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import tensorflow as tf
import cv2
import numpy as np
import config
from logger import logging
def preprocess_video(video_path : str) -> tuple[tf.Tensor, list] :
"""
Preprocess the video by keeping the required number of frames,
resizing the frames and normalizing the frames.
params :
video_path : path of the video file
returns :
Returns tuple (input_tensor, frame_list)
input_tensor : video with required number of frames and size
frame_list : list of required number of frames
"""
logging.info(">>> Preprocessing the video....")
# load the video
video_capture = cv2.VideoCapture(video_path)
# the number of frames in the original video
original_number_of_frames = video_capture.get(cv2.CAP_PROP_FRAME_COUNT)
# gap between two consecutive frames to capture
frame_interval = int(original_number_of_frames / config.FRAME_NUM)
new_video , frame_list = [] , []
for i in range(0, config.FRAME_NUM ):
video_capture.set(cv2.CAP_PROP_POS_FRAMES, i*frame_interval)
success, frame = video_capture.read()
if not success :
logging.info("video loading failed")
break
frame_list.append(frame)
# Resize the Frame to fixed height and width.
resized_frame = cv2.resize(frame, (config.FRAME_HT, config.FRAME_WD))
# Normalize the resized frame by dividing it with 255 so that each pixel value then lies between 0 and 1
normalized_frame = resized_frame / 255
# Append the normalized frame into the frames list
new_video.append(normalized_frame)
new_video_array = np.asarray(new_video)
input_tensor = tf.expand_dims(new_video_array, axis=0)
video_capture.release()
logging.info("Video processing successful.")
return input_tensor, frame_list
# Get top_k labels and probabilities
def get_top_k(probs, label_map,k=5 ):
"""Outputs the top k model labels and probabilities on the given video.
Args:
probs: probability tensor of shape (num_frames, num_classes) that represents
the probability of each class on each frame.
k: the number of top predictions to select.
label_map: a list of labels to map logit indices to label strings.
Returns:
a tuple of the top-k labels and probabilities.
"""
# Sort predictions to find top_k
indices = tf.argsort(probs, direction='DESCENDING').numpy()[0][:k]
# collect the labels of top_k predictions
labels = tf.gather(label_map, indices).numpy()
# decode lablels
labels = [label.decode('utf8') for label in labels]
# top_k probabilities of the predictions
top_probs = tf.gather(probs[0], indices).numpy()
output = dict()
for label, prob in zip(labels, top_probs):
output[label] = float(prob) / 100
return output
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