Tools¶

Performance
Image Manipulation
Results Serialization
HW Discovery
Analytics

Performance ¶

class FPS¶

Monitor the frames per second (FPS) of video streams for performance tracking.

Typical usage:

fps = edgeiq.FPS().start()
while True:
    <main processing loop>
    fps.update()

# Get the elapsed time and FPS
fps.stop()
print("Elapsed seconds: {}".format(fps.get_elapsed_seconds()))
print("FPS: {}".format(fps.compute_fps()))

compute_fps() may also be called in the main processing loop to compute an instantaneous estimate of the FPS.

start()¶: Start tracking FPS.

stop()¶: Stop tracking FPS.

update()¶

Increment the total number of frames examined during the start and end intervals.

Raises: RuntimeError

get_elapsed_seconds()¶

Return the total number of seconds between the start and end intervals.

Returns: float – The elapsed time in seconds between start and end, or since start if stop() has not been called.

compute_fps()¶

Compute the (approximate) frames per second.

Returns: float – the approximate frames per second.

class TimingProfiler¶

Time segments of processing and generate reports.

To time segments of a function:

def func():
    prof = edgeiq.TimingProfiler()
    for i in range(10):
        prof.mark_start('start-loop')
        ...
        prof.mark('finish-block-1')
        ...
        prof.mark('finish-block-2')
        ...
        prof.mark_end('end-loop')

        print(json.dumps(prof.durations, indent=2))
    print(json.dumps(prof.generate_report(), indent=2))

property durations¶

The durations of the last completed iteration.

The durations object has the form:

[
    {
        "segment": "<prev_mark>-><cur_mark>",
        "duration_s": <duration in seconds>
    },
    ...
]

Return type: List[dict]

mark_start(tag)¶

Mark the start of a timing profiling iteration.

This will reset the iteration memory and will complete when mark_end() is called.

Parameters: tag (str) – The tag to identify this event. Should be the same as other start events you’d like to compare this to.

mark(tag)¶

Mark a timing profiling event.

This must be called after mark_start() and will generate a timing event which compares to the previous event.

Parameters: tag (str) – The tag to identify this event. Should be the same as other start events you’d like to compare this to.

mark_end(tag)¶

Mark the end of a timing profiling iteration.

This clears the last durations attribute and generates new durations based on the marks for the last iteration.

Parameters: tag (str) – The tag to identify this event. Should be the same as other end events you’d like to compare this to.

generate_report()¶

Generate a report from the collected timing data.

The report has the form:

{
    "<segment 0>": {
        "mean": <mean>,
        "std_dev": <standard deviation>,
        "max": <max value>,
        "min": <min value>
    },
    ...
},

Return type: Dict[str, Dict[str, float]]

parse_cvat_annotations(path, start_frame=0, end_frame=None, new_id_for_occlusion=False)¶

Parse CVAT dumped annotations file to edgeIQ Predictions

Parameters

path (str) – The path of CVAT dumped ground truth annotations (.xml)
start_frame (int) – Starting Frame
end_frame (Optional[int]) – Ending Frame (default: None -> Parses all available frames)
new_id_for_occlusion (bool) – Associates a new object_id to predictions if occluded in previous frame

Returns

dict – Frame-By-Frame Data {frame_num: list of ObjectDetectionPrediction}

Return type

Tuple[dict, dict]

Returns

dict – Object-By-Object Data {object: {‘tracked_frames : list of frames in which object was tracked, ‘occluded_frames’ : list of frames in which object was occluded}}

Raises

FileNotFoundError if file doesn’t exist

Raises

ValueError if start frame is greater than end

class ModelPerformanceAnalyzer(ground_truth_path, start_frame=0, end_frame=None)¶

Get statistics to analyse the performance of models.

Typical usage:

analyzer = edgeiq.ModelPerformanceAnalyzer(ground_truth_path = 'annotations.xml')
model_results = edgeiq.load_analytics_results('logs/analytics.txt')
analyzer.set_results(model_results)
analyzer.write_analysis_output(output_dir = 'output', filename_suffix = '1', iou_threshold = 0.3)

Parameters

ground_truth_path (string) – The path of CVAT dumped (for Videos) ground truth annotations file (.xml)
start_frame (int) – Starting Frame
end_frame (int) – Ending Frame

set_results(results)¶

Set the model detections list to analyse

Parameters: results (list of ObjectDetectionResults) – The list of ObjectDetectionResults to use for analysis.

get_detections_per_object(iou_threshold=0.01)¶

Get percentage detections per object

Parameters: iou_threshold (float) – Minimum IOU required to associate model detection with ground truth
Returns: dict

get_iou_distribution(iou_threshold=0.01, bins=10)¶

Get distribution of IOU

Parameters

iou_threshold (float) – Minimum IOU required to associate model detection with ground truth
bins (int) – Number of bins to distribute in

Returns

tuple – (Array of counts, Array of bin floors)

get_overlap_distribution(iou_threshold=0.01, bins=10)¶

Get distribution of Overlap

Parameters

iou_threshold (float) – Minimum IOU required to associate model detection with ground truth
bins (int) – Number of bins to distribute in

Returns

tuple – (Array of counts, Array of bin floors)

get_missed_detections(iou_threshold=0.01)¶

Get missed detections per objects

Parameters: iou_threshold (float) – Minimum IOU required to associate model detection with ground truth
Returns: dict – {ground_truth ID : list of missed frames}

get_class_based_stats(iou_threshold=0.01)¶

Get per class statistics like number of True Positives, False Positives, False Negatives, precision and recall for the class

Parameters: iou_threshold (float) – Minimum IOU required to associate model detection with ground truth
Returns: dict – {class_name : { num_gt: , num_detections: , TP: ,FP: ,FN: , precision: , recall: }}

write_analysis_output(output_dir='output_data', filename_suffix='', iou_threshold=0.01)¶

Compute all available stats and write data to csv files.

class_stats.csv -> Per class values for True Positives, False Positives, False Negatives, precision and recall object_stats.csv -> Per object values for ground truth, detections and %correct detections distribution_stats.csv -> IOU and Overlap distributions of True Positives

Parameters

filename_suffix (string) – Suffix to be added at the end of the generated filenames
iou_threshold (float) – Minimum IOU required to associate model detection with ground truth

class TrackerPerformanceAnalyzer(annotations_path, max_distance, start_frame=0, end_frame=None)¶

Analyze tracker performance against ground truth annotations.

TrackerPerformanceAnalyzer compares tracker results to ground truth annotations and collects data for two of the main performance flaws of tracking: ID changes and ID swaps.

ID changes occur when the tracker assigns a new tracker ID to an existing object. This can happen for a number of reasons

Object became occluded
Object was lost and found by tracker (tracker parameters are too tight)
Object ID was swapped with another object (this is examined more closely in ID swap analysis)

The impact of ID changes on the performance of your app depends on the scenario, but a typical result is logging more unique object than there actually were. ID changes can often be overcome by higher-layer analysis to associate objects that were occluded or lost.

ID swaps occur when an object is assigned a tracker ID that was previously assigned to another. This can be very hard to recover from, since it is hard to detect in real-life use cases. The most common causes are

Objects occlude each other
Tracker parameters are too loose

The impact of ID swaps on the performance of your app will be combining metrics from multiple objects into a single object.

Typical usage:

start_frame = 0
end_frame = 2000
tpa = edgeiq.TrackerPerformanceAnalyzer(
        annotations_path='path/to/cvat/annotations.xml',
        start_frame=start_frame,
        end_frame=end_frame,
        max_distance=5)

for frame_idx in range(start_frame, end_frame - 1):
    frame = load_annotation_frame(frame_idx)
    frame = tpa.markup_image(frame_idx, frame, (255, 100, 0))

    # Get predictions and tracker results
    tpa.update(frame_idx, tracked_objects)

report = tpa.generate_report().write_to_file(output_dir=path/to/save/dir)

Parameters

annotations_path (str) – The path of CVAT dumped ground truth annotations (.xml)
start_frame (int) – The start frame to load annotations for.
end_frame (Optional[int]) – The end frame to load annotations for, or None to load all remaining.
max_distance (int) – The max distance to be used for matching tracked objects with annotations.

Raises

FileNotFoundError if annotation file doesn’t exist

Raises

ValueError if start frame is greater than end

get_annotation_predictions(frame_idx)¶

The predictions for the given frame loaded from the annotations.

Parameters: frame_idx (int) – The frame index to read annotations from.
Return type: list
Returns: List of predictions

update(frame_idx, tracked_results)¶

Match a new set of tracker results with the ground truth results from annotations.

Parameters

frame_idx (int) – The frame index to read annotations from.
tracked_results (TrackingResults) – The output of an Object Tracker

markup_image(frame_idx, frame, color)¶

Draw boxes, centers, and matching radius of ground truth predictions on the frame.

Parameters

frame_idx (int) – The frame index to read annotations from.
frame (ndarray) – The image to draw on
color (Tuple[int, int, int]) – The color to draw the center and radius

Return type

ndarray

generate_report()¶

Generate the TrackerPerformanceReport.

Return type: TrackerPerformanceReport

class TrackerPerformanceReport(report)¶

The Tracker Performance Report generated by TrackerPerformanceAnalyzer

property id_change_report¶

The ID change report.

The ID change report is a dictionary with the format:

{
    'num_objects_with_id_changes': <>,
    'objects_with_id_changes': <>,
    'total_id_changes': <>,
    'id_change_events_by_frame': <>,
    'id_changes_by_ground_truth_id': <>
}

Return type: dict

property id_swap_report¶

The ID change report.

The ID change report is a dictionary with the format:

{
    'num_objects_with_id_swaps': <>,
    'id_swaps_by_ground_truth_id': <>,
    'total_object_swaps': <>,
    'id_swap_events_by_frame': <>
}

Return type: dict

write_to_file(output_dir)¶

Write the reports to files.

This function will save ‘id_swap_report.json’ and ‘id_change_report.json’ to the provided directory.

Parameters: output_dir (str) – The directory to save the reports in

Image Manipulation ¶

translate(image, x, y)¶

Translate an image on the X and/or Y axis.

Parameters

image (ndarray) – The image to manipulate.
x (int) – Translate image on X axis by this amount.
y (int) – Translate image on Y axis by this amount.

Return type

ndarray

Returns

The translated image.

rotate(image, angle)¶

Rotate an image by specified angle.

Parameters

image (ndarray) – The image to manipulate.
angle (int) – The angle to rotate the image by (degrees).

Return type

ndarray

Returns

The rotated image.

resize(image, width=None, height=None, keep_scale=True, inter=3)¶

Resize an image to specified height and width.

When both a width and height are given and keep_scale is True, these are treated as the maximum width and height.

Parameters

image (ndarray) – The image to manipulate.
height (Optional[int]) – The new height of image.
width (Optional[int]) – The new width of image.
keep_scale (bool) – Maintain the original scale of the image.
inter (int) – The interpolation method (One of OpenCV InterpolationFlags).

Return type

ndarray

convert_to_jpg(image, jpg_quality)¶

Convert the given image to JPEG represented in bytes.

When on a Jetson device, this will use the nvjpg hardware accelerator.

Parameters

image (ndarray) – The image to convert to JPEG
jpg_quality (int) – The quality setting for the JPEG conversion, in the range (0, 100)

Return type

bytes

Returns

The bytes of the encoded image

list_images(base_path, contains=None)¶

List all images in specified path.

Finds images with the following extensions:

.jpg
.jpeg
.png
.bmp
.tif
.tiff

Parameters

base_path (str) – The base path of folder where images are located.
contains (Optional[str]) – Select only filenames that contain this string.

Return type

List[str]

Returns

The valid image file paths.

list_files(base_path, valid_exts, contains=None)¶

List all files in specified path.

Parameters

base_path (str) – The base path of folder where files are located.
valid_exts (List[str]) – The list of valid extensions to filter for.
contains (Optional[str]) – Select only filenames that contain this string.

Return type

List[str]

Returns

The valid file paths.

pad_to_aspect_ratio(image, a_ratio)¶

Pad an image to a certain aspect ration.

Padding is added to the bottom and right of the image.

Parameters

image (ndarray) – The image to pad.
a_ratio (float) – The aspect ratio to pad to.

Return type

ndarray

Returns

The padded image

cutout_image(image, box)¶

Cut out the portion of an image outlined by a bounding box.

Parameters

image (ndarray) – The image to cut out from.
box (BoundingBox) – The bounding box outlining the section of the image to cut out.

Return type

ndarray

Returns

The segment of the image outlined by the bounding box. Will be independent from the original image.

blend_images(foreground_image, background_image, alpha)¶

Blend a foreground image with a background image, foreground image and background image must have the same dimensions and same color format (RGB/BGR).

Parameters

foreground_image (ndarray) – The image to be scaled by alpha in the blend.
background_image (ndarray) – The image to be scaled by 1 - alpha in the blend.
alpha (float) – The ratio of foreground to background image in the blend, in the range [0.0, 1.0]

Return type

ndarray

Returns

numpy array – The blended image.

overlay_image(foreground_image, background_image, foreground_mask)¶

Overlay a foreground image with a background image according to the foreground mask.

This function will mask both the foreground and background images, then combine them into the output image.

Parameters

foreground_image (ndarray) – The image to be overlaid on the background.
background_image (ndarray) – The image for the foreground to be overlaid on.
foreground_mask (ndarray) – A mask with white indicating foreground and black indicating background. Shades in between will blend the foreground and background accordingly.

Return type

ndarray

Returns

The overlaid image.

perform_histogram_equalization(image, color_space='GS', adaptive=False, clip_limit=2.0, tile_grid_size=(8, 8))¶

Performs Histogram Equalization on the input image and returns the equalized image.

Histogram equalization is a basic image processing technique that adjusts the global contrast of an image by updating the image histogram’s pixel intensity distribution. Doing so enables areas of low contrast to obtain higher contrast in the output image. This function includes implementations of both basic and adaptive histogram equalization. The basic histogram equalization will spread pixels to intensity “buckets” that don’t have as many pixels binned to them. Mathematically, what this means is that the function is applying a linear trend to the image’s cumulative distribution function (CDF). The adaptive histogram equalization function divides an input image into an M x N grid, and then applies equalization to each cell in the grid, resulting in a higher quality output image.

Parameters

image (ndarray) – The image on which we will do Histogram Equalization operation. (Gray Scaled or in BGR format)
color_space (str) – The color space of the image on which we will do Histogram Equalization. Supported color_space parameters: [“GS”, “YCrCb”, “YUV”, “HSV”, “LAB”]. If ‘color_space’ = “GS”, output image will be in gray scaled format(2D array). If ‘color_space’ != “GS”, output image will be in BGR format(3D array).
adaptive (bool) – Whether we want to enable adaptive Histogram Equalization or not.
clip_limit (float) – The clip limit value for Adaptive Histogram Equalization. The ‘clip_limit’ is used only if ‘adaptive’ = True. ‘clip_limit’ value is the threshold for contrast limiting. Typically it is adviced to use the value ranging from 2-5. Allowed range is 0-40. Larger values results in more local contrast and more noise. Try to keep the ‘clip_limit’ value as low as possible.
tile_grid_size (Tuple[int, int]) – Number of grids we want to divide the image into for Adaptive Histogram Equalization. The ‘tile_grid_size’ is used only if ‘adaptive’ = True.

Return type

ndarray

Returns

The image after doing Histogram Equalization(Gray Scaled or in BGR format)

perform_gamma_correction(image, gamma_value=0.8, color=False)¶

Performs gamma correction operation on the input image and returns the corrected image.

Gamma correction is done when you want to control a camera sensor’s color and luminance. Gamma correction is also known as the Power Law Transform: O = I ^ (1 / G) I = input image O = scaled back to the range [0, 255] G = gamma value, should be greater than 0. For gamma values < 1 will shift the image towards the darker end of the spectrum For gamma values > 1 will shift will make the image appear lighter For gamma value of 1 will have no effect

Parameters

image (ndarray) – The image on which we will do Gamma Correction operation.
color (bool) – True will do gamma correction on BGR image and False on Gray-Scaled image. If ‘color’ = True, the output image will be in BGR format(3D array). If ‘color’ = False, the output image will be gray sclaed format(2D array).
gamma_value (float) – The gamma value for Gamma Correction.

Return type

ndarray

Returns

The image after doing Gamma Correction(2D or 3D array)

Results Serialization ¶

to_json_serializable(results_input)¶

Takes in core Computer Vision service results, such as ObjectDetectionResults, ClassificationResults, HumanPoseResult, InstanceSegmentationResults, or results returned by calling the update() method on any of the tracking classes, such as CentroidTracker and returns them in a JSON-serializable format.

Typical usage:

...
results = obj_detect.detect_objects(frame, confidence_level=.5)
serialized_results = edgeiq.to_json_serializable(results)

Parameters: results_input (A core Computer Vision service result object.) – The object to serialize.
Returns: a json serializable object.

HW Discovery ¶

find_usb_device(id_vendor, id_product)¶

Check if a USB device is connected.

Parameters

id_vendor (int) – The vendor ID.
id_product (int) – The product ID.

Return type

bool

find_ncs2()¶

Check if an NCS2 is connected.

Note that once a connection to the NCS2 is opened, the device’s VID and PID will change and this function will no longer find it.

Return type: bool

find_pcie_device(id_vendor, id_product)¶

Check if a PCIe device is connected. :type id_vendor: str :param id_vendor: The vendor ID. :type id_product: str :param id_product: The product ID. :raises: RuntimeError if pciutils library is not found.

Return type: bool

find_hailo()¶

Check if Hailo-8 PCIe chip is connected.

Return type: bool

is_jetson()¶

Determine if running on a NVIDIA Jetson device.

Return type: bool

is_jetson_nano()¶

Determine if running on a NVIDIA Jetson Nano.

Return type: bool

is_jetson_xavier()¶

Determine if running on a NVIDIA Jetson Xavier (can be NX or AGX).

Return type: bool

is_jetson_xavier_nx()¶

Determine if running on a NVIDIA Jetson Xavier NX.

Return type: bool

is_jetson_agx_xavier()¶

Determine if running on a NVIDIA Jetson AGX Xavier.

Return type: bool

is_opencv_cuda_available()¶

Check if OpenCV is built with CUDA support and a CUDA device is available.

Return type: bool
Returns: True if OpenCV is built with CUDA and at least one CUDA device is available.

Analytics ¶

class CustomEvent(results)¶

Store the attributes of this result.

Parameters: results (dict) – The custom event data parsed from the analytics file.

property results¶

The custom event data parsed from the analytics file.

Type: dict

property tag¶

The tag for the custom event.

Type: any

load_analytics_results(filepath)¶

Load results from file published by the alwaysAI Analytics Service.

Parameters: filepath (string) – The full path to the file to load.
Returns: A list of the deserialized results. Each deserialized result will include a tag property.

Typical usage:

deserialized_results = edgeiq.load_analytics_results('logs/analytics.txt')
left_camera_results = [result for result in deserialized_results if 'left' in result.tag]
right_camera_results = [result for result in deserialized_results if 'right' in result.tag]

parse_analytics_packet(packet)¶

publish_analytics(results, tag=None)¶

Publish data to the alwaysAI Analytics Service

Parameters

results (JSON-serializable object.) – The results to publish.
tag (Optional[Any]) – Additional information to assist in querying and visualizations.

Raises

ConnectionBlockedError when using connection to the alwaysAI Device Agent and resources are at capacity,

Raises

PacketRateError when publish rate exceeds current limit,

Raises

PacketSizeError when packet size exceeds current limit. Packet publish size and rate limits will be provided in the error message.

Tools¶

Performance¶

Image Manipulation¶

Results Serialization¶

HW Discovery¶

Analytics¶

Performance ¶

Image Manipulation ¶

Results Serialization ¶

HW Discovery ¶

Analytics ¶