From 433a59567ccf7fd6fdbbd1227eac3778876e8bd9 Mon Sep 17 00:00:00 2001 From: Jakub Sujak Date: Wed, 17 Jun 2020 15:35:03 +0100 Subject: MLECO-955: Added python object detection example for PyArmNN Change-Id: I1344c027f4cc70520b7846b34dfbc2abf399d10a Signed-off-by: Jakub Sujak --- python/pyarmnn/examples/object_detection/utils.py | 231 ++++++++++++++++++++++ 1 file changed, 231 insertions(+) create mode 100644 python/pyarmnn/examples/object_detection/utils.py (limited to 'python/pyarmnn/examples/object_detection/utils.py') diff --git a/python/pyarmnn/examples/object_detection/utils.py b/python/pyarmnn/examples/object_detection/utils.py new file mode 100644 index 0000000000..1235bf4fa6 --- /dev/null +++ b/python/pyarmnn/examples/object_detection/utils.py @@ -0,0 +1,231 @@ +# 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) -- cgit v1.2.1