min(img.shape[1] - x, expanded_w)
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h = min(img.shape[0] - y, expanded_h)
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# extract detected face unaligned
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detected_face = img[int(y) : int(y + h), int(x) : int(x + w)]
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# align original image, then find projection of detected face area after alignment
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if align is True: # and left_eye is not None and right_eye is not None:
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aligned_img, angle = align_img_wrt_eyes(img=img, left_eye=left_eye, right_eye=right_eye)
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rotated_x1, rotated_y1, rotated_x2, rotated_y2 = project_facial_area(
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facial_area=(x, y, x + w, y + h), angle=angle, size=(img.shape[0], img.shape[1])
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)
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detected_face = aligned_img[
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int(rotated_y1) : int(rotated_y2), int(rotated_x1) : int(rotated_x2)
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]
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# restore x, y, le and re before border added
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x = x - width_border
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y = y - height_border
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# w and h will not change
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if left_eye is not None:
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left_eye = (left_eye[0] - width_border, left_eye[1] - height_border)
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if right_eye is not None:
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right_eye = (right_eye[0] - width_border, right_eye[1] - height_border)
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result = DetectedFace(
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img=detected_face,
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facial_area=FacialAreaRegion(
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x=x, y=y, h=h, w=w, confidence=confidence, left_eye=left_eye, right_eye=right_eye
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),
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confidence=confidence,
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)
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results.append(result)
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return results
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def align_img_wrt_eyes(
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img: np.ndarray,
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left_eye: Union[list, tuple],
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right_eye: Union[list, tuple],
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@ -198,3 +310,64 @@ def align_face(
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angle = float(np.degrees(np.arctan2(left_eye[1] - right_eye[1], left_eye[0] - right_eye[0])))
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img = np.array(Image.fromarray(img).rotate(angle, resample=Image.BICUBIC))
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return img, angle
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def project_facial_area(
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facial_area: Tuple[int, int, int, int], angle: float, size: Tuple[int, int]
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) -> Tuple[int, int, int, int]:
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"""
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Update pre-calculated facial area coordinates after image itself
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rotated with respect to the eyes.
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Inspried from the work of @UmutDeniz26 - github.com/serengil/retinaface/pull/80
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Args:
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facial_area (tuple of int): Representing the (x1, y1, x2, y2) of the facial area.
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x2 is equal to x1 + w1, and y2 is equal to y1 + h1
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angle (float): Angle of rotation in degrees. Its sign determines the direction of rotation.
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Note that angles > 360 degrees are normalized to the range [0, 360).
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size (tuple of int): Tuple representing the size of the image (width, height).
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Returns:
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rotated_coordinates (tuple of int): Representing the new coordinates
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(x1, y1, x2, y2) or (x1, y1, x1+w1, y1+h1) of the rotated facial area.
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"""
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# Normalize the witdh of the angle so we don't have to
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# worry about rotations greater than 360 degrees.
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# We workaround the quirky behavior of the modulo operator
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# for negative angle values.
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direction = 1 if angle >= 0 else -1
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angle = abs(angle) % 360
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if angle == 0:
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return facial_area
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# Angle in radians
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angle = angle * np.pi / 180
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height, weight = size
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# Translate the facial area to the center of the image
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x = (facial_area[0] + facial_area[2]) / 2 - weight / 2
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y = (facial_area[1] + facial_area[3]) / 2 - height / 2
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# Rotate the facial area
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x_new = x * np.cos(angle) + y * direction * np.sin(angle)
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y_new = -x * direction * np.sin(angle) + y * np.cos(angle)
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# Translate the facial area back to the original position
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x_new = x_new + weight / 2
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y_new = y_new + height / 2
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# Calculate projected coordinates after alignment
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x1 = x_new - (facial_area[2] - facial_area[0]) / 2
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y1 = y_new - (facial_area[3] - facial_area[1]) / 2
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x2 = x_new + (facial_area[2] - facial_area[0]) / 2
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y2 = y_new + (facial_area[3] - facial_area[1]) / 2
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# validate projected coordinates are in image's boundaries
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x1 = max(int(x1), 0)
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y1 = max(int(y1), 0)
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x2 = min(int(x2), weight)
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y2 = min(int(y2), height)
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return (x1, y1, x2, y2)
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