mirror of
https://github.com/serengil/deepface.git
synced 2025-06-06 19:45:21 +00:00
Merge branch 'master' of https://github.com/dakotah-jones/deepface into patch-1
This commit is contained in:
dakotah-jones
commit
27d82ff00b
@ -405,23 +405,27 @@ If you do like this work, then you can support it financially on [Patreon](https
|
||||
<img src="https://raw.githubusercontent.com/serengil/deepface/master/icon/patreon.png" width="30%" height="30%">
|
||||
</a>
|
||||
|
||||
<a href="https://github.com/sponsors/serengil">
|
||||
<img src="https://raw.githubusercontent.com/serengil/deepface/refs/heads/master/icon/github_sponsor_button.png" width="37%" height="37%">
|
||||
</a>
|
||||
|
||||
<a href="https://buymeacoffee.com/serengil">
|
||||
<img src="https://raw.githubusercontent.com/serengil/deepface/master/icon/bmc-button.png" width="25%" height="25%">
|
||||
</a>
|
||||
|
||||
<!--
|
||||
Additionally, you can help us reach a wider audience by upvoting our posts on Hacker News and Product Hunt.
|
||||
|
||||
<div style="display: flex; align-items: center; gap: 10px;">
|
||||
<!-- Hacker News Badge -->
|
||||
<a href="https://news.ycombinator.com/item?id=42584896">
|
||||
<img src="https://hackerbadge.vercel.app/api?id=42584896&type=orange" style="width: 250px; height: 54px;" width="250" alt="Featured on Hacker News">
|
||||
</a>
|
||||
|
||||
<!-- Product Hunt Badge -->
|
||||
<a href="https://www.producthunt.com/posts/deepface?embed=true&utm_source=badge-featured&utm_medium=badge&utm_souce=badge-deepface" target="_blank">
|
||||
<img src="https://api.producthunt.com/widgets/embed-image/v1/featured.svg?post_id=753599&theme=light" alt="DeepFace - A Lightweight Deep Face Recognition Library for Python | Product Hunt" style="width: 250px; height: 54px;" width="250" height="54" />
|
||||
</a>
|
||||
</div>
|
||||
-->
|
||||
|
||||
## Citation
|
||||
|
||||
|
||||
@ -2,7 +2,7 @@
|
||||
import os
|
||||
import warnings
|
||||
import logging
|
||||
from typing import Any, Dict, IO, List, Union, Optional
|
||||
from typing import Any, Dict, IO, List, Union, Optional, Sequence
|
||||
|
||||
# this has to be set before importing tensorflow
|
||||
os.environ["TF_USE_LEGACY_KERAS"] = "1"
|
||||
@ -174,7 +174,7 @@ def analyze(
|
||||
expand_percentage: int = 0,
|
||||
silent: bool = False,
|
||||
anti_spoofing: bool = False,
|
||||
) -> List[Dict[str, Any]]:
|
||||
) -> Union[List[Dict[str, Any]], List[List[Dict[str, Any]]]]:
|
||||
"""
|
||||
Analyze facial attributes such as age, gender, emotion, and race in the provided image.
|
||||
Args:
|
||||
@ -206,7 +206,10 @@ def analyze(
|
||||
anti_spoofing (boolean): Flag to enable anti spoofing (default is False).
|
||||
|
||||
Returns:
|
||||
results (List[Dict[str, Any]]): A list of dictionaries, where each dictionary represents
|
||||
(List[List[Dict[str, Any]]]): A list of analysis results if received batched image,
|
||||
explained below.
|
||||
|
||||
(List[Dict[str, Any]]): A list of dictionaries, where each dictionary represents
|
||||
the analysis results for a detected face. Each dictionary in the list contains the
|
||||
following keys:
|
||||
|
||||
@ -373,7 +376,7 @@ def find(
|
||||
|
||||
|
||||
def represent(
|
||||
img_path: Union[str, np.ndarray, IO[bytes]],
|
||||
img_path: Union[str, np.ndarray, IO[bytes], Sequence[Union[str, np.ndarray, IO[bytes]]]],
|
||||
model_name: str = "VGG-Face",
|
||||
enforce_detection: bool = True,
|
||||
detector_backend: str = "opencv",
|
||||
@ -382,15 +385,18 @@ def represent(
|
||||
normalization: str = "base",
|
||||
anti_spoofing: bool = False,
|
||||
max_faces: Optional[int] = None,
|
||||
) -> List[Dict[str, Any]]:
|
||||
) -> Union[List[Dict[str, Any]], List[List[Dict[str, Any]]]]:
|
||||
"""
|
||||
Represent facial images as multi-dimensional vector embeddings.
|
||||
|
||||
Args:
|
||||
img_path (str or np.ndarray or IO[bytes]): The exact path to the image, a numpy array
|
||||
img_path (str, np.ndarray, IO[bytes], or Sequence[Union[str, np.ndarray, IO[bytes]]]):
|
||||
The exact path to the image, a numpy array
|
||||
in BGR format, a file object that supports at least `.read` and is opened in binary
|
||||
mode, or a base64 encoded image. If the source image contains multiple faces,
|
||||
the result will include information for each detected face.
|
||||
the result will include information for each detected face. If a sequence is provided,
|
||||
each element should be a string or numpy array representing an image, and the function
|
||||
will process images in batch.
|
||||
|
||||
model_name (str): Model for face recognition. Options: VGG-Face, Facenet, Facenet512,
|
||||
OpenFace, DeepFace, DeepID, Dlib, ArcFace, SFace and GhostFaceNet
|
||||
@ -417,8 +423,9 @@ def represent(
|
||||
max_faces (int): Set a limit on the number of faces to be processed (default is None).
|
||||
|
||||
Returns:
|
||||
results (List[Dict[str, Any]]): A list of dictionaries, each containing the
|
||||
following fields:
|
||||
results (List[Dict[str, Any]] or List[Dict[str, Any]]): A list of dictionaries.
|
||||
Result type becomes List of List of Dict if batch input passed.
|
||||
Each containing the following fields:
|
||||
|
||||
- embedding (List[float]): Multidimensional vector representing facial features.
|
||||
The number of dimensions varies based on the reference model
|
||||
|
||||
@ -1,4 +1,4 @@
|
||||
from typing import Union
|
||||
from typing import Union, List
|
||||
from abc import ABC, abstractmethod
|
||||
import numpy as np
|
||||
from deepface.commons import package_utils
|
||||
@ -18,5 +18,51 @@ class Demography(ABC):
|
||||
model_name: str
|
||||
|
||||
@abstractmethod
|
||||
def predict(self, img: np.ndarray) -> Union[np.ndarray, np.float64]:
|
||||
def predict(self, img: Union[np.ndarray, List[np.ndarray]]) -> Union[np.ndarray, np.float64]:
|
||||
pass
|
||||
|
||||
def _predict_internal(self, img_batch: np.ndarray) -> np.ndarray:
|
||||
"""
|
||||
Predict for single image or batched images.
|
||||
This method uses legacy method while receiving single image as input.
|
||||
And switch to batch prediction if receives batched images.
|
||||
|
||||
Args:
|
||||
img_batch:
|
||||
Batch of images as np.ndarray (n, x, y, c)
|
||||
with n >= 1, x = image width, y = image height, c = channel
|
||||
Or Single image as np.ndarray (1, x, y, c)
|
||||
with x = image width, y = image height and c = channel
|
||||
The channel dimension will be 1 if input is grayscale. (For emotion model)
|
||||
"""
|
||||
if not self.model_name: # Check if called from derived class
|
||||
raise NotImplementedError("no model selected")
|
||||
assert img_batch.ndim == 4, "expected 4-dimensional tensor input"
|
||||
|
||||
if img_batch.shape[0] == 1: # Single image
|
||||
# Predict with legacy method.
|
||||
return self.model(img_batch, training=False).numpy()[0, :]
|
||||
|
||||
# Batch of images
|
||||
# Predict with batch prediction
|
||||
return self.model.predict_on_batch(img_batch)
|
||||
|
||||
def _preprocess_batch_or_single_input(
|
||||
self, img: Union[np.ndarray, List[np.ndarray]]
|
||||
) -> np.ndarray:
|
||||
"""
|
||||
Preprocess single or batch of images, return as 4-D numpy array.
|
||||
Args:
|
||||
img: Single image as np.ndarray (224, 224, 3) or
|
||||
List of images as List[np.ndarray] or
|
||||
Batch of images as np.ndarray (n, 224, 224, 3)
|
||||
Returns:
|
||||
Four-dimensional numpy array (n, 224, 224, 3)
|
||||
"""
|
||||
image_batch = np.array(img)
|
||||
|
||||
# Check input dimension
|
||||
if len(image_batch.shape) == 3:
|
||||
# Single image - add batch dimension
|
||||
image_batch = np.expand_dims(image_batch, axis=0)
|
||||
return image_batch
|
||||
|
||||
@ -18,7 +18,7 @@ class FacialRecognition(ABC):
|
||||
input_shape: Tuple[int, int]
|
||||
output_shape: int
|
||||
|
||||
def forward(self, img: np.ndarray) -> List[float]:
|
||||
def forward(self, img: np.ndarray) -> Union[List[float], List[List[float]]]:
|
||||
if not isinstance(self.model, Model):
|
||||
raise ValueError(
|
||||
"You must overwrite forward method if it is not a keras model,"
|
||||
@ -26,4 +26,9 @@ class FacialRecognition(ABC):
|
||||
)
|
||||
# model.predict causes memory issue when it is called in a for loop
|
||||
# embedding = model.predict(img, verbose=0)[0].tolist()
|
||||
return self.model(img, training=False).numpy()[0].tolist()
|
||||
if img.shape == 4 and img.shape[0] == 1:
|
||||
img = img[0]
|
||||
embeddings = self.model(img, training=False).numpy()
|
||||
if embeddings.shape[0] == 1:
|
||||
return embeddings[0].tolist()
|
||||
return embeddings.tolist()
|
||||
|
||||
@ -1,3 +1,7 @@
|
||||
# stdlib dependencies
|
||||
|
||||
from typing import List, Union
|
||||
|
||||
# 3rd party dependencies
|
||||
import numpy as np
|
||||
|
||||
@ -9,7 +13,6 @@ from deepface.commons.logger import Logger
|
||||
|
||||
logger = Logger()
|
||||
|
||||
# ----------------------------------------
|
||||
# dependency configurations
|
||||
|
||||
tf_version = package_utils.get_tf_major_version()
|
||||
@ -21,12 +24,11 @@ else:
|
||||
from tensorflow.keras.models import Model, Sequential
|
||||
from tensorflow.keras.layers import Convolution2D, Flatten, Activation
|
||||
|
||||
# ----------------------------------------
|
||||
|
||||
WEIGHTS_URL = (
|
||||
"https://github.com/serengil/deepface_models/releases/download/v1.0/age_model_weights.h5"
|
||||
)
|
||||
|
||||
|
||||
# pylint: disable=too-few-public-methods
|
||||
class ApparentAgeClient(Demography):
|
||||
"""
|
||||
@ -37,11 +39,28 @@ class ApparentAgeClient(Demography):
|
||||
self.model = load_model()
|
||||
self.model_name = "Age"
|
||||
|
||||
def predict(self, img: np.ndarray) -> np.float64:
|
||||
# model.predict causes memory issue when it is called in a for loop
|
||||
# age_predictions = self.model.predict(img, verbose=0)[0, :]
|
||||
age_predictions = self.model(img, training=False).numpy()[0, :]
|
||||
return find_apparent_age(age_predictions)
|
||||
def predict(self, img: Union[np.ndarray, List[np.ndarray]]) -> Union[np.float64, np.ndarray]:
|
||||
"""
|
||||
Predict apparent age(s) for single or multiple faces
|
||||
Args:
|
||||
img: Single image as np.ndarray (224, 224, 3) or
|
||||
List of images as List[np.ndarray] or
|
||||
Batch of images as np.ndarray (n, 224, 224, 3)
|
||||
Returns:
|
||||
np.ndarray (age_classes,) if single image,
|
||||
np.ndarray (n, age_classes) if batched images.
|
||||
"""
|
||||
# Preprocessing input image or image list.
|
||||
imgs = self._preprocess_batch_or_single_input(img)
|
||||
|
||||
# Prediction from 3 channels image
|
||||
age_predictions = self._predict_internal(imgs)
|
||||
|
||||
# Calculate apparent ages
|
||||
if len(age_predictions.shape) == 1: # Single prediction list
|
||||
return find_apparent_age(age_predictions)
|
||||
|
||||
return np.array([find_apparent_age(age_prediction) for age_prediction in age_predictions])
|
||||
|
||||
|
||||
def load_model(
|
||||
@ -65,7 +84,7 @@ def load_model(
|
||||
|
||||
# --------------------------
|
||||
|
||||
age_model = Model(inputs=model.input, outputs=base_model_output)
|
||||
age_model = Model(inputs=model.inputs, outputs=base_model_output)
|
||||
|
||||
# --------------------------
|
||||
|
||||
@ -83,10 +102,14 @@ def find_apparent_age(age_predictions: np.ndarray) -> np.float64:
|
||||
"""
|
||||
Find apparent age prediction from a given probas of ages
|
||||
Args:
|
||||
age_predictions (?)
|
||||
age_predictions (age_classes,)
|
||||
Returns:
|
||||
apparent_age (float)
|
||||
"""
|
||||
assert (
|
||||
len(age_predictions.shape) == 1
|
||||
), f"Input should be a list of predictions, \
|
||||
not batched. Got shape: {age_predictions.shape}"
|
||||
output_indexes = np.arange(0, 101)
|
||||
apparent_age = np.sum(age_predictions * output_indexes)
|
||||
return apparent_age
|
||||
|
||||
@ -1,3 +1,6 @@
|
||||
# stdlib dependencies
|
||||
from typing import List, Union
|
||||
|
||||
# 3rd party dependencies
|
||||
import numpy as np
|
||||
import cv2
|
||||
@ -43,16 +46,38 @@ class EmotionClient(Demography):
|
||||
self.model = load_model()
|
||||
self.model_name = "Emotion"
|
||||
|
||||
def predict(self, img: np.ndarray) -> np.ndarray:
|
||||
img_gray = cv2.cvtColor(img[0], cv2.COLOR_BGR2GRAY)
|
||||
def _preprocess_image(self, img: np.ndarray) -> np.ndarray:
|
||||
"""
|
||||
Preprocess single image for emotion detection
|
||||
Args:
|
||||
img: Input image (224, 224, 3)
|
||||
Returns:
|
||||
Preprocessed grayscale image (48, 48)
|
||||
"""
|
||||
img_gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
|
||||
img_gray = cv2.resize(img_gray, (48, 48))
|
||||
img_gray = np.expand_dims(img_gray, axis=0)
|
||||
return img_gray
|
||||
|
||||
# model.predict causes memory issue when it is called in a for loop
|
||||
# emotion_predictions = self.model.predict(img_gray, verbose=0)[0, :]
|
||||
emotion_predictions = self.model(img_gray, training=False).numpy()[0, :]
|
||||
def predict(self, img: Union[np.ndarray, List[np.ndarray]]) -> np.ndarray:
|
||||
"""
|
||||
Predict emotion probabilities for single or multiple faces
|
||||
Args:
|
||||
img: Single image as np.ndarray (224, 224, 3) or
|
||||
List of images as List[np.ndarray] or
|
||||
Batch of images as np.ndarray (n, 224, 224, 3)
|
||||
Returns:
|
||||
np.ndarray (n, n_emotions)
|
||||
where n_emotions is the number of emotion categories
|
||||
"""
|
||||
# Preprocessing input image or image list.
|
||||
imgs = self._preprocess_batch_or_single_input(img)
|
||||
|
||||
return emotion_predictions
|
||||
processed_imgs = np.expand_dims(np.array([self._preprocess_image(img) for img in imgs]), axis=-1)
|
||||
|
||||
# Prediction
|
||||
predictions = self._predict_internal(processed_imgs)
|
||||
|
||||
return predictions
|
||||
|
||||
|
||||
def load_model(
|
||||
|
||||
@ -1,3 +1,7 @@
|
||||
# stdlib dependencies
|
||||
|
||||
from typing import List, Union
|
||||
|
||||
# 3rd party dependencies
|
||||
import numpy as np
|
||||
|
||||
@ -37,11 +41,23 @@ class GenderClient(Demography):
|
||||
self.model = load_model()
|
||||
self.model_name = "Gender"
|
||||
|
||||
def predict(self, img: np.ndarray) -> np.ndarray:
|
||||
# model.predict causes memory issue when it is called in a for loop
|
||||
# return self.model.predict(img, verbose=0)[0, :]
|
||||
return self.model(img, training=False).numpy()[0, :]
|
||||
def predict(self, img: Union[np.ndarray, List[np.ndarray]]) -> np.ndarray:
|
||||
"""
|
||||
Predict gender probabilities for single or multiple faces
|
||||
Args:
|
||||
img: Single image as np.ndarray (224, 224, 3) or
|
||||
List of images as List[np.ndarray] or
|
||||
Batch of images as np.ndarray (n, 224, 224, 3)
|
||||
Returns:
|
||||
np.ndarray (n, 2)
|
||||
"""
|
||||
# Preprocessing input image or image list.
|
||||
imgs = self._preprocess_batch_or_single_input(img)
|
||||
|
||||
# Prediction
|
||||
predictions = self._predict_internal(imgs)
|
||||
|
||||
return predictions
|
||||
|
||||
def load_model(
|
||||
url=WEIGHTS_URL,
|
||||
@ -64,7 +80,7 @@ def load_model(
|
||||
|
||||
# --------------------------
|
||||
|
||||
gender_model = Model(inputs=model.input, outputs=base_model_output)
|
||||
gender_model = Model(inputs=model.inputs, outputs=base_model_output)
|
||||
|
||||
# --------------------------
|
||||
|
||||
|
||||
@ -1,3 +1,6 @@
|
||||
# stdlib dependencies
|
||||
from typing import List, Union
|
||||
|
||||
# 3rd party dependencies
|
||||
import numpy as np
|
||||
|
||||
@ -37,10 +40,24 @@ class RaceClient(Demography):
|
||||
self.model = load_model()
|
||||
self.model_name = "Race"
|
||||
|
||||
def predict(self, img: np.ndarray) -> np.ndarray:
|
||||
# model.predict causes memory issue when it is called in a for loop
|
||||
# return self.model.predict(img, verbose=0)[0, :]
|
||||
return self.model(img, training=False).numpy()[0, :]
|
||||
def predict(self, img: Union[np.ndarray, List[np.ndarray]]) -> np.ndarray:
|
||||
"""
|
||||
Predict race probabilities for single or multiple faces
|
||||
Args:
|
||||
img: Single image as np.ndarray (224, 224, 3) or
|
||||
List of images as List[np.ndarray] or
|
||||
Batch of images as np.ndarray (n, 224, 224, 3)
|
||||
Returns:
|
||||
np.ndarray (n, n_races)
|
||||
where n_races is the number of race categories
|
||||
"""
|
||||
# Preprocessing input image or image list.
|
||||
imgs = self._preprocess_batch_or_single_input(img)
|
||||
|
||||
# Prediction
|
||||
predictions = self._predict_internal(imgs)
|
||||
|
||||
return predictions
|
||||
|
||||
|
||||
def load_model(
|
||||
@ -62,7 +79,7 @@ def load_model(
|
||||
|
||||
# --------------------------
|
||||
|
||||
race_model = Model(inputs=model.input, outputs=base_model_output)
|
||||
race_model = Model(inputs=model.inputs, outputs=base_model_output)
|
||||
|
||||
# --------------------------
|
||||
|
||||
|
||||
@ -1,5 +1,5 @@
|
||||
# built-in dependencies
|
||||
from typing import List
|
||||
from typing import List, Union
|
||||
|
||||
# 3rd party dependencies
|
||||
import numpy as np
|
||||
@ -26,24 +26,22 @@ class DlibClient(FacialRecognition):
|
||||
self.input_shape = (150, 150)
|
||||
self.output_shape = 128
|
||||
|
||||
def forward(self, img: np.ndarray) -> List[float]:
|
||||
def forward(self, img: np.ndarray) -> Union[List[float], List[List[float]]]:
|
||||
"""
|
||||
Find embeddings with Dlib model.
|
||||
This model necessitates the override of the forward method
|
||||
because it is not a keras model.
|
||||
Args:
|
||||
img (np.ndarray): pre-loaded image in BGR
|
||||
img (np.ndarray): pre-loaded image(s) in BGR
|
||||
Returns
|
||||
embeddings (list): multi-dimensional vector
|
||||
embeddings (list of lists or list of floats): multi-dimensional vectors
|
||||
"""
|
||||
# return self.model.predict(img)[0].tolist()
|
||||
|
||||
# extract_faces returns 4 dimensional images
|
||||
if len(img.shape) == 4:
|
||||
img = img[0]
|
||||
# Handle single image case
|
||||
if len(img.shape) == 3:
|
||||
img = np.expand_dims(img, axis=0)
|
||||
|
||||
# bgr to rgb
|
||||
img = img[:, :, ::-1] # bgr to rgb
|
||||
img = img[:, :, :, ::-1] # bgr to rgb
|
||||
|
||||
# img is in scale of [0, 1] but expected [0, 255]
|
||||
if img.max() <= 1:
|
||||
@ -51,10 +49,11 @@ class DlibClient(FacialRecognition):
|
||||
|
||||
img = img.astype(np.uint8)
|
||||
|
||||
img_representation = self.model.model.compute_face_descriptor(img)
|
||||
img_representation = np.array(img_representation)
|
||||
img_representation = np.expand_dims(img_representation, axis=0)
|
||||
return img_representation[0].tolist()
|
||||
embeddings = self.model.model.compute_face_descriptor(img)
|
||||
embeddings = [np.array(embedding).tolist() for embedding in embeddings]
|
||||
if len(embeddings) == 1:
|
||||
return embeddings[0]
|
||||
return embeddings
|
||||
|
||||
|
||||
class DlibResNet:
|
||||
|
||||
@ -1,5 +1,5 @@
|
||||
# built-in dependencies
|
||||
from typing import Any, List
|
||||
from typing import Any, List, Union
|
||||
|
||||
# 3rd party dependencies
|
||||
import numpy as np
|
||||
@ -27,7 +27,7 @@ class SFaceClient(FacialRecognition):
|
||||
self.input_shape = (112, 112)
|
||||
self.output_shape = 128
|
||||
|
||||
def forward(self, img: np.ndarray) -> List[float]:
|
||||
def forward(self, img: np.ndarray) -> Union[List[float], List[List[float]]]:
|
||||
"""
|
||||
Find embeddings with SFace model
|
||||
This model necessitates the override of the forward method
|
||||
@ -37,14 +37,17 @@ class SFaceClient(FacialRecognition):
|
||||
Returns
|
||||
embeddings (list): multi-dimensional vector
|
||||
"""
|
||||
# return self.model.predict(img)[0].tolist()
|
||||
input_blob = (img * 255).astype(np.uint8)
|
||||
|
||||
# revert the image to original format and preprocess using the model
|
||||
input_blob = (img[0] * 255).astype(np.uint8)
|
||||
embeddings = []
|
||||
for i in range(input_blob.shape[0]):
|
||||
embedding = self.model.model.feature(input_blob[i])
|
||||
embeddings.append(embedding)
|
||||
embeddings = np.concatenate(embeddings, axis=0)
|
||||
|
||||
embeddings = self.model.model.feature(input_blob)
|
||||
|
||||
return embeddings[0].tolist()
|
||||
if embeddings.shape[0] == 1:
|
||||
return embeddings[0].tolist()
|
||||
return embeddings.tolist()
|
||||
|
||||
|
||||
def load_model(
|
||||
|
||||
@ -57,8 +57,7 @@ class VggFaceClient(FacialRecognition):
|
||||
def forward(self, img: np.ndarray) -> List[float]:
|
||||
"""
|
||||
Generates embeddings using the VGG-Face model.
|
||||
This method incorporates an additional normalization layer,
|
||||
necessitating the override of the forward method.
|
||||
This method incorporates an additional normalization layer.
|
||||
|
||||
Args:
|
||||
img (np.ndarray): pre-loaded image in BGR
|
||||
@ -70,8 +69,14 @@ class VggFaceClient(FacialRecognition):
|
||||
|
||||
# having normalization layer in descriptor troubles for some gpu users (e.g. issue 957, 966)
|
||||
# instead we are now calculating it with traditional way not with keras backend
|
||||
embedding = self.model(img, training=False).numpy()[0].tolist()
|
||||
embedding = verification.l2_normalize(embedding)
|
||||
embedding = super().forward(img)
|
||||
if (
|
||||
isinstance(embedding, list) and
|
||||
isinstance(embedding[0], list)
|
||||
):
|
||||
embedding = verification.l2_normalize(embedding, axis=1)
|
||||
else:
|
||||
embedding = verification.l2_normalize(embedding)
|
||||
return embedding.tolist()
|
||||
|
||||
|
||||
|
||||
@ -100,6 +100,29 @@ def analyze(
|
||||
- 'white': Confidence score for White ethnicity.
|
||||
"""
|
||||
|
||||
if isinstance(img_path, np.ndarray) and len(img_path.shape) == 4:
|
||||
# Received 4-D array, which means image batch.
|
||||
# Check batch dimension and process each image separately.
|
||||
if img_path.shape[0] > 1:
|
||||
batch_resp_obj = []
|
||||
# Execute analysis for each image in the batch.
|
||||
for single_img in img_path:
|
||||
# Call the analyze function for each image in the batch.
|
||||
resp_obj = analyze(
|
||||
img_path=single_img,
|
||||
actions=actions,
|
||||
enforce_detection=enforce_detection,
|
||||
detector_backend=detector_backend,
|
||||
align=align,
|
||||
expand_percentage=expand_percentage,
|
||||
silent=silent,
|
||||
anti_spoofing=anti_spoofing,
|
||||
)
|
||||
|
||||
# Append the response object to the batch response list.
|
||||
batch_resp_obj.append(resp_obj)
|
||||
return batch_resp_obj
|
||||
|
||||
# if actions is passed as tuple with single item, interestingly it becomes str here
|
||||
if isinstance(actions, str):
|
||||
actions = (actions,)
|
||||
|
||||
@ -1,5 +1,5 @@
|
||||
# built-in dependencies
|
||||
from typing import Any, Dict, List, Union, Optional
|
||||
from typing import Any, Dict, List, Union, Optional, Sequence, IO
|
||||
|
||||
# 3rd party dependencies
|
||||
import numpy as np
|
||||
@ -11,7 +11,7 @@ from deepface.models.FacialRecognition import FacialRecognition
|
||||
|
||||
|
||||
def represent(
|
||||
img_path: Union[str, np.ndarray],
|
||||
img_path: Union[str, IO[bytes], np.ndarray, Sequence[Union[str, np.ndarray, IO[bytes]]]],
|
||||
model_name: str = "VGG-Face",
|
||||
enforce_detection: bool = True,
|
||||
detector_backend: str = "opencv",
|
||||
@ -20,14 +20,16 @@ def represent(
|
||||
normalization: str = "base",
|
||||
anti_spoofing: bool = False,
|
||||
max_faces: Optional[int] = None,
|
||||
) -> List[Dict[str, Any]]:
|
||||
) -> Union[List[Dict[str, Any]], List[List[Dict[str, Any]]]]:
|
||||
"""
|
||||
Represent facial images as multi-dimensional vector embeddings.
|
||||
|
||||
Args:
|
||||
img_path (str or np.ndarray): The exact path to the image, a numpy array in BGR format,
|
||||
or a base64 encoded image. If the source image contains multiple faces, the result will
|
||||
include information for each detected face.
|
||||
img_path (str, np.ndarray, or Sequence[Union[str, np.ndarray]]):
|
||||
The exact path to the image, a numpy array in BGR format,
|
||||
a base64 encoded image, or a sequence of these.
|
||||
If the source image contains multiple faces,
|
||||
the result will include information for each detected face.
|
||||
|
||||
model_name (str): Model for face recognition. Options: VGG-Face, Facenet, Facenet512,
|
||||
OpenFace, DeepFace, DeepID, Dlib, ArcFace, SFace and GhostFaceNet
|
||||
@ -51,8 +53,9 @@ def represent(
|
||||
max_faces (int): Set a limit on the number of faces to be processed (default is None).
|
||||
|
||||
Returns:
|
||||
results (List[Dict[str, Any]]): A list of dictionaries, each containing the
|
||||
following fields:
|
||||
results (List[Dict[str, Any]] or List[Dict[str, Any]]): A list of dictionaries.
|
||||
Result type becomes List of List of Dict if batch input passed.
|
||||
Each containing the following fields:
|
||||
|
||||
- embedding (List[float]): Multidimensional vector representing facial features.
|
||||
The number of dimensions varies based on the reference model
|
||||
@ -70,80 +73,105 @@ def represent(
|
||||
task="facial_recognition", model_name=model_name
|
||||
)
|
||||
|
||||
# ---------------------------------
|
||||
# we have run pre-process in verification. so, this can be skipped if it is coming from verify.
|
||||
target_size = model.input_shape
|
||||
if detector_backend != "skip":
|
||||
# Images are returned in RGB format.
|
||||
img_objs = detection.extract_faces(
|
||||
img_path=img_path,
|
||||
detector_backend=detector_backend,
|
||||
grayscale=False,
|
||||
enforce_detection=enforce_detection,
|
||||
align=align,
|
||||
expand_percentage=expand_percentage,
|
||||
anti_spoofing=anti_spoofing,
|
||||
max_faces=max_faces,
|
||||
)
|
||||
else: # skip
|
||||
# Try load. If load error, will raise exception internal
|
||||
img, _ = image_utils.load_image(img_path)
|
||||
# Handle list of image paths or 4D numpy array
|
||||
if isinstance(img_path, list):
|
||||
images = img_path
|
||||
elif isinstance(img_path, np.ndarray) and img_path.ndim == 4:
|
||||
images = [img_path[i] for i in range(img_path.shape[0])]
|
||||
else:
|
||||
images = [img_path]
|
||||
|
||||
if len(img.shape) != 3:
|
||||
raise ValueError(f"Input img must be 3 dimensional but it is {img.shape}")
|
||||
batch_images, batch_regions, batch_confidences, batch_indexes = [], [], [], []
|
||||
|
||||
# Convert to RGB format to keep compatability with `extract_faces`.
|
||||
img = img[:, :, ::-1]
|
||||
for idx, single_img_path in enumerate(images):
|
||||
# we have run pre-process in verification. so, skip if it is coming from verify.
|
||||
target_size = model.input_shape
|
||||
if detector_backend != "skip":
|
||||
# Images are returned in RGB format.
|
||||
img_objs = detection.extract_faces(
|
||||
img_path=single_img_path,
|
||||
detector_backend=detector_backend,
|
||||
grayscale=False,
|
||||
enforce_detection=enforce_detection,
|
||||
align=align,
|
||||
expand_percentage=expand_percentage,
|
||||
anti_spoofing=anti_spoofing,
|
||||
max_faces=max_faces,
|
||||
)
|
||||
else: # skip
|
||||
# Try load. If load error, will raise exception internal
|
||||
img, _ = image_utils.load_image(single_img_path)
|
||||
|
||||
# make dummy region and confidence to keep compatibility with `extract_faces`
|
||||
img_objs = [
|
||||
{
|
||||
"face": img,
|
||||
"facial_area": {"x": 0, "y": 0, "w": img.shape[0], "h": img.shape[1]},
|
||||
"confidence": 0,
|
||||
}
|
||||
]
|
||||
# ---------------------------------
|
||||
if len(img.shape) != 3:
|
||||
raise ValueError(f"Input img must be 3 dimensional but it is {img.shape}")
|
||||
|
||||
if max_faces is not None and max_faces < len(img_objs):
|
||||
# sort as largest facial areas come first
|
||||
img_objs = sorted(
|
||||
img_objs,
|
||||
key=lambda img_obj: img_obj["facial_area"]["w"] * img_obj["facial_area"]["h"],
|
||||
reverse=True,
|
||||
)
|
||||
# discard rest of the items
|
||||
img_objs = img_objs[0:max_faces]
|
||||
# Convert to RGB format to keep compatability with `extract_faces`.
|
||||
img = img[:, :, ::-1]
|
||||
|
||||
for img_obj in img_objs:
|
||||
if anti_spoofing is True and img_obj.get("is_real", True) is False:
|
||||
raise ValueError("Spoof detected in the given image.")
|
||||
img = img_obj["face"]
|
||||
# make dummy region and confidence to keep compatibility with `extract_faces`
|
||||
img_objs = [
|
||||
{
|
||||
"face": img,
|
||||
"facial_area": {"x": 0, "y": 0, "w": img.shape[0], "h": img.shape[1]},
|
||||
"confidence": 0,
|
||||
}
|
||||
]
|
||||
# ---------------------------------
|
||||
|
||||
# rgb to bgr
|
||||
img = img[:, :, ::-1]
|
||||
if max_faces is not None and max_faces < len(img_objs):
|
||||
# sort as largest facial areas come first
|
||||
img_objs = sorted(
|
||||
img_objs,
|
||||
key=lambda img_obj: img_obj["facial_area"]["w"] * img_obj["facial_area"]["h"],
|
||||
reverse=True,
|
||||
)
|
||||
# discard rest of the items
|
||||
img_objs = img_objs[0:max_faces]
|
||||
|
||||
region = img_obj["facial_area"]
|
||||
confidence = img_obj["confidence"]
|
||||
for img_obj in img_objs:
|
||||
if anti_spoofing is True and img_obj.get("is_real", True) is False:
|
||||
raise ValueError("Spoof detected in the given image.")
|
||||
|
||||
# resize to expected shape of ml model
|
||||
img = preprocessing.resize_image(
|
||||
img=img,
|
||||
# thanks to DeepId (!)
|
||||
target_size=(target_size[1], target_size[0]),
|
||||
)
|
||||
img = img_obj["face"]
|
||||
|
||||
# custom normalization
|
||||
img = preprocessing.normalize_input(img=img, normalization=normalization)
|
||||
# rgb to bgr
|
||||
img = img[:, :, ::-1]
|
||||
|
||||
embedding = model.forward(img)
|
||||
region = img_obj["facial_area"]
|
||||
confidence = img_obj["confidence"]
|
||||
|
||||
resp_objs.append(
|
||||
{
|
||||
"embedding": embedding,
|
||||
"facial_area": region,
|
||||
"face_confidence": confidence,
|
||||
}
|
||||
)
|
||||
# resize to expected shape of ml model
|
||||
img = preprocessing.resize_image(
|
||||
img=img,
|
||||
# thanks to DeepId (!)
|
||||
target_size=(target_size[1], target_size[0]),
|
||||
)
|
||||
|
||||
return resp_objs
|
||||
# custom normalization
|
||||
img = preprocessing.normalize_input(img=img, normalization=normalization)
|
||||
|
||||
batch_images.append(img)
|
||||
batch_regions.append(region)
|
||||
batch_confidences.append(confidence)
|
||||
batch_indexes.append(idx)
|
||||
|
||||
# Convert list of images to a numpy array for batch processing
|
||||
batch_images = np.concatenate(batch_images, axis=0)
|
||||
|
||||
# Forward pass through the model for the entire batch
|
||||
embeddings = model.forward(batch_images)
|
||||
|
||||
for idx in range(0, len(images)):
|
||||
resp_obj = []
|
||||
for idy, batch_index in enumerate(batch_indexes):
|
||||
if idx == batch_index:
|
||||
resp_obj.append(
|
||||
{
|
||||
"embedding": embeddings if len(batch_images) == 1 else embeddings[idy],
|
||||
"facial_area": batch_regions[idy],
|
||||
"face_confidence": batch_confidences[idy],
|
||||
}
|
||||
)
|
||||
resp_objs.append(resp_obj)
|
||||
|
||||
return resp_objs[0] if len(images) == 1 else resp_objs
|
||||
|
||||
BIN
icon/github_sponsor_button.png
Normal file
BIN
icon/github_sponsor_button.png
Normal file
Binary file not shown.
|
After Width: | Height: | Size: 7.9 KiB |
@ -1,8 +1,10 @@
|
||||
# 3rd party dependencies
|
||||
import cv2
|
||||
import numpy as np
|
||||
|
||||
# project dependencies
|
||||
from deepface import DeepFace
|
||||
from deepface.models.demography import Age, Emotion, Gender, Race
|
||||
from deepface.commons.logger import Logger
|
||||
|
||||
logger = Logger()
|
||||
@ -16,6 +18,7 @@ def test_standard_analyze():
|
||||
demography_objs = DeepFace.analyze(img, silent=True)
|
||||
for demography in demography_objs:
|
||||
logger.debug(demography)
|
||||
assert type(demography) == dict
|
||||
assert demography["age"] > 20 and demography["age"] < 40
|
||||
assert demography["dominant_gender"] == "Woman"
|
||||
logger.info("✅ test standard analyze done")
|
||||
@ -29,6 +32,7 @@ def test_analyze_with_all_actions_as_tuple():
|
||||
|
||||
for demography in demography_objs:
|
||||
logger.debug(f"Demography: {demography}")
|
||||
assert type(demography) == dict
|
||||
age = demography["age"]
|
||||
gender = demography["dominant_gender"]
|
||||
race = demography["dominant_race"]
|
||||
@ -53,6 +57,7 @@ def test_analyze_with_all_actions_as_list():
|
||||
|
||||
for demography in demography_objs:
|
||||
logger.debug(f"Demography: {demography}")
|
||||
assert type(demography) == dict
|
||||
age = demography["age"]
|
||||
gender = demography["dominant_gender"]
|
||||
race = demography["dominant_race"]
|
||||
@ -74,6 +79,7 @@ def test_analyze_for_some_actions():
|
||||
demography_objs = DeepFace.analyze(img, ["age", "gender"], silent=True)
|
||||
|
||||
for demography in demography_objs:
|
||||
assert type(demography) == dict
|
||||
age = demography["age"]
|
||||
gender = demography["dominant_gender"]
|
||||
|
||||
@ -95,6 +101,7 @@ def test_analyze_for_preloaded_image():
|
||||
resp_objs = DeepFace.analyze(img, silent=True)
|
||||
for resp_obj in resp_objs:
|
||||
logger.debug(resp_obj)
|
||||
assert type(resp_obj) == dict
|
||||
assert resp_obj["age"] > 20 and resp_obj["age"] < 40
|
||||
assert resp_obj["dominant_gender"] == "Woman"
|
||||
|
||||
@ -131,7 +138,89 @@ def test_analyze_for_different_detectors():
|
||||
]
|
||||
|
||||
# validate probabilities
|
||||
assert type(result) == dict
|
||||
if result["dominant_gender"] == "Man":
|
||||
assert result["gender"]["Man"] > result["gender"]["Woman"]
|
||||
else:
|
||||
assert result["gender"]["Man"] < result["gender"]["Woman"]
|
||||
|
||||
|
||||
def test_analyze_for_numpy_batched_image():
|
||||
img1_path = "dataset/img4.jpg"
|
||||
img2_path = "dataset/couple.jpg"
|
||||
|
||||
# Copy and combine the same image to create multiple faces
|
||||
img1 = cv2.imread(img1_path)
|
||||
img2 = cv2.imread(img2_path)
|
||||
|
||||
expected_num_faces = [1, 2]
|
||||
|
||||
img1 = cv2.resize(img1, (500, 500))
|
||||
img2 = cv2.resize(img2, (500, 500))
|
||||
|
||||
img = np.stack([img1, img2])
|
||||
assert len(img.shape) == 4 # Check dimension.
|
||||
assert img.shape[0] == 2 # Check batch size.
|
||||
|
||||
demography_batch = DeepFace.analyze(img, silent=True)
|
||||
# 2 image in batch, so 2 demography objects.
|
||||
assert len(demography_batch) == 2
|
||||
|
||||
for i, demography_objs in enumerate(demography_batch):
|
||||
|
||||
assert len(demography_objs) == expected_num_faces[i]
|
||||
for demography in demography_objs: # Iterate over faces
|
||||
assert isinstance(demography, dict) # Check type
|
||||
assert demography["age"] > 20 and demography["age"] < 40
|
||||
assert demography["dominant_gender"] in ["Woman", "Man"]
|
||||
|
||||
logger.info("✅ test analyze for multiple faces done")
|
||||
|
||||
|
||||
def test_batch_detect_age_for_multiple_faces():
|
||||
# Load test image and resize to model input size
|
||||
img = cv2.resize(cv2.imread("dataset/img1.jpg"), (224, 224))
|
||||
imgs = [img, img]
|
||||
results = Age.ApparentAgeClient().predict(imgs)
|
||||
# Check there are two ages detected
|
||||
assert len(results) == 2
|
||||
# Check two faces ages are the same in integer format（e.g. 23.6 -> 23）
|
||||
# Must use int() to compare because of max float precision issue in different platforms
|
||||
assert np.array_equal(int(results[0]), int(results[1]))
|
||||
logger.info("✅ test batch detect age for multiple faces done")
|
||||
|
||||
|
||||
def test_batch_detect_emotion_for_multiple_faces():
|
||||
# Load test image and resize to model input size
|
||||
img = cv2.resize(cv2.imread("dataset/img1.jpg"), (224, 224))
|
||||
imgs = [img, img]
|
||||
results = Emotion.EmotionClient().predict(imgs)
|
||||
# Check there are two emotions detected
|
||||
assert len(results) == 2
|
||||
# Check two faces emotions are the same
|
||||
assert np.array_equal(results[0], results[1])
|
||||
logger.info("✅ test batch detect emotion for multiple faces done")
|
||||
|
||||
|
||||
def test_batch_detect_gender_for_multiple_faces():
|
||||
# Load test image and resize to model input size
|
||||
img = cv2.resize(cv2.imread("dataset/img1.jpg"), (224, 224))
|
||||
imgs = [img, img]
|
||||
results = Gender.GenderClient().predict(imgs)
|
||||
# Check there are two genders detected
|
||||
assert len(results) == 2
|
||||
# Check two genders are the same
|
||||
assert np.array_equal(results[0], results[1])
|
||||
logger.info("✅ test batch detect gender for multiple faces done")
|
||||
|
||||
|
||||
def test_batch_detect_race_for_multiple_faces():
|
||||
# Load test image and resize to model input size
|
||||
img = cv2.resize(cv2.imread("dataset/img1.jpg"), (224, 224))
|
||||
imgs = [img, img]
|
||||
results = Race.RaceClient().predict(imgs)
|
||||
# Check there are two races detected
|
||||
assert len(results) == 2
|
||||
# Check two races are the same
|
||||
assert np.array_equal(results[0], results[1])
|
||||
logger.info("✅ test batch detect race for multiple faces done")
|
||||
|
||||
@ -2,6 +2,8 @@
|
||||
import io
|
||||
import cv2
|
||||
import pytest
|
||||
import numpy as np
|
||||
import pytest
|
||||
|
||||
# project dependencies
|
||||
from deepface import DeepFace
|
||||
@ -13,7 +15,12 @@ logger = Logger()
|
||||
def test_standard_represent():
|
||||
img_path = "dataset/img1.jpg"
|
||||
embedding_objs = DeepFace.represent(img_path)
|
||||
# type should be list of dict
|
||||
assert isinstance(embedding_objs, list)
|
||||
|
||||
for embedding_obj in embedding_objs:
|
||||
assert isinstance(embedding_obj, dict)
|
||||
|
||||
embedding = embedding_obj["embedding"]
|
||||
logger.debug(f"Function returned {len(embedding)} dimensional vector")
|
||||
assert len(embedding) == 4096
|
||||
@ -23,18 +30,18 @@ def test_standard_represent():
|
||||
def test_standard_represent_with_io_object():
|
||||
img_path = "dataset/img1.jpg"
|
||||
default_embedding_objs = DeepFace.represent(img_path)
|
||||
io_embedding_objs = DeepFace.represent(open(img_path, 'rb'))
|
||||
io_embedding_objs = DeepFace.represent(open(img_path, "rb"))
|
||||
assert default_embedding_objs == io_embedding_objs
|
||||
|
||||
# Confirm non-seekable io objects are handled properly
|
||||
io_obj = io.BytesIO(open(img_path, 'rb').read())
|
||||
io_obj = io.BytesIO(open(img_path, "rb").read())
|
||||
io_obj.seek = None
|
||||
no_seek_io_embedding_objs = DeepFace.represent(io_obj)
|
||||
assert default_embedding_objs == no_seek_io_embedding_objs
|
||||
|
||||
# Confirm non-image io objects raise exceptions
|
||||
with pytest.raises(ValueError, match='Failed to decode image'):
|
||||
DeepFace.represent(io.BytesIO(open(r'../requirements.txt', 'rb').read()))
|
||||
with pytest.raises(ValueError, match="Failed to decode image"):
|
||||
DeepFace.represent(io.BytesIO(open(r"../requirements.txt", "rb").read()))
|
||||
|
||||
logger.info("✅ test standard represent with io object function done")
|
||||
|
||||
@ -55,6 +62,27 @@ def test_represent_for_skipped_detector_backend_with_image_path():
|
||||
logger.info("✅ test represent function for skipped detector and image path input backend done")
|
||||
|
||||
|
||||
def test_represent_for_preloaded_image():
|
||||
face_img = "dataset/img5.jpg"
|
||||
img = cv2.imread(face_img)
|
||||
img_objs = DeepFace.represent(img_path=img)
|
||||
# type should be list of dict
|
||||
assert isinstance(img_objs, list)
|
||||
assert len(img_objs) >= 1
|
||||
|
||||
for img_obj in img_objs:
|
||||
assert isinstance(img_obj, dict)
|
||||
assert "embedding" in img_obj.keys()
|
||||
assert "facial_area" in img_obj.keys()
|
||||
assert isinstance(img_obj["facial_area"], dict)
|
||||
assert "x" in img_obj["facial_area"].keys()
|
||||
assert "y" in img_obj["facial_area"].keys()
|
||||
assert "w" in img_obj["facial_area"].keys()
|
||||
assert "h" in img_obj["facial_area"].keys()
|
||||
assert "face_confidence" in img_obj.keys()
|
||||
logger.info("✅ test represent function for skipped detector and preloaded image done")
|
||||
|
||||
|
||||
def test_represent_for_skipped_detector_backend_with_preloaded_image():
|
||||
face_img = "dataset/img5.jpg"
|
||||
img = cv2.imread(face_img)
|
||||
@ -84,12 +112,6 @@ def test_max_faces():
|
||||
|
||||
|
||||
def test_represent_detector_backend():
|
||||
"""
|
||||
There shouldn't be a difference between:
|
||||
- Using a detector backend provided by `represent`
|
||||
- Manually calling a detector backend, then calling `represent`.
|
||||
"""
|
||||
|
||||
# Results using a detection backend.
|
||||
results_1 = DeepFace.represent(img_path="dataset/img1.jpg")
|
||||
assert len(results_1) == 1
|
||||
@ -108,3 +130,108 @@ def test_represent_detector_backend():
|
||||
embedding_2 = results_2[0]['embedding']
|
||||
assert embedding_1 == embedding_2
|
||||
logger.info("✅ test represent function for consistent output.")
|
||||
|
||||
|
||||
@pytest.mark.parametrize(
|
||||
"model_name",
|
||||
[
|
||||
"VGG-Face",
|
||||
"Facenet",
|
||||
"SFace",
|
||||
],
|
||||
)
|
||||
def test_batched_represent_for_list_input(model_name):
|
||||
img_paths = [
|
||||
"dataset/img1.jpg",
|
||||
"dataset/img2.jpg",
|
||||
"dataset/img3.jpg",
|
||||
"dataset/img4.jpg",
|
||||
"dataset/img5.jpg",
|
||||
"dataset/couple.jpg",
|
||||
]
|
||||
|
||||
expected_faces = [1, 1, 1, 1, 1, 2]
|
||||
|
||||
batched_embedding_objs = DeepFace.represent(img_path=img_paths, model_name=model_name)
|
||||
|
||||
# type should be list of list of dict for batch input
|
||||
assert isinstance(batched_embedding_objs, list)
|
||||
|
||||
assert len(batched_embedding_objs) == len(
|
||||
img_paths
|
||||
), f"Expected {len(img_paths)} embeddings, got {len(batched_embedding_objs)}"
|
||||
|
||||
# the last one has two faces
|
||||
for idx, embedding_objs in enumerate(batched_embedding_objs):
|
||||
# type should be list of list of dict for batch input
|
||||
# batched_embedding_objs was list already, embedding_objs should be list of dict
|
||||
assert isinstance(embedding_objs, list)
|
||||
for embedding_obj in embedding_objs:
|
||||
assert isinstance(embedding_obj, dict)
|
||||
|
||||
assert expected_faces[idx] == len(
|
||||
embedding_objs
|
||||
), f"{img_paths[idx]} has {expected_faces[idx]} faces, but got {len(embedding_objs)} embeddings!"
|
||||
|
||||
for idx, img_path in enumerate(img_paths):
|
||||
single_embedding_objs = DeepFace.represent(img_path=img_path, model_name=model_name)
|
||||
# type should be list of dict for single input
|
||||
assert isinstance(single_embedding_objs, list)
|
||||
for embedding_obj in single_embedding_objs:
|
||||
assert isinstance(embedding_obj, dict)
|
||||
|
||||
assert len(single_embedding_objs) == len(batched_embedding_objs[idx])
|
||||
|
||||
for alpha, beta in zip(single_embedding_objs, batched_embedding_objs[idx]):
|
||||
assert np.allclose(
|
||||
alpha["embedding"], beta["embedding"], rtol=1e-2, atol=1e-2
|
||||
), "Embeddings do not match within tolerance"
|
||||
|
||||
logger.info(f"✅ test batch represent function with string input for model {model_name} done")
|
||||
|
||||
|
||||
@pytest.mark.parametrize(
|
||||
"model_name",
|
||||
[
|
||||
"VGG-Face",
|
||||
"Facenet",
|
||||
"SFace",
|
||||
],
|
||||
)
|
||||
def test_batched_represent_for_numpy_input(model_name):
|
||||
img_paths = [
|
||||
"dataset/img1.jpg",
|
||||
"dataset/img2.jpg",
|
||||
"dataset/img3.jpg",
|
||||
"dataset/img4.jpg",
|
||||
"dataset/img5.jpg",
|
||||
"dataset/couple.jpg",
|
||||
]
|
||||
expected_faces = [1, 1, 1, 1, 1, 2]
|
||||
|
||||
imgs = []
|
||||
for img_path in img_paths:
|
||||
img = cv2.imread(img_path)
|
||||
img = cv2.resize(img, (1000, 1000))
|
||||
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
|
||||
# print(img.shape)
|
||||
imgs.append(img)
|
||||
|
||||
imgs = np.array(imgs)
|
||||
assert imgs.ndim == 4 and imgs.shape[0] == len(img_paths)
|
||||
|
||||
batched_embedding_objs = DeepFace.represent(img_path=imgs, model_name=model_name)
|
||||
|
||||
# type should be list of list of dict for batch input
|
||||
assert isinstance(batched_embedding_objs, list)
|
||||
for idx, batched_embedding_obj in enumerate(batched_embedding_objs):
|
||||
assert isinstance(batched_embedding_obj, list)
|
||||
# it also has to have the expected number of faces
|
||||
assert len(batched_embedding_obj) == expected_faces[idx]
|
||||
for embedding_obj in batched_embedding_obj:
|
||||
assert isinstance(embedding_obj, dict)
|
||||
|
||||
# we should have the same number of embeddings as the number of images
|
||||
assert len(batched_embedding_objs) == len(img_paths)
|
||||
|
||||
logger.info(f"✅ test batch represent function with numpy input for model {model_name} done")
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user