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# 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
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