MLECO-1253 Adding ASR sample application using the PyArmNN api

Change-Id: I450b23800ca316a5bfd4608c8559cf4f11271c21
Signed-off-by: Éanna Ó Catháin <eanna.ocathain@arm.com>

1 files changed, 0 insertions, 231 deletions

diff --git a/python/pyarmnn/examples/object_detection/utils.py b/python/pyarmnn/examples/object_detection/utils.py
deleted file mode 100644
index 1235bf4fa6..0000000000
--- a/python/pyarmnn/examples/object_detection/utils.py
+++ /dev/null
@@ -1,231 +0,0 @@
-# Copyright © 2020 Arm Ltd and Contributors. All rights reserved.

-# SPDX-License-Identifier: MIT

-

-"""

-This file contains shared functions used in the object detection scripts for

-preprocessing data, preparing the network and postprocessing.

-"""

-

-import os

-import cv2

-import numpy as np

-import pyarmnn as ann

-

-

-def create_video_writer(video: cv2.VideoCapture, video_path: str, output_path: str):

-    """

-    Creates a video writer object to write processed frames to file.

-

-    Args:

-        video: Video capture object, contains information about data source.

-        video_path: User-specified video file path.

-        output_path: Optional path to save the processed video.

-

-    Returns:

-        Video writer object.

-    """

-    _, ext = os.path.splitext(video_path)

-

-    if output_path is not None:

-        assert os.path.isdir(output_path)

-

-    i, filename = 0, os.path.join(output_path if output_path is not None else str(), f'object_detection_demo{ext}')

-    while os.path.exists(filename):

-        i += 1

-        filename = os.path.join(output_path if output_path is not None else str(), f'object_detection_demo({i}){ext}')

-

-    video_writer = cv2.VideoWriter(filename=filename,

-                                   fourcc=cv2.VideoWriter_fourcc(*'mp4v'),

-                                   fps=int(video.get(cv2.CAP_PROP_FPS)),

-                                   frameSize=(int(video.get(cv2.CAP_PROP_FRAME_WIDTH)),

-                                              int(video.get(cv2.CAP_PROP_FRAME_HEIGHT))))

-    return video_writer

-

-

-def create_network(model_file: str, backends: list):

-    """

-    Creates a network based on the model file and a list of backends.

-

-    Args:

-        model_file: User-specified model file.

-        backends: List of backends to optimize network.

-

-    Returns:

-        net_id: Unique ID of the network to run.

-        runtime: Runtime context for executing inference.

-        input_binding_info: Contains essential information about the model input.

-        output_binding_info: Used to map output tensor and its memory.

-    """

-    if not os.path.exists(model_file):

-        raise FileNotFoundError(f'Model file not found for: {model_file}')

-

-    # Determine which parser to create based on model file extension

-    parser = None

-    _, ext = os.path.splitext(model_file)

-    if ext == '.tflite':

-        parser = ann.ITfLiteParser()

-    elif ext == '.pb':

-        parser = ann.ITfParser()

-    elif ext == '.onnx':

-        parser = ann.IOnnxParser()

-    assert (parser is not None)

-    network = parser.CreateNetworkFromBinaryFile(model_file)

-

-    # Specify backends to optimize network

-    preferred_backends = []

-    for b in backends:

-        preferred_backends.append(ann.BackendId(b))

-

-    # Select appropriate device context and optimize the network for that device

-    options = ann.CreationOptions()

-    runtime = ann.IRuntime(options)

-    opt_network, messages = ann.Optimize(network, preferred_backends, runtime.GetDeviceSpec(),

-                                  ann.OptimizerOptions())

-    print(f'Preferred backends: {backends}\n{runtime.GetDeviceSpec()}\n'

-          f'Optimization warnings: {messages}')

-

-    # Load the optimized network onto the Runtime device

-    net_id, _ = runtime.LoadNetwork(opt_network)

-

-    # Get input and output binding information

-    graph_id = parser.GetSubgraphCount() - 1

-    input_names = parser.GetSubgraphInputTensorNames(graph_id)

-    input_binding_info = parser.GetNetworkInputBindingInfo(graph_id, input_names[0])

-    output_names = parser.GetSubgraphOutputTensorNames(graph_id)

-    output_binding_info = []

-    for output_name in output_names:

-        outBindInfo = parser.GetNetworkOutputBindingInfo(graph_id, output_name)

-        output_binding_info.append(outBindInfo)

-    return net_id, runtime, input_binding_info, output_binding_info

-

-

-def dict_labels(labels_file: str):

-    """

-    Creates a labels dictionary from the input labels file.

-

-    Args:

-        labels_file: Default or user-specified file containing the model output labels.

-

-    Returns:

-        A dictionary keyed on the classification index with values corresponding to

-        labels and randomly generated RGB colors.

-    """

-    labels_dict = {}

-    with open(labels_file, 'r') as labels:

-        for index, line in enumerate(labels, 0):

-            labels_dict[index] = line.strip('\n'), tuple(np.random.random(size=3) * 255)

-        return labels_dict

-

-

-def resize_with_aspect_ratio(frame: np.ndarray, input_binding_info: tuple):

-    """

-    Resizes frame while maintaining aspect ratio, padding any empty space.

-

-    Args:

-        frame: Captured frame.

-        input_binding_info: Contains shape of model input layer.

-

-    Returns:

-        Frame resized to the size of model input layer.

-    """

-    aspect_ratio = frame.shape[1] / frame.shape[0]

-    model_height, model_width = list(input_binding_info[1].GetShape())[1:3]

-

-    if aspect_ratio >= 1.0:

-        new_height, new_width = int(model_width / aspect_ratio), model_width

-        b_padding, r_padding = model_height - new_height, 0

-    else:

-        new_height, new_width = model_height, int(model_height * aspect_ratio)

-        b_padding, r_padding = 0, model_width - new_width

-

-    # Resize and pad any empty space

-    frame = cv2.resize(frame, (new_width, new_height), interpolation=cv2.INTER_LINEAR)

-    frame = cv2.copyMakeBorder(frame, top=0, bottom=b_padding, left=0, right=r_padding,

-                               borderType=cv2.BORDER_CONSTANT, value=[0, 0, 0])

-    return frame

-

-

-def preprocess(frame: np.ndarray, input_binding_info: tuple):

-    """

-    Takes a frame, resizes, swaps channels and converts data type to match

-    model input layer. The converted frame is wrapped in a const tensor

-    and bound to the input tensor.

-

-    Args:

-        frame: Captured frame from video.

-        input_binding_info:  Contains shape and data type of model input layer.

-

-    Returns:

-        Input tensor.

-    """

-    # Swap channels and resize frame to model resolution

-    frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)

-    resized_frame = resize_with_aspect_ratio(frame, input_binding_info)

-

-    # Expand dimensions and convert data type to match model input

-    data_type = np.float32 if input_binding_info[1].GetDataType() == ann.DataType_Float32 else np.uint8

-    resized_frame = np.expand_dims(np.asarray(resized_frame, dtype=data_type), axis=0)

-    assert resized_frame.shape == tuple(input_binding_info[1].GetShape())

-

-    input_tensors = ann.make_input_tensors([input_binding_info], [resized_frame])

-    return input_tensors

-

-

-def execute_network(input_tensors: list, output_tensors: list, runtime, net_id: int) -> np.ndarray:

-    """

-    Executes inference for the loaded network.

-

-    Args:

-        input_tensors: The input frame tensor.

-        output_tensors: The output tensor from output node.

-        runtime: Runtime context for executing inference.

-        net_id: Unique ID of the network to run.

-

-    Returns:

-        Inference results as a list of ndarrays.

-    """

-    runtime.EnqueueWorkload(net_id, input_tensors, output_tensors)

-    output = ann.workload_tensors_to_ndarray(output_tensors)

-    return output

-

-

-def draw_bounding_boxes(frame: np.ndarray, detections: list, resize_factor, labels: dict):

-    """

-    Draws bounding boxes around detected objects and adds a label and confidence score.

-

-    Args:

-        frame: The original captured frame from video source.

-        detections: A list of detected objects in the form [class, [box positions], confidence].

-        resize_factor: Resizing factor to scale box coordinates to output frame size.

-        labels: Dictionary of labels and colors keyed on the classification index.

-    """

-    for detection in detections:

-        class_idx, box, confidence = [d for d in detection]

-        label, color = labels[class_idx][0].capitalize(), labels[class_idx][1]

-

-        # Obtain frame size and resized bounding box positions

-        frame_height, frame_width = frame.shape[:2]

-        x_min, y_min, x_max, y_max = [int(position * resize_factor) for position in box]

-

-        # Ensure box stays within the frame

-        x_min, y_min = max(0, x_min), max(0, y_min)

-        x_max, y_max = min(frame_width, x_max), min(frame_height, y_max)

-

-        # Draw bounding box around detected object

-        cv2.rectangle(frame, (x_min, y_min), (x_max, y_max), color, 2)

-

-        # Create label for detected object class

-        label = f'{label} {confidence * 100:.1f}%'

-        label_color = (0, 0, 0) if sum(color)>200 else (255, 255, 255)

-

-        # Make sure label always stays on-screen

-        x_text, y_text = cv2.getTextSize(label, cv2.FONT_HERSHEY_DUPLEX, 1, 1)[0][:2]

-

-        lbl_box_xy_min = (x_min, y_min if y_min<25 else y_min - y_text)

-        lbl_box_xy_max = (x_min + int(0.55 * x_text), y_min + y_text if y_min<25 else y_min)

-        lbl_text_pos = (x_min + 5, y_min + 16 if y_min<25 else y_min - 5)

-

-        # Add label and confidence value

-        cv2.rectangle(frame, lbl_box_xy_min, lbl_box_xy_max, color, -1)

-        cv2.putText(frame, label, lbl_text_pos, cv2.FONT_HERSHEY_DUPLEX, 0.50,

-                    label_color, 1, cv2.LINE_AA)