mirror of
https://github.com/serengil/deepface.git
synced 2025-06-07 03:55:21 +00:00
moving logics under modules
- all extract_faces logic moved to detection module - load image related logics moved to preprocessing module
This commit is contained in:
Sefik Ilkin Serengil
parent
9fbb229b97
commit
95c55c0401
@ -449,6 +449,7 @@ def extract_faces(
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enforce_detection=enforce_detection,
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align=align,
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grayscale=grayscale,
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human_readable=True,
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)
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@ -1,42 +1,19 @@
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import os
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from typing import Union, Tuple, List
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import base64
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from pathlib import Path
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# 3rd party dependencies
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from PIL import Image
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import requests
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import numpy as np
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import cv2
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import tensorflow as tf
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# package dependencies
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from deepface.detectors import DetectorWrapper
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from deepface.models.Detector import DetectedFace, FacialAreaRegion
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from deepface.commons.logger import Logger
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logger = Logger(module="commons.functions")
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# pylint: disable=no-else-raise
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# --------------------------------------------------
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# configurations of dependencies
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def get_tf_major_version() -> int:
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return int(tf.__version__.split(".", maxsplit=1)[0])
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tf_major_version = get_tf_major_version()
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if tf_major_version == 1:
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from keras.preprocessing import image
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elif tf_major_version == 2:
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from tensorflow.keras.preprocessing import image
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# --------------------------------------------------
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def initialize_folder() -> None:
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"""Initialize the folder for storing weights and models.
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@ -65,266 +42,6 @@ def get_deepface_home() -> str:
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return str(os.getenv("DEEPFACE_HOME", default=str(Path.home())))
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# --------------------------------------------------
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def loadBase64Img(uri: str) -> np.ndarray:
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"""Load image from base64 string.
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Args:
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uri: a base64 string.
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Returns:
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numpy array: the loaded image.
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"""
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encoded_data = uri.split(",")[1]
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nparr = np.fromstring(base64.b64decode(encoded_data), np.uint8)
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img_bgr = cv2.imdecode(nparr, cv2.IMREAD_COLOR)
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# img_rgb = cv2.cvtColor(img_bgr, cv2.COLOR_BGR2RGB)
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return img_bgr
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def load_image(img: Union[str, np.ndarray]) -> Tuple[np.ndarray, str]:
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"""
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Load image from path, url, base64 or numpy array.
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Args:
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img: a path, url, base64 or numpy array.
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Returns:
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image (numpy array): the loaded image in BGR format
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image name (str): image name itself
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"""
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# The image is already a numpy array
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if isinstance(img, np.ndarray):
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return img, "numpy array"
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if isinstance(img, Path):
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img = str(img)
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if not isinstance(img, str):
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raise ValueError(f"img must be numpy array or str but it is {type(img)}")
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# The image is a base64 string
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if img.startswith("data:image/"):
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return loadBase64Img(img), "base64 encoded string"
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# The image is a url
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if img.startswith("http"):
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return (
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np.array(Image.open(requests.get(img, stream=True, timeout=60).raw).convert("BGR")),
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# return url as image name
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img,
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)
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# The image is a path
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if os.path.isfile(img) is not True:
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raise ValueError(f"Confirm that {img} exists")
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# image must be a file on the system then
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# image name must have english characters
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if img.isascii() is False:
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raise ValueError(f"Input image must not have non-english characters - {img}")
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img_obj_bgr = cv2.imread(img)
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# img_obj_rgb = cv2.cvtColor(img_obj_bgr, cv2.COLOR_BGR2RGB)
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return img_obj_bgr, img
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# --------------------------------------------------
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def extract_faces(
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img: Union[str, np.ndarray],
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target_size: tuple = (224, 224),
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detector_backend: str = "opencv",
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grayscale: bool = False,
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enforce_detection: bool = True,
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align: bool = True,
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) -> List[Tuple[np.ndarray, dict, float]]:
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"""
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Extract faces from an image.
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Args:
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img: a path, url, base64 or numpy array.
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target_size (tuple, optional): the target size of the extracted faces.
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Defaults to (224, 224).
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detector_backend (str, optional): the face detector backend. Defaults to "opencv".
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grayscale (bool, optional): whether to convert the extracted faces to grayscale.
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Defaults to False.
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enforce_detection (bool, optional): whether to enforce face detection. Defaults to True.
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align (bool, optional): whether to align the extracted faces. Defaults to True.
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Raises:
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ValueError: if face could not be detected and enforce_detection is True.
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Returns:
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results (List[Tuple[np.ndarray, dict, float]]): A list of tuples
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where each tuple contains:
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- detected_face (np.ndarray): The detected face as a NumPy array.
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- face_region (dict): The image region represented as
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{"x": x, "y": y, "w": w, "h": h}
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- confidence (float): The confidence score associated with the detected face.
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"""
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# this is going to store a list of img itself (numpy), it region and confidence
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extracted_faces = []
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# img might be path, base64 or numpy array. Convert it to numpy whatever it is.
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img, img_name = load_image(img)
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base_region = FacialAreaRegion(x=0, y=0, w=img.shape[1], h=img.shape[0])
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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(detector_backend, img, align)
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# in case of no face found
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if len(face_objs) == 0 and enforce_detection is True:
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if img_name is not None:
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raise ValueError(
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f"Face could not be detected in {img_name}."
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"Please confirm that the picture is a face photo "
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"or consider to set enforce_detection param to False."
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)
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else:
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raise ValueError(
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"Face could not be detected. Please confirm that the picture is a face photo "
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"or consider to set enforce_detection param to False."
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)
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if len(face_objs) == 0 and enforce_detection is False:
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face_objs = [DetectedFace(img=img, facial_area=base_region, confidence=0)]
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for face_obj in face_objs:
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current_img = face_obj.img
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current_region = face_obj.facial_area
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confidence = face_obj.confidence
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if current_img.shape[0] > 0 and current_img.shape[1] > 0:
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if grayscale is True:
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current_img = cv2.cvtColor(current_img, cv2.COLOR_BGR2GRAY)
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# resize and padding
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factor_0 = target_size[0] / current_img.shape[0]
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factor_1 = target_size[1] / current_img.shape[1]
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factor = min(factor_0, factor_1)
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dsize = (
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int(current_img.shape[1] * factor),
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int(current_img.shape[0] * factor),
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)
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current_img = cv2.resize(current_img, dsize)
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diff_0 = target_size[0] - current_img.shape[0]
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diff_1 = target_size[1] - current_img.shape[1]
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if grayscale is False:
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# Put the base image in the middle of the padded image
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current_img = np.pad(
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current_img,
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(
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(diff_0 // 2, diff_0 - diff_0 // 2),
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(diff_1 // 2, diff_1 - diff_1 // 2),
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(0, 0),
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),
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"constant",
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)
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else:
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current_img = np.pad(
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current_img,
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(
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(diff_0 // 2, diff_0 - diff_0 // 2),
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(diff_1 // 2, diff_1 - diff_1 // 2),
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),
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"constant",
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)
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# double check: if target image is not still the same size with target.
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if current_img.shape[0:2] != target_size:
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current_img = cv2.resize(current_img, target_size)
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# normalizing the image pixels
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# what this line doing? must?
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img_pixels = image.img_to_array(current_img)
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img_pixels = np.expand_dims(img_pixels, axis=0)
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img_pixels /= 255 # normalize input in [0, 1]
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# int cast is for the exception - object of type 'float32' is not JSON serializable
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region_obj = {
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"x": current_region.x,
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"y": current_region.y,
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"w": current_region.w,
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"h": current_region.h,
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}
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extracted_face = (img_pixels, region_obj, confidence)
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extracted_faces.append(extracted_face)
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if len(extracted_faces) == 0 and enforce_detection == True:
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raise ValueError(
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f"Detected face shape is {img.shape}. Consider to set enforce_detection arg to False."
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)
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return extracted_faces
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def normalize_input(img: np.ndarray, normalization: str = "base") -> np.ndarray:
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"""Normalize input image.
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Args:
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img (numpy array): the input image.
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normalization (str, optional): the normalization technique. Defaults to "base",
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for no normalization.
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Returns:
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numpy array: the normalized image.
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"""
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# issue 131 declares that some normalization techniques improves the accuracy
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if normalization == "base":
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return img
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# @trevorgribble and @davedgd contributed this feature
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# restore input in scale of [0, 255] because it was normalized in scale of
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# [0, 1] in preprocess_face
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img *= 255
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if normalization == "raw":
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pass # return just restored pixels
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elif normalization == "Facenet":
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mean, std = img.mean(), img.std()
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img = (img - mean) / std
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elif normalization == "Facenet2018":
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# simply / 127.5 - 1 (similar to facenet 2018 model preprocessing step as @iamrishab posted)
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img /= 127.5
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img -= 1
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elif normalization == "VGGFace":
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# mean subtraction based on VGGFace1 training data
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img[..., 0] -= 93.5940
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img[..., 1] -= 104.7624
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img[..., 2] -= 129.1863
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elif normalization == "VGGFace2":
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# mean subtraction based on VGGFace2 training data
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img[..., 0] -= 91.4953
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img[..., 1] -= 103.8827
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img[..., 2] -= 131.0912
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elif normalization == "ArcFace":
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# Reference study: The faces are cropped and resized to 112×112,
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# and each pixel (ranged between [0, 255]) in RGB images is normalised
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# by subtracting 127.5 then divided by 128.
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img -= 127.5
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img /= 128
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else:
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raise ValueError(f"unimplemented normalization type - {normalization}")
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return img
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def find_target_size(model_name: str) -> tuple:
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"""Find the target size of the model.
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@ -6,8 +6,7 @@ import numpy as np
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from tqdm import tqdm
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# project dependencies
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from deepface.modules import modeling
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from deepface.commons import functions
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from deepface.modules import modeling, detection
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from deepface.extendedmodels import Gender, Race, Emotion
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@ -114,8 +113,8 @@ def analyze(
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# ---------------------------------
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resp_objects = []
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img_objs = functions.extract_faces(
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img=img_path,
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img_objs = detection.extract_faces(
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img_path=img_path,
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target_size=(224, 224),
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detector_backend=detector_backend,
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grayscale=False,
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@ -123,7 +122,10 @@ def analyze(
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align=align,
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)
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for img_content, img_region, img_confidence in img_objs:
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for img_obj in img_objs:
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img_content = img_obj["face"]
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img_region = img_obj["facial_area"]
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img_confidence = img_obj["confidence"]
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if img_content.shape[0] > 0 and img_content.shape[1] > 0:
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obj = {}
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# facial attribute analysis
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@ -3,10 +3,26 @@ from typing import Any, Dict, List, Tuple, Union
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# 3rd part dependencies
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import numpy as np
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import cv2
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from PIL import Image
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# project dependencies
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from deepface.modules import preprocessing
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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.commons import functions
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from deepface.commons.logger import Logger
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logger = Logger(module="deepface/modules/detection.py")
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# pylint: disable=no-else-raise
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tf_major_version = functions.get_tf_major_version()
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if tf_major_version == 1:
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from keras.preprocessing import image
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elif tf_major_version == 2:
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from tensorflow.keras.preprocessing import image
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def extract_faces(
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@ -16,6 +32,7 @@ def extract_faces(
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enforce_detection: bool = True,
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align: bool = True,
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grayscale: bool = False,
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human_readable=False,
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) -> List[Dict[str, Any]]:
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"""
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Extract faces from a given image
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@ -38,6 +55,8 @@ def extract_faces(
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grayscale (boolean): Flag to convert the image to grayscale before
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processing (default is False).
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human_readable (bool): Flag to make the image human readable. 3D RGB for human readable
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or 4D BGR for ML models (default is False).
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Returns:
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results (List[Dict[str, Any]]): A list of dictionaries, where each dictionary contains:
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@ -48,27 +67,108 @@ def extract_faces(
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resp_objs = []
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img_objs = functions.extract_faces(
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img=img_path,
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target_size=target_size,
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detector_backend=detector_backend,
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grayscale=grayscale,
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enforce_detection=enforce_detection,
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align=align,
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# img might be path, base64 or numpy array. Convert it to numpy whatever it is.
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img, img_name = preprocessing.load_image(img_path)
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base_region = FacialAreaRegion(x=0, y=0, w=img.shape[1], h=img.shape[0])
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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(detector_backend, img, align)
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# in case of no face found
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if len(face_objs) == 0 and enforce_detection is True:
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if img_name is not None:
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raise ValueError(
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f"Face could not be detected in {img_name}."
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"Please confirm that the picture is a face photo "
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"or consider to set enforce_detection param to False."
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)
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else:
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raise ValueError(
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"Face could not be detected. Please confirm that the picture is a face photo "
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"or consider to set enforce_detection param to False."
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)
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for img, region, confidence in img_objs:
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resp_obj = {}
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if len(face_objs) == 0 and enforce_detection is False:
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face_objs = [DetectedFace(img=img, facial_area=base_region, confidence=0)]
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for face_obj in face_objs:
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current_img = face_obj.img
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current_region = face_obj.facial_area
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confidence = face_obj.confidence
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if current_img.shape[0] == 0 or current_img.shape[1] == 0:
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continue
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if grayscale is True:
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current_img = cv2.cvtColor(current_img, cv2.COLOR_BGR2GRAY)
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# resize and padding
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factor_0 = target_size[0] / current_img.shape[0]
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factor_1 = target_size[1] / current_img.shape[1]
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factor = min(factor_0, factor_1)
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dsize = (
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int(current_img.shape[1] * factor),
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int(current_img.shape[0] * factor),
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)
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current_img = cv2.resize(current_img, dsize)
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diff_0 = target_size[0] - current_img.shape[0]
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diff_1 = target_size[1] - current_img.shape[1]
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if grayscale is False:
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# Put the base image in the middle of the padded image
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current_img = np.pad(
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current_img,
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(
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(diff_0 // 2, diff_0 - diff_0 // 2),
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(diff_1 // 2, diff_1 - diff_1 // 2),
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(0, 0),
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),
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"constant",
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)
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else:
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current_img = np.pad(
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current_img,
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(
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(diff_0 // 2, diff_0 - diff_0 // 2),
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(diff_1 // 2, diff_1 - diff_1 // 2),
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),
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"constant",
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)
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# double check: if target image is not still the same size with target.
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if current_img.shape[0:2] != target_size:
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current_img = cv2.resize(current_img, target_size)
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# normalizing the image pixels
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||||
# what this line doing? must?
|
||||
img_pixels = image.img_to_array(current_img)
|
||||
img_pixels = np.expand_dims(img_pixels, axis=0)
|
||||
img_pixels /= 255 # normalize input in [0, 1]
|
||||
# discard expanded dimension
|
||||
if len(img.shape) == 4:
|
||||
img = img[0]
|
||||
if human_readable is True and len(img_pixels.shape) == 4:
|
||||
img_pixels = img_pixels[0]
|
||||
|
||||
# bgr to rgb
|
||||
resp_obj["face"] = img[:, :, ::-1]
|
||||
resp_obj["facial_area"] = region
|
||||
resp_obj["confidence"] = confidence
|
||||
resp_objs.append(resp_obj)
|
||||
resp_objs.append(
|
||||
{
|
||||
"face": img_pixels[:, :, ::-1] if human_readable is True else img_pixels,
|
||||
"facial_area": {
|
||||
"x": current_region.x,
|
||||
"y": current_region.y,
|
||||
"w": current_region.w,
|
||||
"h": current_region.h,
|
||||
},
|
||||
"confidence": confidence,
|
||||
}
|
||||
)
|
||||
|
||||
if len(resp_objs) == 0 and enforce_detection == True:
|
||||
raise ValueError(
|
||||
f"Detected face shape is {img.shape}. Consider to set enforce_detection arg to False."
|
||||
)
|
||||
|
||||
return resp_objs
|
||||
|
||||
|
||||
131
deepface/modules/preprocessing.py
Normal file
131
deepface/modules/preprocessing.py
Normal file
@ -0,0 +1,131 @@
|
||||
import os
|
||||
from typing import Union, Tuple
|
||||
import base64
|
||||
from pathlib import Path
|
||||
|
||||
# 3rd party
|
||||
import numpy as np
|
||||
import cv2
|
||||
from PIL import Image
|
||||
import requests
|
||||
|
||||
|
||||
def load_image(img: Union[str, np.ndarray]) -> Tuple[np.ndarray, str]:
|
||||
"""
|
||||
Load image from path, url, base64 or numpy array.
|
||||
Args:
|
||||
img: a path, url, base64 or numpy array.
|
||||
Returns:
|
||||
image (numpy array): the loaded image in BGR format
|
||||
image name (str): image name itself
|
||||
"""
|
||||
|
||||
# The image is already a numpy array
|
||||
if isinstance(img, np.ndarray):
|
||||
return img, "numpy array"
|
||||
|
||||
if isinstance(img, Path):
|
||||
img = str(img)
|
||||
|
||||
if not isinstance(img, str):
|
||||
raise ValueError(f"img must be numpy array or str but it is {type(img)}")
|
||||
|
||||
# The image is a base64 string
|
||||
if img.startswith("data:image/"):
|
||||
return load_base64(img), "base64 encoded string"
|
||||
|
||||
# The image is a url
|
||||
if img.startswith("http"):
|
||||
return (
|
||||
np.array(Image.open(requests.get(img, stream=True, timeout=60).raw).convert("BGR")),
|
||||
# return url as image name
|
||||
img,
|
||||
)
|
||||
|
||||
# The image is a path
|
||||
if os.path.isfile(img) is not True:
|
||||
raise ValueError(f"Confirm that {img} exists")
|
||||
|
||||
# image must be a file on the system then
|
||||
|
||||
# image name must have english characters
|
||||
if img.isascii() is False:
|
||||
raise ValueError(f"Input image must not have non-english characters - {img}")
|
||||
|
||||
img_obj_bgr = cv2.imread(img)
|
||||
# img_obj_rgb = cv2.cvtColor(img_obj_bgr, cv2.COLOR_BGR2RGB)
|
||||
return img_obj_bgr, img
|
||||
|
||||
|
||||
def load_base64(uri: str) -> np.ndarray:
|
||||
"""Load image from base64 string.
|
||||
|
||||
Args:
|
||||
uri: a base64 string.
|
||||
|
||||
Returns:
|
||||
numpy array: the loaded image.
|
||||
"""
|
||||
encoded_data = uri.split(",")[1]
|
||||
nparr = np.fromstring(base64.b64decode(encoded_data), np.uint8)
|
||||
img_bgr = cv2.imdecode(nparr, cv2.IMREAD_COLOR)
|
||||
# img_rgb = cv2.cvtColor(img_bgr, cv2.COLOR_BGR2RGB)
|
||||
return img_bgr
|
||||
|
||||
|
||||
def normalize_input(img: np.ndarray, normalization: str = "base") -> np.ndarray:
|
||||
"""Normalize input image.
|
||||
|
||||
Args:
|
||||
img (numpy array): the input image.
|
||||
normalization (str, optional): the normalization technique. Defaults to "base",
|
||||
for no normalization.
|
||||
|
||||
Returns:
|
||||
numpy array: the normalized image.
|
||||
"""
|
||||
|
||||
# issue 131 declares that some normalization techniques improves the accuracy
|
||||
|
||||
if normalization == "base":
|
||||
return img
|
||||
|
||||
# @trevorgribble and @davedgd contributed this feature
|
||||
# restore input in scale of [0, 255] because it was normalized in scale of
|
||||
# [0, 1] in preprocess_face
|
||||
img *= 255
|
||||
|
||||
if normalization == "raw":
|
||||
pass # return just restored pixels
|
||||
|
||||
elif normalization == "Facenet":
|
||||
mean, std = img.mean(), img.std()
|
||||
img = (img - mean) / std
|
||||
|
||||
elif normalization == "Facenet2018":
|
||||
# simply / 127.5 - 1 (similar to facenet 2018 model preprocessing step as @iamrishab posted)
|
||||
img /= 127.5
|
||||
img -= 1
|
||||
|
||||
elif normalization == "VGGFace":
|
||||
# mean subtraction based on VGGFace1 training data
|
||||
img[..., 0] -= 93.5940
|
||||
img[..., 1] -= 104.7624
|
||||
img[..., 2] -= 129.1863
|
||||
|
||||
elif normalization == "VGGFace2":
|
||||
# mean subtraction based on VGGFace2 training data
|
||||
img[..., 0] -= 91.4953
|
||||
img[..., 1] -= 103.8827
|
||||
img[..., 2] -= 131.0912
|
||||
|
||||
elif normalization == "ArcFace":
|
||||
# Reference study: The faces are cropped and resized to 112×112,
|
||||
# and each pixel (ranged between [0, 255]) in RGB images is normalised
|
||||
# by subtracting 127.5 then divided by 128.
|
||||
img -= 127.5
|
||||
img /= 128
|
||||
else:
|
||||
raise ValueError(f"unimplemented normalization type - {normalization}")
|
||||
|
||||
return img
|
||||
@ -12,7 +12,7 @@ from tqdm import tqdm
|
||||
# project dependencies
|
||||
from deepface.commons import functions, distance as dst
|
||||
from deepface.commons.logger import Logger
|
||||
from deepface.modules import representation
|
||||
from deepface.modules import representation, detection
|
||||
|
||||
logger = Logger(module="deepface/modules/recognition.py")
|
||||
|
||||
@ -202,8 +202,8 @@ def find(
|
||||
)
|
||||
|
||||
# img path might have more than once face
|
||||
source_objs = functions.extract_faces(
|
||||
img=img_path,
|
||||
source_objs = detection.extract_faces(
|
||||
img_path=img_path,
|
||||
target_size=target_size,
|
||||
detector_backend=detector_backend,
|
||||
grayscale=False,
|
||||
@ -213,7 +213,9 @@ def find(
|
||||
|
||||
resp_obj = []
|
||||
|
||||
for source_img, source_region, _ in source_objs:
|
||||
for source_obj in source_objs:
|
||||
source_img = source_obj["face"]
|
||||
source_region = source_obj["facial_area"]
|
||||
target_embedding_obj = representation.represent(
|
||||
img_path=source_img,
|
||||
model_name=model_name,
|
||||
@ -333,8 +335,8 @@ def __find_bulk_embeddings(
|
||||
desc="Finding representations",
|
||||
disable=silent,
|
||||
):
|
||||
img_objs = functions.extract_faces(
|
||||
img=employee,
|
||||
img_objs = detection.extract_faces(
|
||||
img_path=employee,
|
||||
target_size=target_size,
|
||||
detector_backend=detector_backend,
|
||||
grayscale=False,
|
||||
@ -342,7 +344,9 @@ def __find_bulk_embeddings(
|
||||
align=align,
|
||||
)
|
||||
|
||||
for img_content, img_region, _ in img_objs:
|
||||
for img_obj in img_objs:
|
||||
img_content = img_obj["face"]
|
||||
img_region = img_obj["facial_area"]
|
||||
embedding_obj = representation.represent(
|
||||
img_path=img_content,
|
||||
model_name=model_name,
|
||||
|
||||
@ -6,7 +6,7 @@ import numpy as np
|
||||
import cv2
|
||||
|
||||
# project dependencies
|
||||
from deepface.modules import modeling
|
||||
from deepface.modules import modeling, detection, preprocessing
|
||||
from deepface.commons import functions
|
||||
from deepface.models.FacialRecognition import FacialRecognition
|
||||
|
||||
@ -63,8 +63,8 @@ def represent(
|
||||
# we have run pre-process in verification. so, this can be skipped if it is coming from verify.
|
||||
target_size = functions.find_target_size(model_name=model_name)
|
||||
if detector_backend != "skip":
|
||||
img_objs = functions.extract_faces(
|
||||
img=img_path,
|
||||
img_objs = detection.extract_faces(
|
||||
img_path=img_path,
|
||||
target_size=(target_size[1], target_size[0]),
|
||||
detector_backend=detector_backend,
|
||||
grayscale=False,
|
||||
@ -73,7 +73,7 @@ def represent(
|
||||
)
|
||||
else: # skip
|
||||
# Try load. If load error, will raise exception internal
|
||||
img, _ = functions.load_image(img_path)
|
||||
img, _ = preprocessing.load_image(img_path)
|
||||
# --------------------------------
|
||||
if len(img.shape) == 4:
|
||||
img = img[0] # e.g. (1, 224, 224, 3) to (224, 224, 3)
|
||||
@ -85,13 +85,21 @@ def represent(
|
||||
img = (img.astype(np.float32) / 255.0).astype(np.float32)
|
||||
# --------------------------------
|
||||
# make dummy region and confidence to keep compatibility with `extract_faces`
|
||||
img_region = {"x": 0, "y": 0, "w": img.shape[1], "h": img.shape[2]}
|
||||
img_objs = [(img, img_region, 0)]
|
||||
img_objs = [
|
||||
{
|
||||
"face": img,
|
||||
"facial_area": {"x": 0, "y": 0, "w": img.shape[1], "h": img.shape[2]},
|
||||
"confidence": 0,
|
||||
}
|
||||
]
|
||||
# ---------------------------------
|
||||
|
||||
for img, region, confidence in img_objs:
|
||||
for img_obj in img_objs:
|
||||
img = img_obj["face"]
|
||||
region = img_obj["facial_area"]
|
||||
confidence = img_obj["confidence"]
|
||||
# custom normalization
|
||||
img = functions.normalize_input(img=img, normalization=normalization)
|
||||
img = preprocessing.normalize_input(img=img, normalization=normalization)
|
||||
|
||||
embedding = model.find_embeddings(img)
|
||||
|
||||
|
||||
@ -7,7 +7,7 @@ import numpy as np
|
||||
|
||||
# project dependencies
|
||||
from deepface.commons import functions, distance as dst
|
||||
from deepface.modules import representation
|
||||
from deepface.modules import representation, detection
|
||||
|
||||
|
||||
def verify(
|
||||
@ -82,8 +82,8 @@ def verify(
|
||||
target_size = functions.find_target_size(model_name=model_name)
|
||||
|
||||
# img pairs might have many faces
|
||||
img1_objs = functions.extract_faces(
|
||||
img=img1_path,
|
||||
img1_objs = detection.extract_faces(
|
||||
img_path=img1_path,
|
||||
target_size=target_size,
|
||||
detector_backend=detector_backend,
|
||||
grayscale=False,
|
||||
@ -91,8 +91,8 @@ def verify(
|
||||
align=align,
|
||||
)
|
||||
|
||||
img2_objs = functions.extract_faces(
|
||||
img=img2_path,
|
||||
img2_objs = detection.extract_faces(
|
||||
img_path=img2_path,
|
||||
target_size=target_size,
|
||||
detector_backend=detector_backend,
|
||||
grayscale=False,
|
||||
@ -103,8 +103,12 @@ def verify(
|
||||
distances = []
|
||||
regions = []
|
||||
# now we will find the face pair with minimum distance
|
||||
for img1_content, img1_region, _ in img1_objs:
|
||||
for img2_content, img2_region, _ in img2_objs:
|
||||
for img1_obj in img1_objs:
|
||||
img1_content = img1_obj["face"]
|
||||
img1_region = img1_obj["facial_area"]
|
||||
for img2_obj in img2_objs:
|
||||
img2_content = img2_obj["face"]
|
||||
img2_region = img2_obj["facial_area"]
|
||||
img1_embedding_obj = representation.represent(
|
||||
img_path=img1_content,
|
||||
model_name=model_name,
|
||||
|
||||
@ -1,12 +1,14 @@
|
||||
import numpy as np
|
||||
import pytest
|
||||
from deepface import DeepFace
|
||||
from deepface.commons.logger import Logger
|
||||
|
||||
logger = Logger("tests/test_extract_faces.py")
|
||||
|
||||
|
||||
def test_different_detectors():
|
||||
detectors = ["opencv", "mtcnn"]
|
||||
|
||||
|
||||
def test_different_detectors():
|
||||
for detector in detectors:
|
||||
img_objs = DeepFace.extract_faces(img_path="dataset/img11.jpg", detector_backend=detector)
|
||||
for img_obj in img_objs:
|
||||
@ -22,3 +24,21 @@ def test_different_detectors():
|
||||
img = img_obj["face"]
|
||||
assert img.shape[0] > 0 and img.shape[1] > 0
|
||||
logger.info(f"✅ extract_faces for {detector} backend test is done")
|
||||
|
||||
|
||||
def test_backends_for_enforced_detection_with_non_facial_inputs():
|
||||
black_img = np.zeros([224, 224, 3])
|
||||
for detector in detectors:
|
||||
with pytest.raises(ValueError):
|
||||
_ = DeepFace.extract_faces(img_path=black_img, detector_backend=detector)
|
||||
logger.info("✅ extract_faces for enforced detection and non-facial image test is done")
|
||||
|
||||
|
||||
def test_backends_for_not_enforced_detection_with_non_facial_inputs():
|
||||
black_img = np.zeros([224, 224, 3])
|
||||
for detector in detectors:
|
||||
objs = DeepFace.extract_faces(
|
||||
img_path=black_img, detector_backend=detector, enforce_detection=False
|
||||
)
|
||||
assert objs[0]["face"].shape == (224, 224, 3)
|
||||
logger.info("✅ extract_faces for not enforced detection and non-facial image test is done")
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user