| # Copyright © 2020-2022 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 |
| |
| |
| def preprocess(frame: np.ndarray, input_data_type, input_data_shape: tuple, is_normalised: bool, |
| keep_aspect_ratio: bool=True): |
| """ |
| Takes a frame, resizes, swaps channels and converts data type to match |
| model input layer. |
| |
| Args: |
| frame: Captured frame from video. |
| input_data_type: Contains data type of model input layer. |
| input_data_shape: Contains shape of model input layer. |
| is_normalised: if the input layer expects normalised data |
| keep_aspect_ratio: Network executor's input data aspect ratio |
| |
| Returns: |
| Input tensor. |
| """ |
| frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB) |
| |
| if keep_aspect_ratio: |
| # Swap channels and resize frame to model resolution |
| resized_frame = resize_with_aspect_ratio(frame, input_data_shape) |
| else: |
| # select the height and width from input_data_shape |
| frame_height = input_data_shape[1] |
| frame_width = input_data_shape[2] |
| resized_frame = cv2.resize(frame, (frame_width, frame_height)) |
| # Expand dimensions and convert data type to match model input |
| if np.float32 == input_data_type: |
| 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 == input_data_shape |
| |
| return resized_frame |
| |
| |
| def resize_with_aspect_ratio(frame: np.ndarray, input_data_shape: tuple): |
| """ |
| Resizes frame while maintaining aspect ratio, padding any empty space. |
| |
| Args: |
| frame: Captured frame. |
| input_data_shape: 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, _ = input_data_shape |
| |
| 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)) |
| |
| |
| def crop_bounding_box_object(input_frame: np.ndarray, x_min: float, y_min: float, x_max: float, y_max: float): |
| """ |
| Creates a cropped image based on x and y coordinates. |
| |
| Args: |
| input_frame: Image to crop |
| x_min, y_min, x_max, y_max: Coordinates of the bounding box |
| |
| Returns: |
| Cropped image |
| """ |
| # Adding +1 to exclude the bounding box pixels. |
| cropped_image = input_frame[int(y_min) + 1:int(y_max), int(x_min) + 1:int(x_max)] |
| return cropped_image |