CODEWITHSUNDEEP
pythontensorflow-liteinference
Sayaki
Sayaki

Reputation: 789

TFLite Inference on video input

I have an SSD tflite detection model that I am running with Python on a desktop computer. As for now, my script below takes a single image as an input for inference and it works fine:

    # Load TFLite model and allocate tensors.
    interpreter = tf.lite.Interpreter(model_path="model.tflite")
    interpreter.allocate_tensors()

    img_resized = Image.open(file_name)
    input_data = np.expand_dims(img_resized, axis=0)
    input_data = (np.float32(input_data) - input_mean) / input_std

    input_details = interpreter.get_input_details()
    output_details = interpreter.get_output_details()

    interpreter.set_tensor(input_details[0]['index'], input_data)
    interpreter.invoke()
    output_data = interpreter.get_tensor(output_details[0]['index'])

How to run inference on a .mp4 video as an input?

Is it also possible to draw bounding boxes from detected objects on that video?

Upvotes: 5

Views: 3847

Answers (1)

MrKhan
MrKhan

Reputation: 154

To Answer your first question of running inference on a video. Here is the code that you can use. I made this code for the inference of classification model, So in your case the output of the output_data variable will be in the form of bounding boxes, you have to map them on the frames using OpenCV which answer your second question as well (drawing bounding boxes on the video).

import cv2
from PIL import Image
import numpy as np
import tensorflow as tf

def read_tensor_from_readed_frame(frame, input_height=224, input_width=224,
        input_mean=0, input_std=255):
  output_name = "normalized"
  float_caster = tf.cast(frame, tf.float32)
  dims_expander = tf.expand_dims(float_caster, 0);
  resized = tf.image.resize_bilinear(dims_expander, [input_height, input_width])
  normalized = tf.divide(tf.subtract(resized, [input_mean]), [input_std])
  sess = tf.Session()
  result = sess.run(normalized)
  return result

def load_labels(label_file):
  label = []
  proto_as_ascii_lines = tf.gfile.GFile(label_file).readlines()
  for l in proto_as_ascii_lines:
    label.append(l.rstrip())
  return label

def VideoSrcInit(paath):
    cap = cv2.VideoCapture(paath)
    flag, image = cap.read()
    if flag:
        print("Valid Video Path. Lets move to detection!")
    else:
        raise ValueError("Video Initialization Failed. Please make sure video path is valid.")
    return cap

def main():
  Labels_Path = "labels.txt"
  Model_Path = "model.tflite"
  input_path = "video.mp4"

  ##Loading labels
  labels = load_labels(Labels_Path)

  ##Load tflite model and allocate tensors
  interpreter = tf.lite.Interpreter(model_path=Model_Path)
  interpreter.allocate_tensors()
  # Get input and output tensors.
  input_details = interpreter.get_input_details()
  output_details = interpreter.get_output_details()

  input_shape = input_details[0]['shape']

  ##Read video
  cap = VideoSrcInit(input_path)

  while True:
    ok, cv_image = cap.read()
    if not ok:
      break

    ##Converting the readed frame to RGB as opencv reads frame in BGR
    image = Image.fromarray(cv_image).convert('RGB')

    ##Converting image into tensor
    image_tensor = read_tensor_from_readed_frame(image ,224, 224)

    ##Test model
    interpreter.set_tensor(input_details[0]['index'], image_tensor)
    interpreter.invoke()
    output_data = interpreter.get_tensor(output_details[0]['index'])

    ## You need to check the output of the output_data variable and 
    ## map it on the frame in order to draw the bounding boxes.


    cv2.namedWindow("cv_image", cv2.WINDOW_NORMAL)
    cv2.imshow("cv_image",cv_image)

    ##Use p to pause the video and use q to termiate the program
    key = cv2.waitKey(10) & 0xFF
    if key == ord("q"):
      break
    elif key == ord("p"):
      cv2.waitKey(0)
      continue 
  cap.release()

if __name__ == '__main__':
  main()

Upvotes: 3

Related Questions