I am working on a personal project on Raspberry Pi 4 and I used Tensorflow. I have achieved around 1.39 fps and I wanted to convert to Tensorflow Lite to get more fps as well as utilize a Coral USB Accelerator and would like to know how can I use a Tensorflow lite model for this code.
# Import packages
import os
import cv2
import numpy as np
import tensorflow as tf
import argparse
import sys
import tensorflow.compat.v1 as tf
tf.disable_v2_behavior()
# Set up camera constants
IM_WIDTH = 640
IM_HEIGHT = 480
# Select camera type (if user enters --usbcam when calling this script,
# a USB webcam will be used)
parser = argparse.ArgumentParser()
parser.add_argument('--usbcam', help='Use a USB webcam instead of picamera',
action='store_true')
args = parser.parse_args()
if args.usbcam:
camera_type = 'usb'
#### Initialize TensorFlow model ####
# This is needed since the working directory is the object_detection folder.
sys.path.append('..')
# Import utilites
from object_detection.utils import label_map_util
from object_detection.utils import visualization_utils as vis_util
# Name of the directory containing the object detection module we're using
MODEL_NAME = 'ssd_inception_v2_coco_2017_11_17'
# Grab path to current working directory
CWD_PATH = os.getcwd()
# Path to frozen detection graph .pb file, which contains the model that is used
# for object detection.
PATH_TO_CKPT = os.path.join(CWD_PATH,MODEL_NAME,'frozen_inference_graph.pb')
# Path to label map file
PATH_TO_LABELS = os.path.join(CWD_PATH,'data','mscoco_label_map.pbtxt')
# Number of classes the object detector can identify
NUM_CLASSES = 90
## Load the label map.
# Label maps map indices to category names, so that when the convolution
# network predicts `5`, we know that this corresponds to `airplane`.
# Here we use internal utility functions, but anything that returns a
# dictionary mapping integers to appropriate string labels would be fine
label_map = label_map_util.load_labelmap(PATH_TO_LABELS)
categories = label_map_util.convert_label_map_to_categories(label_map, max_num_classes=NUM_CLASSES, use_display_name=True)
category_index = label_map_util.create_category_index(categories)
# Load the Tensorflow model into memory.
detection_graph = tf.Graph()
with detection_graph.as_default():
od_graph_def = tf.GraphDef()
with tf.gfile.GFile(PATH_TO_CKPT, 'rb') as fid:
serialized_graph = fid.read()
od_graph_def.ParseFromString(serialized_graph)
tf.import_graph_def(od_graph_def, name='')
sess = tf.Session(graph=detection_graph)
# Define input and output tensors (i.e. data) for the object detection classifier
# Input tensor is the image
image_tensor = detection_graph.get_tensor_by_name('image_tensor:0')
# Output tensors are the detection boxes, scores, and classes
# Each box represents a part of the image where a particular object was detected
detection_boxes = detection_graph.get_tensor_by_name('detection_boxes:0')
# Each score represents level of confidence for each of the objects.
# The score is shown on the result image, together with the class label.
detection_scores = detection_graph.get_tensor_by_name('detection_scores:0')
detection_classes = detection_graph.get_tensor_by_name('detection_classes:0')
# Number of objects detected
num_detections = detection_graph.get_tensor_by_name('num_detections:0')
#### Initialize other parameters ####
# Initialize frame rate calculation
frame_rate_calc = 1
freq = cv2.getTickFrequency()
font = cv2.FONT_HERSHEY_SIMPLEX
# Define inside box coordinates (top left and bottom right)
TL_inside = (int(IM_WIDTH*0.016),int(IM_HEIGHT*0.021))
BR_inside = (int(IM_WIDTH*0.323),int(IM_HEIGHT*0.979))
# Define outside box coordinates (top left and bottom right)
TL_outside = (int(IM_WIDTH*0.333),int(IM_HEIGHT*0.021))
BR_outside = (int(IM_WIDTH*0.673),int(IM_HEIGHT*0.979))
# Define outside box coordinates (top left and bottom right)
TL_right = (int(IM_WIDTH*0.683),int(IM_HEIGHT*0.021))
BR_right = (int(IM_WIDTH*0.986),int(IM_HEIGHT*0.979))
# Initialize control variables used for pet detector
detected_inside = False
detected_outside = False
detected_right = False
inside_counter = 0
outside_counter = 0
right_counter = 0
pause = 0
pause_counter = 0
#### Pet detection function ####
# This function contains the code to detect a pet, determine if it's
# inside or outside, and send a text to the user's phone.
def pet_detector(frame):
# Use globals for the control variables so they retain their value after function exits
global detected_inside, detected_outside, detected_right
global inside_counter, outside_counter, right_counter
global pause, pause_counter
frame_expanded = np.expand_dims(frame, axis=0)
# Perform the actual detection by running the model with the image as input
(boxes, scores, classes, num) = sess.run(
[detection_boxes, detection_scores, detection_classes, num_detections],
feed_dict={image_tensor: frame_expanded})
# Draw the results of the detection (aka 'visulaize the results')
vis_util.visualize_boxes_and_labels_on_image_array(
frame,
np.squeeze(boxes),
np.squeeze(classes).astype(np.int32),
np.squeeze(scores),
category_index,
use_normalized_coordinates=True,
line_thickness=8,
min_score_thresh=0.40)
# Draw boxes defining "outside" and "inside" locations.
cv2.rectangle(frame,TL_outside,BR_outside,(255,20,20),3)
cv2.putText(frame,"Outside box",(TL_outside[0]+10,TL_outside[1]-10),font,1,(255,20,255),3,cv2.LINE_AA)
cv2.rectangle(frame,TL_inside,BR_inside,(20,20,255),3)
cv2.putText(frame,"Inside box",(TL_inside[0]+10,TL_inside[1]-10),font,1,(20,255,255),3,cv2.LINE_AA)
cv2.rectangle(frame,TL_right,BR_right,(20,255,25),3)
cv2.putText(frame,"right box",(TL_right[0]+10,TL_right[1]-10),font,1,(20,255,255),3,cv2.LINE_AA)
# Check the class of the top detected object by looking at classes[0][0].
# If the top detected object is a cat (17) or a dog (18) (or a teddy bear (88) for test purposes),
# find its center coordinates by looking at the boxes[0][0] variable.
# boxes[0][0] variable holds coordinates of detected objects as (ymin, xmin, ymax, xmax)
if (((int(classes[0][0]) == 1) or (int(classes[0][0] == 18) or (int(classes[0][0]) == 88))) and (pause == 0)):
x = int(((boxes[0][0][1]+boxes[0][0][3])/2)*IM_WIDTH)
y = int(((boxes[0][0][0]+boxes[0][0][2])/2)*IM_HEIGHT)
# Draw a circle at center of object
cv2.circle(frame,(x,y), 5, (75,13,180), -1)
# If object is in inside box, increment inside counter variable
if ((x > TL_inside[0]) and (x < BR_inside[0]) and (y > TL_inside[1]) and (y < BR_inside[1])):
inside_counter = inside_counter + 1
# If object is in outside box, increment outside counter variable
if ((x > TL_outside[0]) and (x < BR_outside[0]) and (y > TL_outside[1]) and (y < BR_outside[1])):
outside_counter = outside_counter + 1
# If object is in outside box, increment outside counter variable
if ((x > TL_right[0]) and (x < BR_right[0]) and (y > TL_right[1]) and (y < BR_right[1])):
right_counter = right_counter + 1
# If pet has been detected inside for more than 10 frames, set detected_inside flag
# and send a text to the phone.
if inside_counter == 1:
detected_inside = True
inside_counter = 0
outside_counter = 0
right_counter = 0
# Pause pet detection by setting "pause" flag
pause = 1
# If pet has been detected outside for more than 10 frames, set detected_outside flag
# and send a text to the phone.
if outside_counter == 1:
detected_outside = True
inside_counter = 0
outside_counter = 0
right_counter = 0
# Pause pet detection by setting "pause" flag
pause = 1
# If pet has been detected outside for more than 10 frames, set detected_outside flag
# and send a text to the phone.
if right_counter == 1:
detected_right = True
inside_counter = 0
outside_counter = 0
right_counter = 0
# Pause pet detection by setting "pause" flag
pause = 1
# If pause flag is set, draw message on screen.
if pause == 1:
if detected_inside == True:
cv2.putText(frame,'Left detected!',(int(IM_WIDTH*0.027),int(IM_HEIGHT-60)),font,3,(0,0,0),7,cv2.LINE_AA)
cv2.putText(frame,'Left detected!',(int(IM_WIDTH*0.967),int(IM_HEIGHT-60)),font,3,(95,176,23),5,cv2.LINE_AA)
if detected_outside == True:
cv2.putText(frame,'Mid detected!',(int(IM_WIDTH*0.027),int(IM_HEIGHT-60)),font,3,(0,0,0),7,cv2.LINE_AA)
cv2.putText(frame,'Mid detected!',(int(IM_WIDTH*0.967),int(IM_HEIGHT-60)),font,3,(95,176,23),5,cv2.LINE_AA)
if detected_right == True:
cv2.putText(frame,'Right detected!',(int(IM_WIDTH*0.027),int(IM_HEIGHT-60)),font,3,(0,0,0),7,cv2.LINE_AA)
cv2.putText(frame,'Right detected!',(int(IM_WIDTH*0.967),int(IM_HEIGHT-60)),font,3,(95,176,23),5,cv2.LINE_AA)
# Increment pause counter until it reaches 30 (for a framerate of 1.5 FPS, this is about 20 seconds),
# then unpause the application (set pause flag to 0).
pause_counter = pause_counter + 1
if pause_counter > 3:
pause = 0
pause_counter = 0
detected_inside = False
detected_outside = False
detected_right = False
# Draw counter info
cv2.putText(frame,'Detection counter: ' + str(max(inside_counter,outside_counter, right_counter)),(10,100),font,0.5,(255,255,0),1,cv2.LINE_AA)
cv2.putText(frame,'Pause counter: ' + str(pause_counter),(10,150),font,0.5,(255,255,0),1,cv2.LINE_AA)
return frame
#### Initialize camera and perform object detection ####
# The camera has to be set up and used differently depending on if it's a
# Picamera or USB webcam.
### USB webcam ###
# Initialize USB webcam feed
camera = cv2.VideoCapture(0)
ret = camera.set(3,IM_WIDTH)
ret = camera.set(4,IM_HEIGHT)
# Continuously capture frames and perform object detection on them
while(True):
t1 = cv2.getTickCount()
# Acquire frame and expand frame dimensions to have shape: [1, None, None, 3]
# i.e. a single-column array, where each item in the column has the pixel RGB value
ret, frame = camera.read()
# Pass frame into pet detection function
frame = pet_detector(frame)
# Draw FPS
cv2.putText(frame,"FPS: {0:.2f}".format(frame_rate_calc),(30,50),font,1,(255,255,0),2,cv2.LINE_AA)
# All the results have been drawn on the frame, so it's time to display it.
cv2.imshow('Object detector', frame)
# FPS calculation
t2 = cv2.getTickCount()
time1 = (t2-t1)/freq
frame_rate_calc = 1/time1
# Press 'q' to quit
if cv2.waitKey(1) == ord('q'):
break
camera.release()
cv2.destroyAllWindows()
I do not know what are the equivalent syntaxes for Tensorflow and Tensorflow lite. Any help will be appreciated! Cheers!
I have tried just straight up swapping the .pb model for a .tflite model but that did not work at all.
To run TFLite model you need to use TFLite interpreter. Here is example:
import tensorflow as tf
# load model
interpreter = tf.lite.Interpreter(model_path='your_model.tflite')
interpreter.allocate_tensors()
# get input/output details
input_details = interpreter.get_input_details()
output_details = interpreter.get_output_details()
# load your data
input_data = <your_data>
# set model input
interpreter.set_tensor(input_details[0]['index'], input_data)
# run model
interpreter.invoke()
# get output
output_data = interpreter.get_tensor(output_details[0]['index'])