Welcome to Trueface’s documentation!¶
trueface.age |
Age module |
trueface.db |
DB Module |
trueface.face_attributes |
module to handle face attributes like emotion |
trueface.heartbeat |
Heartbeat detector |
trueface.helper |
helper methods |
trueface.motion |
Motion Detector module |
trueface.object_detection |
Object Detection Module |
trueface.recognition |
Recognition Module |
trueface.reid_tracker |
Re-Id module |
trueface.searching |
Search Module |
trueface.spoof |
Spoof detection module |
trueface.tracking |
Tracking module |
trueface.utils |
utility methods |
trueface.video |
Video module |
Age module
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class
trueface.age.
AgeDetector
(model_path=None, params_path=None, license=None, ctx='cpu', set_weights=True)¶ AgeDetector class
Parameters: - model_path – path to the age model
- params_path – path to params file
- license – your license key
- ctx – “gpu” or “cpu”
- set_weights – True or false
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predict
(chip)¶ predict the age of the chip
Parameters: chip – image chip Returns: predicted age
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set_weights
()¶ Set weights
DB Module
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class
trueface.db.
Collection
(**kwargs)¶ A simple constructor that allows initialization from kwargs.
Sets attributes on the constructed instance using the names and values in
kwargs
.Only keys that are present as attributes of the instance’s class are allowed. These could be, for example, any mapped columns or relationships.
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class
trueface.db.
DBService
(user='root', password='aez5Buem', host='127.0.0.1', port=3306, db_name='trueface_tests')¶ DBService class
Constructor for DBService class :param user: default: root :type user: str :param password: default: aez5Buem :type password: str :param host: mysql connect string, default:”127.0.0.1” :type host: str :param port: default: 3306 :type port: int :param db_name: default: “trueface_tests” :type db_name: str
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create_collection
(name, features=None, labels=None, imgpaths=None)¶ Create a new collection in the database :param name: table name of the new collection :type name: str :param features: list of features :type features: list :param labels: corresponding list of labels :type labels: list
Returns: labels labels: labels Return type: features
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delete_from_collection
(collection_name, label)¶ Remove a label and the corresponding feature from a collection :param collection_name: table name of an existing collection :type collection_name: str :param labels: list of labels to remove :type labels: list
Returns: nothing
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insert_into_collection
(name, labels, features)¶ Insert features and labels into an existing collection :param name: name of the collection, this will become the table name :type name: str :param labels: list of labels :type labels: list :param features: list of corresponding features :type features: list
Returns: nothing
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class
trueface.db.
Detection
(**kwargs)¶ A simple constructor that allows initialization from kwargs.
Sets attributes on the constructed instance using the names and values in
kwargs
.Only keys that are present as attributes of the instance’s class are allowed. These could be, for example, any mapped columns or relationships.
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class
trueface.db.
Frame
(**kwargs)¶ A simple constructor that allows initialization from kwargs.
Sets attributes on the constructed instance using the names and values in
kwargs
.Only keys that are present as attributes of the instance’s class are allowed. These could be, for example, any mapped columns or relationships.
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class
trueface.db.
Video
(**kwargs)¶ A simple constructor that allows initialization from kwargs.
Sets attributes on the constructed instance using the names and values in
kwargs
.Only keys that are present as attributes of the instance’s class are allowed. These could be, for example, any mapped columns or relationships.
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class
trueface.db.
VideoPart
(**kwargs)¶ A simple constructor that allows initialization from kwargs.
Sets attributes on the constructed instance using the names and values in
kwargs
.Only keys that are present as attributes of the instance’s class are allowed. These could be, for example, any mapped columns or relationships.
module to handle face attributes like emotion
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class
trueface.face_attributes.
FaceAttributes
(model, params, labels)¶ Face attributes class
init emotion class
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get_attributes
(chip)¶ gets emotion for a face
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Heartbeat detector
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class
trueface.heartbeat.
HeartbeatDetector
(buffer_size=250)¶ Heartbeat detector class
helper methods
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trueface.helper.
adjust_input
(in_data)¶ - adjust the input from (h, w, c) to ( 1, c, h, w) for network input
- in_data: numpy array of shape (h, w, c)
- input data
- out_data: numpy array of shape (1, c, h, w)
- reshaped array
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trueface.helper.
detect_first_stage
(img, net, scale, threshold)¶ - run PNet for first stage
- img: numpy array, bgr order
- input image
- scale: float number
- how much should the input image scale
- net: PNet
- worker
total_boxes : bboxes
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trueface.helper.
generate_bbox
(map, reg, scale, threshold)¶ generate bbox from feature map
- map: numpy array , n x m x 1
- detect score for each position
- reg: numpy array , n x m x 4
- bbox
- scale: float number
- scale of this detection
- threshold: float number
- detect threshold
bbox array
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trueface.helper.
nms
(boxes, overlap_threshold, mode='Union')¶ non max suppression
- box: numpy array n x 5
- input bbox array
- overlap_threshold: float number
- threshold of overlap
- mode: float number
- how to compute overlap ratio, ‘Union’ or ‘Min’
index array of the selected bbox
Motion Detector module
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class
trueface.motion.
MotionDetector
(frame, threshold=2, max_value=2, frames=100)¶ MotionDetector class
Object Detection Module
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class
trueface.object_detection.
ObjectRecognizer
(ctx='cpu', model_path=None, params_path=None, classes=None, license=None, conf_threshold=0.5, nms_threshold=0.4, dims=(None, None))¶ TF Local Object Detector
Parameters: - ctx – ‘cpu’ or ‘gpu’
- model_path – path to model
- params_path – path to params file
- license – your license token
- method – ‘ssd’ or ‘yolo’
- conf_threshold – set the conf_threshold
- nms_threshold – set the nms_threshold
- dims –
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batch_predict
(images)¶ Detect object
Parameters: images – Returns: list of results
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compute_resize_scale
(image_shape, min_side=512, max_side=700)¶ Compute an image scale such that the image size is constrained to min_side and max_side. :param min_side: The image’s min side will be equal to min_side after resizing. :param max_side: If after resizing the image’s max side is above max_side, resize until the max side is equal to max_side.
Returns: A resizing scale.
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predict
(image)¶ Detect object
Parameters: image – Returns: list of results
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resize_image
(img, min_side=512, max_side=700)¶ Resize an image such that the size is constrained to min_side and max_side. :param min_side: The image’s min side will be equal to min_side after resizing. :param max_side: If after resizing the image’s max side is above max_side, resize until the max side is equal to max_side.
Returns: A resized image.
Recognition Module
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class
trueface.recognition.
BaseRecognizer
(license, ctx='cpu', gpu=0)¶ BaseRecognizer class
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batch_make_mx_input
(images, dims=(512, 512))¶ Prepare list of images for MXNet input
Parameters: - images –
- dims –
Returns: list with mx.nd.array elements
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batch_preprocess_image
(images, dims=(512, 512))¶ Takes a list of images, preprocesses them (resize and BGR2RGB
Parameters: - images –
- dims –
Returns: list of preprocessed images
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blur_region
(region, frame)¶ blurs a region in the image
Parameters: - region – (leftx, topy, rightx, bottomy)
- frame –
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cosine_sim
(feature, collection_features, length=512)¶ Cosine Similarity with mxnet
Parameters: - feature – the source feature
- collection_features – a list of features
- length –
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draw_box
(img, box)¶ draws a box on the image
Parameters: - img (str or binary) – image path, base64 encoded image, numpy array or OpenCV image
- box – (pt1, pt2, pt3, pt4)
- pt1 – (x coordinate of vertex, y coordinate of vertex)
- pt2 – (x coordinate of point opposite vertex, y coordinate of vertex)
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draw_label
(image, point, label, font=0, font_scale=0.5, thickness=1)¶ Draw label on the image
Parameters: - img (str or binary) – image path, base64 encoded image, numpy array or OpenCV image
- point (tuple) – (x_label, y_label)
- label (str) – The label you want to write
- font (int) – your preferred font
- font_scale (float) – scaling factor for the font
- thickness (int) – thickness of text
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get_image
(_bin, rgb=True)¶ gets an image from a path or from base64 string
Parameters: - _bin (str) – filesystem path or base64 string
- rgb (bool) – whether to perform BGR2RGB conversion
Returns: a pre-handled image ready for further processing
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get_string_from_cv2
(image, encode=False)¶ Gets a string from a cv2 image
Parameters: - image –
- encode –
Returns: string representation of image
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make_cv_input
(image)¶ Create OpenCV blob from an image
Parameters: image – Returns: cv2.dnn.blob
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make_mx_input
(image, dims=(512, 512))¶ prepare image for MXNet input
Parameters: - image –
- dims –
Returns: mx.nd.array
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preprocess_image
(image, dims=(512, 512))¶ resize image to dims and perform a BGR2RGB conversion
Parameters: - image (opencv image) – Image
- dims – Dimensions
Returns:
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class
trueface.recognition.
ColorRecognizer
(n_clusters=10)¶ Color Recognizer class
Parameters: n_clusters – -
centroid_histogram
()¶ grab the number of different clusters and create a histogram based on the number of pixels assigned to each cluster
Returns: Return type: histogram
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detect
(img)¶ detect colors present in image
Parameters: img – the input image Returns: list of colors along with percentages
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class
trueface.recognition.
FaceRecognizer
(ctx='cpu', min_face=40, accurate_landmark=False, fd_model_path=None, fr_model_path=None, params_path=None, license=None, gpu=0)¶ TF Local Face Detector
Parameters: - ctx (str) – ‘cpu’ or ‘gpu’, memory and computation context
- min_face (float) – minimum size of a face in pixels
- accurate_landmark (bool) – use accurate landmark localization or not
- fd_model_path (str) – path to the face detect model. For example fd_model
- fr_model_path (str) – path to the face recognition model. For example model-lite/model.trueface
- params_path (str) – path to the params file. For example model-lite/model.params
- license (str) – your licsense token from creds.json
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static
average_features
(features_lists)¶ - return the mean of all features in the list
- This allows us to directly input a list of tracked objects where some of them will have a list of features and some will not
Parameters: features_lists – list of features lists or None Returns:
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batch_get_features
(image_array, batch_size=24, progress_bar=True)¶ - returns face features for a list of images
- this function does not perform any face detection but feeds the provided image directly to the model for batch feature extraction
Parameters: - image_array (list) –
- batch_size (int) –
- progress_bar (bool) –
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batch_identify
(chips, collection='collection.npz', features=None, labels=None, threshold=0.25, db=None, return_features=False, length=512, batch_size=24)¶ identify a batch of face chips by comparing it to a collection npz file, a list of features or a memsql table
Parameters: - chips (list) – a list of face chips. (opencv images or numpy arrays)
- collection (str) – path to a collection npz file or the name of the collection the database
- db (DBService) – DBService object or None if no database should be used
- features (list) – list of corresponding features as numpy arrays
- labels (list) – list of labels
- return_features (bool) – whether to return the extracted features
- threshold (float) – similarity threshold over which to call it a match
Returns: List of dictionaries with predicted_label and confidence as well as features if the return_features param was set to True
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create_collection
(name, folder=None, images=None, labels=None, return_features=False, batch_size=8, db=False, mp=False)¶ creates a collection from a folder or from a list of features with corresponding labels. Creates an npz file by default or will write the collection to DB if a DBService object is passed
Parameters: - name (str) – location for the generated npz collection file or the table name if a DBService object is being passed
- folder (str) – path to folder holding images
- images (list) – alternatively, you can pass a list of images with corresponding labels
- labels (list) – labels corresponding to the images list. You will have to pass both images and labels or neither
- db (DBService) – DBService object that connects to a database
- return_features (bool) – whether to return the extracted features
- batch_size (int) – size of the batch to break the passed list into for processing
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create_collection_arrays
(images, labels, batch_size)¶ creates a collection from two arrays of images and labels
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create_collection_directory
(folder, batch_size=8, mp=None)¶ Create a list of labels and corresponding features out of a directory
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delete_feature_from_collection
(collection_filename, index_in_collection)¶ Remove an individual face feature from a collection
Parameters: - collection_filename (str) – Path to an existing collection npz file
- feature (numpy array) – the feature as returend by identify for example
Returns: updated collection
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delete_from_collection
(collection_filename, labels)¶ Remove a label and the corresponding features from a collection
Parameters: - collection_filename (str) – Path to an existing collection npz file
- labels (list) – list of labels to delete
Returns: updated collection
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find_biggest_face
(img, return_chips=False, chip_size=112, padding=0.2, return_binary=False)¶ finds the biggest face in the image
Parameters: - img (image path, base64 encoded image, numpy array or OpenCV image) – image
- return_chips (bool) –
- chip_size (int) – size of face chip
- padding (float) –
- return_binary (bool) –
Returns: the detected box, points and chips if the return_binary param was set to True, or a json dict with the above data otherwise. The chip is only returned if the return_chips param was set to True
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find_faces
(img, return_chips=False, chip_size=112, padding=0.2, return_binary=False)¶ finds all faces and returns chips
Parameters: - img (image path, base64 encoded image, numpy array or OpenCV image) – image
- return_chips (bool) –
- chip_size (int) – size of face chip
- padding (float) –
- return_binary (bool) –
Returns: the detected box, points and chips if the return_binary param was set to True, or a json dict with the above data otherwise. The chip is only returned if the return_chips param was set to True
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get_features
(img, b64=False)¶ features of the biggest face found in the provided image
Parameters: - img (image path, base64 encoded image, numpy array or OpenCV image) – image
- b64 (bool) – whether to return embedding b64 encoded
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get_match
(score, threshold, use_sim)¶ Return true or false related to score and threshold
Parameters: - score –
- threshold –
- use_sim – return true if score >= threshold, if use_sim is false, return true if score <= threshold
Returns: boolean
Return type: match
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identify
(chip, collection=None, threshold=0.25, return_features=False, labels=None, features=None, length=512, db=False)¶ identify a face chip by comparing it to a collection npz file or database table
Parameters: - chip (opencv image or numpy array) – face chip (padded image array)
- collection (str) – path to a collection npz file
- features (list) – list of features, this gets overloaded if you pass a collection
- threshold (float) – similarity threshold over which to call it a match
- return_features (bool) – whether to return the features
- labels (bool) – whether to return the label
- db (DBService) – pass a DBService object if you want to use the DB backend to manage your collection
Returns: A dictionary with predicted_label and confidence as well as features if the return_features param was set to True
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impl_get_features
(img)¶ - returns face features for an image
- this function does not perform any face detection but feeds the provided image directly to the model for feature extraction
Parameters: img (opencv image or numpy array) – Returns: feature found in image
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match_two_features
(source, target, use_sim=False, threshold=1.5)¶ Match two features
Parameters: - source – base64 encoded source feature
- target – base64 encoded source feature
- use_sim – if use_sim = True, we recommend a threshold of 0.25
- threshold –
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match_two_images
(source, target, use_sim=False, threshold=1.5)¶ matches two images
Parameters: - source (path, base64, binary, OpenCV or nympy image) – source image to match
- target (path, base64, binary, OpenCV or nympy image) – target image to match
- use_sim (bool) – if use_sim = True, we recommend a threshold of 0.25
- threshold (float) –
Returns: JSON object with score, match and probability
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read_collection_dir_parallel
(folder)¶ Load an image collection from disk using all available CPUs
Parameters: folder (str) – path to collection folder Returns: list of extracted features labels (list): list of corresponding labels Return type: features (list)
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read_collection_dir_sequential
(folder)¶ Load an image collection from disk in sequence on one CPU
Parameters: folder (str) – path to collection folder Returns: list of extracted features labels (list): list of corresponding labels Return type: features (list)
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update_collection
(input_folder=None, collection_filename=None, return_features=False, features=None, labels=None, db=None)¶ Update a collection by adding features and labels
Parameters: - input_folder (str) – Path to a folder with updated images
- collection_filename (str) – Path to an existing collection npz file
- return_features (bool) – Whether to return features from input_folder
- features (list) – list of features
- labels (list) – corresponding list of labels
- db (DBService) – db object to write the collection to
Returns: json response with the path to the updated collection filename
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class
trueface.recognition.
ObjectRecognizer
(ctx='cpu', model_path=None, params_path=None, license=None, method='ssd', conf_threshold=0.5, nms_threshold=0.4, dims=(None, None))¶ TF Local Object Detector
Parameters: - ctx – ‘cpu’ or ‘gpu’
- model_path – path to model
- params_path – path to params file
- license – your license token
- method – ‘ssd’ or ‘yolo’
- conf_threshold – set the conf_threshold
- nms_threshold – set the nms_threshold
- dims –
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detect
(input, dims=None)¶ Detect object
Parameters: - input –
- dims –
Returns: list of results
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non_max_suppression
(boxes, confidences)¶ Remove the bounding boxes with low confidence using non-max suppression
Parameters: - boxes – list of boxes
- confidences – list of corresponding confidence scores
Returns:
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postprocess_mx_output
(outs, conf_threshold, nms_threshold=None, dims=None)¶ Scan through all the bounding boxes output from the network and keep only the ones with high confidence scores. Assign the box’s class label as the class with the highest score.
Parameters: - outs – list of outs
- conf_threshold – confidence threshold
- nms_threshold – NMS threshold
- dims – dimensions
Returns: filtered results
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trueface.recognition.
response
(message, data)¶ return a json object
Parameters: - message –
- data –
Returns: a json object containing a message and a data field
Re-Id module
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class
trueface.reid_tracker.
VideoTracker
(detector_weight_path=None, reid_weight_path=None, license=None, detector_threshold=0.5, detector_nms_threshold=0.4, max_dist=0.2, min_confidence=0.3, nms_overlap=0.5, iou_dist=0.7, max_age=9999, num_init=3, nn_budget=100, use_cuda=False)¶ Person re-identification tracker
Parameters: - detector_weight_path (str) – Path to detector (yolo) weights file. For example “~/yolo_v3.weights”.
- re-id_weight_path (str) – Path to reid model. For example “~/reid.t7”.
- license (str) – sdk license token given by Trueface team
- detector_threshold (float) – Score threshold for detector.
- detector_nms_threshold (float) – Non maximum suppression threshold for detections.
- max_dist (float) – The matching threshold. Detections with larger distance are considered an invalid match.
- min_confidence (float) – Min confidence for detections to be considered correct.
- nms_overlap (float) – ROIs that overlap more than this values are suppressed.
- iou_dist (float) – Gating threshold. Associations with cost larger than this value are disregarded.
- max_age (int) – Maximum number frames with missed detections before a track is deleted.
- num_init (int) – Number of consecutive detections before the track is confirmed. The track state is set to Deleted if a miss occurs within the first num_init frames.
- nn_budget (int) – Fix samples per class to at most this number. Removes the oldest samples when the budget is reached.
- use_cuda (bool) – Use cuda if set True and compatible gpu is available, otherwise use cpu.
Returns: Array of bounding boxes and associated identities, class confidences.
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draw_box
(image, bbox, identities)¶ Draw bounding box around detected people
Parameters: - image (float array) – source image
- bbox (list) – list of bounding boxes with format x1,y1,x2,y2
- identities (list) – list of identities associated with bounding boxes
Returns: Image with bounding box drawn around detected people
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run
(image)¶ Run person reid tracker
Parameters: image (float array) – Image to run tracker on Returns: Identities, associated bounding boxes and class confidences for detected people
Search Module
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class
trueface.searching.
VideoSearch
(index='index.npy', video='video.avi', output=None, out_filename='results.avi', save_photos=False, random_name_len=5, recognizer=None, similarity_threshold=0.25)¶ VideoSearch class
Spoof detection module
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class
trueface.spoof.
SpoofDetector
(model_path, params_path, token, ctx='cpu')¶ The spoof detector class
Parameters: - model_path – filesystem path to model
- params_path – filesystem path to params
- token – your token from creds.json
- ctx – the mxnet context, “cpu” or “gpu”
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is_spoof
(image, threshold)¶ Returns true or false wheter image is a spoof based on threshold
Parameters: - image (numpy array or str) – if image is a string it will be read as a filepath
- threshold (float) – threshold between 0 and 1 below which we call it a spoof
Returns: true or false whether this is a spoof image
Return type: bool
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spoof_probability
(image)¶ Returns probability that image passed is a spoof
Parameters: image (numpy array or str) – if image is a string it will be read as a filepath Returns: Probability this is a spoof from 0 to 1 Return type: float
Tracking module
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class
trueface.tracking.
BaseTracker
(threshold=10.0, min_feats=1, track_movements=0, max_steps=20)¶ BaseTracker class
Parameters: - threshold (float) – default 10, value between 0 and 30
- min_feats (integer) – minimum number of features to keep, used for multi frame inference
- track_movements (integer) – value between 0 and 1, records movement of objects
- max_steps (integer) – number of movements to keep in tracked object
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clean
()¶ clean tracked objects, removes idenities that fell below the threshold
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draw_motion_tracks
(frame)¶ draw motion tracks on frame :param frame: numpy/opencv frame to draw movements on
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find_tracked_object
(bbox, image)¶ Abstract method
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remove_unknown_identities
()¶ removes unknown identities that haven’t been identitied and that reached the max feature count
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track
(object_to_track, image, identity, chip=None, related_object=None)¶ Abstract method
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update
(bboxes, frame, chips=None, features=None)¶ Abstract method
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class
trueface.tracking.
COObjectTracker
(threshold, min_feats=1, track_movements=0, max_steps=20)¶ A tracker class utilizing a correlation based tracker :param threshold: default 10, value between 0 and 30 :type threshold: float :param min_feats: minimum number of features to keep, used for multi frame inference :type min_feats: integer :param track_movements: value between 0 and 1, records movement of objects :type track_movements: integer :param max_steps: number of movements to keep in tracked object :type max_steps: integer
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find_tracked_object
(obj, frame, track_movements=False, method=2, iou_threshold=0.6)¶ Matches passed obj to a tracked obj :param obj: rectangle containing tracked object to find from face detect call :param frame: opencv/numpy frame
Returns: quality measure that can be compared against the threshold matched_oid: id of matched object in the tracked object array bbox: bounding box of matched tracked object Return type: quality
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track
(object_to_track, image, identity=None, chip=None, features=None, related_object=None)¶ Initiates the tracking of a obj :param object_to_track: rectangle containing object to track :type object_to_track: list :param image: numpy or opencv image :param identity: identity label if available :param chip: extracted face chip, used in face averaging and multi frame inference :param features: face recognition features, used in multi frame inference :param related_object: related tracked object
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update
(bboxes, frame, chips=None, features=None)¶ updates tracked object returning known and unkown objects in the frame :param bounding boxes: :param frame: numpy/opencv frame :param chips: optional, chips to store in tracked :type chips: numpy/opencv image :param objects: :param features: optional, features to store in tracked object :type features: array
Returns: an array of known_objects and unknown_objects
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update_trackers
(frame)¶ update bounding box positions of trackers Args: frame: numpy/opencv frame
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class
trueface.tracking.
CVObjectTracker
(threshold, min_feats=1, track_movements=0, max_steps=20, tracker_type='KCF')¶ A Tracking class that exposes 8 Opencv Trackers
Parameters: - threshold (float) – default 10, value between 0 and 30
- min_feats (integer) – minimum number of features to keep, used for multi frame inference
- track_movements (integer) – value between 0 and 1, records movement of objects
- max_steps (integer) – number of movements to keep in tracked object
- tracker_type (str) – one of the following choices (BOOSTING, MIL, TLD, MEDIANFLOW, GOTURN, MOSSE, CSRT)
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find_tracked_object
(obj, frame)¶ Matches passed obj to a tracked obj :param object: bounding box of object to find :param frame: numpy of opencv frame
Returns: boolean matched_oid: id of matched object in the tracked object array bbox: bounding box of matched object Return type: found
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track
(object_to_track, image, identity, chip=None, features=None, related_object=None)¶ track object :param object_to_track: rectangle containing object to track :type object_to_track: list :param image: numpy or opencv image :param identity: identity label if avaliable :param chip: extracted face chip, used in face averaging and multi frame inference :param features: face recognition features, used in multi frame inference :param related_object: related tracked object
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update
(bboxes, frame, chips=[], features=[])¶ updates tracked object positions :param bounding boxes: :param frame: numpy/opencv frame :param chips: optional, chips to store in tracked objects :type chips: numpy/opencv image :param features: optional, features to store in tracked object :type features: array
Returns: an array of known_objects and unknown_objects
utility methods
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class
trueface.utils.
RedisQueue
(name, namespace='queue', **redis_kwargs)¶ Simple Queue with Redis Backend
The default connection parameters are: host=’localhost’, port=6379, db=0
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empty
()¶ Return True if the queue is empty, False otherwise.
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get
(block=True, timeout=None)¶ Remove and return an item from the queue.
If optional args block is true and timeout is None (the default), block if necessary until an item is available.
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get_nowait
()¶ Equivalent to get(False).
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put
(item)¶ Put item into the queue.
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qsize
()¶ Return the approximate size of the queue.
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trueface.utils.
bbox_to_rect
(bbox)¶ Convert a bounding box to a rectangle
Parameters: bbox – array with coordinates [top_left_x, top_left_y, width, height] Returns: array with coordinates [top_left_x, top_left_y, bottom_right_x, bottom_right_y] Return type: rectangle
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trueface.utils.
compute_color_for_labels
(label)¶ Simple function that adds fixed color depending on the class
Video module
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class
trueface.video.
QVideoStream
(src=0, queue_size=128)¶ QVideoStream class
initialize the file video stream along with the boolean used to indicate if the thread should be stopped or not
Parameters: - src –
- queue_size –
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start
()¶ start a thread to read frames from the file video stream :return: QVideoStream object
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update
()¶ read and update the video stream
Returns: loops infinitely