# Copyright © 2020-2021 Arm Ltd and Contributors. All rights reserved. # SPDX-License-Identifier: MIT """ This file contains helper functions for reading video/image data and pre/postprocessing of video/image data using OpenCV. """ import os import cv2 import numpy as np import pyarmnn as ann def preprocess(frame: np.ndarray, input_binding_info: tuple, is_normalised: bool): """ 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. is_normalised: if the input layer expects normalised data 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 if input_binding_info[1].GetDataType() == ann.DataType_Float32: data_type = np.float32 if is_normalised: resized_frame = resized_frame.astype("float32")/255 else: data_type = 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 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 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=get_source_encoding_int(video), 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 init_video_file_capture(video_path: str, output_path: str): """ Creates a video capture object from a video file. Args: video_path: User-specified video file path. output_path: Optional path to save the processed video. Returns: Video capture object to capture frames, video writer object to write processed frames to file, plus total frame count of video source to iterate through. """ if not os.path.exists(video_path): raise FileNotFoundError(f'Video file not found for: {video_path}') video = cv2.VideoCapture(video_path) if not video.isOpened: raise RuntimeError(f'Failed to open video capture from file: {video_path}') video_writer = create_video_writer(video, video_path, output_path) iter_frame_count = range(int(video.get(cv2.CAP_PROP_FRAME_COUNT))) return video, video_writer, iter_frame_count def init_video_stream_capture(video_source: int): """ Creates a video capture object from a device. Args: video_source: Device index used to read video stream. Returns: Video capture object used to capture frames from a video stream. """ video = cv2.VideoCapture(video_source) if not video.isOpened: raise RuntimeError(f'Failed to open video capture for device with index: {video_source}') print('Processing video stream. Press \'Esc\' key to exit the demo.') return video 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) def get_source_encoding_int(video_capture): return int(video_capture.get(cv2.CAP_PROP_FOURCC))