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Diffstat (limited to 'python/pyarmnn/examples/common/cv_utils.py')
-rw-r--r-- | python/pyarmnn/examples/common/cv_utils.py | 184 |
1 files changed, 184 insertions, 0 deletions
diff --git a/python/pyarmnn/examples/common/cv_utils.py b/python/pyarmnn/examples/common/cv_utils.py new file mode 100644 index 0000000000..61aa46c3d7 --- /dev/null +++ b/python/pyarmnn/examples/common/cv_utils.py @@ -0,0 +1,184 @@ +# Copyright © 2020 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): + """ + 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 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)) |