python/pyarmnn/examples/object_detection/utils.py - platform/external/armnn - Git at Google

 # 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)
	# 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)