TFLite_detection_webcam_loop.py

######## Webcam Object Detection Using Tensorflow-trained Classifier #########
#
# Author: Evan Juras & Ethan Dell
# Date: 10/27/19 & 1/24/21
# Description: 
# This program uses a TensorFlow Lite model to perform object detection on a live webcam
# feed. It draws boxes and scores around the objects of interest in each frame from the
# webcam. To improve FPS, the webcam object runs in a separate thread from the main program.
# This script will work with either a Picamera or regular USB webcam.
#
# This code is based off the TensorFlow Lite image classification example at:
# https://github.com/tensorflow/tensorflow/blob/master/tensorflow/lite/examples/python/label_image.py
#
# I added my own method of drawing boxes and labels using OpenCV.
# ^^^^ Credit to Evan for writing this script. I modified it to interface with a GPIO button and LED and also save out images.

# Import packages
import os
import argparse
import cv2
import numpy as np
import sys
import pdb
import time
import pathlib
from threading import Thread
import importlib.util
import datetime

import time
import RPi.GPIO as GPIO

GPIO.setmode(GPIO.BCM)
#led
GPIO.setup(4, GPIO.OUT)
#button
GPIO.setup(17, GPIO.IN, pull_up_down=GPIO.PUD_UP)


# Define VideoStream class to handle streaming of video from webcam in separate processing thread
# Source - Adrian Rosebrock, PyImageSearch: https://www.pyimagesearch.com/2015/12/28/increasing-raspberry-pi-fps-with-python-and-opencv/
class VideoStream:
    """Camera object that controls video streaming from the Picamera"""
    def __init__(self,resolution=(640,480),framerate=30):
        # Initialize the PiCamera and the camera image stream
        self.stream = cv2.VideoCapture(0)
        ret = self.stream.set(cv2.CAP_PROP_FOURCC, cv2.VideoWriter_fourcc(*'MJPG'))
        ret = self.stream.set(3,resolution[0])
        ret = self.stream.set(4,resolution[1])
            
        # Read first frame from the stream
        (self.grabbed, self.frame) = self.stream.read()

    # Variable to control when the camera is stopped
        self.stopped = False

    def start(self):
    # Start the thread that reads frames from the video stream
        Thread(target=self.update,args=()).start()
        return self

    def update(self):
        # Keep looping indefinitely until the thread is stopped
        while True:
            # If the camera is stopped, stop the thread
            if self.stopped:
                # Close camera resources
                self.stream.release()
                return

            # Otherwise, grab the next frame from the stream
            (self.grabbed, self.frame) = self.stream.read()

    def read(self):
    # Return the most recent frame
        return self.frame

    def stop(self):
    # Indicate that the camera and thread should be stopped
        self.stopped = True

# Define and parse input arguments
parser = argparse.ArgumentParser()
parser.add_argument('--modeldir', help='Folder the .tflite file is located in',
                    required=True)
parser.add_argument('--graph', help='Name of the .tflite file, if different than detect.tflite',
                    default='detect.tflite')
parser.add_argument('--labels', help='Name of the labelmap file, if different than labelmap.txt',
                    default='labelmap.txt')
parser.add_argument('--threshold', help='Minimum confidence threshold for displaying detected objects',
                    default=0.5)
parser.add_argument('--resolution', help='Desired webcam resolution in WxH. If the webcam does not support the resolution entered, errors may occur.',
                    default='1280x720')
parser.add_argument('--edgetpu', help='Use Coral Edge TPU Accelerator to speed up detection',
                    action='store_true')
parser.add_argument('--output_path', help="Where to save processed imges from pi.",
                    required=True)

args = parser.parse_args()

MODEL_NAME = args.modeldir
GRAPH_NAME = args.graph
LABELMAP_NAME = args.labels
min_conf_threshold = float(args.threshold)
resW, resH = args.resolution.split('x')
imW, imH = int(resW), int(resH)
use_TPU = args.edgetpu

# Import TensorFlow libraries
# If tensorflow is not installed, import interpreter from tflite_runtime, else import from regular tensorflow
# If using Coral Edge TPU, import the load_delegate library
pkg = importlib.util.find_spec('tensorflow')
if pkg is None:
    from tflite_runtime.interpreter import Interpreter
    if use_TPU:
        from tflite_runtime.interpreter import load_delegate
else:
    from tensorflow.lite.python.interpreter import Interpreter
    if use_TPU:
        from tensorflow.lite.python.interpreter import load_delegate

# If using Edge TPU, assign filename for Edge TPU model
if use_TPU:
    # If user has specified the name of the .tflite file, use that name, otherwise use default 'edgetpu.tflite'
    if (GRAPH_NAME == 'detect.tflite'):
        GRAPH_NAME = 'edgetpu.tflite'       

# Get path to current working directory
CWD_PATH = os.getcwd()

# Path to .tflite file, which contains the model that is used for object detection
PATH_TO_CKPT = os.path.join(CWD_PATH,MODEL_NAME,GRAPH_NAME)

# Path to label map file
PATH_TO_LABELS = os.path.join(CWD_PATH,MODEL_NAME,LABELMAP_NAME)

# Load the label map
with open(PATH_TO_LABELS, 'r') as f:
    labels = [line.strip() for line in f.readlines()]

# Have to do a weird fix for label map if using the COCO "starter model" from
# https://www.tensorflow.org/lite/models/object_detection/overview
# First label is '???', which has to be removed.
if labels[0] == '???':
    del(labels[0])

# Load the Tensorflow Lite model.
# If using Edge TPU, use special load_delegate argument
if use_TPU:
    interpreter = Interpreter(model_path=PATH_TO_CKPT,
                              experimental_delegates=[load_delegate('libedgetpu.so.1.0')])
    print(PATH_TO_CKPT)
else:
    interpreter = Interpreter(model_path=PATH_TO_CKPT)

interpreter.allocate_tensors()

# Get model details
input_details = interpreter.get_input_details()
output_details = interpreter.get_output_details()
height = input_details[0]['shape'][1]
width = input_details[0]['shape'][2]

led_on = False
floating_model = (input_details[0]['dtype'] == np.float32)

input_mean = 127.5
input_std = 127.5

#make output directory
#os.makedirs(args.output_path, exist_ok=True)

try:
    print("Progam started - waiting for button push...")
    while True:
        if not led_on and  not GPIO.input(17):
            #timestamp an output directory for each capture
            outdir = pathlib.Path(args.output_path) / time.strftime('%Y-%m-%d_%H-%M-%S-%Z')
            outdir.mkdir(parents=True)
            GPIO.output(4, True)
            time.sleep(.1)
            led_on = True
            f = []

            # Initialize frame rate calculation
            frame_rate_calc = 1
            freq = cv2.getTickFrequency()

            # Initialize video stream
            videostream = VideoStream(resolution=(imW,imH),framerate=30).start()
            time.sleep(1)

            #for frame1 in camera.capture_continuous(rawCapture, format="bgr",use_video_port=True):
            while True:

                # Start timer (for calculating frame rate)
                t1 = cv2.getTickCount()

                # Grab frame from video stream
                frame1 = videostream.read()

                # Acquire frame and resize to expected shape [1xHxWx3]
                frame = frame1.copy()
                frame_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
                frame_resized = cv2.resize(frame_rgb, (width, height))
                input_data = np.expand_dims(frame_resized, axis=0)

                # Normalize pixel values if using a floating model (i.e. if model is non-quantized)
                if floating_model:
                    input_data = (np.float32(input_data) - input_mean) / input_std

                # Perform the actual detection by running the model with the image as input
                interpreter.set_tensor(input_details[0]['index'],input_data)
                interpreter.invoke()

                # Retrieve detection results
                boxes = interpreter.get_tensor(output_details[0]['index'])[0] # Bounding box coordinates of detected objects
                classes = interpreter.get_tensor(output_details[1]['index'])[0] # Class index of detected objects
                scores = interpreter.get_tensor(output_details[2]['index'])[0] # Confidence of detected objects
                #num = interpreter.get_tensor(output_details[3]['index'])[0]  # Total number of detected objects (inaccurate and not needed)

                # Loop over all detections and draw detection box if confidence is above minimum threshold
                for i in range(len(scores)):
                    if ((scores[i] > min_conf_threshold) and (scores[i] <= 1.0)):

                        # Get bounding box coordinates and draw box
                        # Interpreter can return coordinates that are outside of image dimensions, need to force them to be within image using max() and min()
                        ymin = int(max(1,(boxes[i][0] * imH)))
                        xmin = int(max(1,(boxes[i][1] * imW)))
                        ymax = int(min(imH,(boxes[i][2] * imH)))
                        xmax = int(min(imW,(boxes[i][3] * imW)))
                        
                        cv2.rectangle(frame, (xmin,ymin), (xmax,ymax), (10, 255, 0), 2)

                        # Draw label
                        object_name = labels[int(classes[i])] # Look up object name from "labels" array using class index
                        label = '%s: %d%%' % (object_name, int(scores[i]*100)) # Example: 'person: 72%'
                        labelSize, baseLine = cv2.getTextSize(label, cv2.FONT_HERSHEY_SIMPLEX, 0.7, 2) # Get font size
                        label_ymin = max(ymin, labelSize[1] + 10) # Make sure not to draw label too close to top of window
                        cv2.rectangle(frame, (xmin, label_ymin-labelSize[1]-10), (xmin+labelSize[0], label_ymin+baseLine-10), (255, 255, 255), cv2.FILLED) # Draw white box to put label text in
                        cv2.putText(frame, label, (xmin, label_ymin-7), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 0), 2) # Draw label text

                # Draw framerate in corner of frame
                cv2.putText(frame,'FPS: {0:.2f}'.format(frame_rate_calc),(30,50),cv2.FONT_HERSHEY_SIMPLEX,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)

                # Calculate framerate
                t2 = cv2.getTickCount()
                time1 = (t2-t1)/freq
                frame_rate_calc= 1/time1
                f.append(frame_rate_calc)

                #path = '/home/pi/tflite1/webcam/' + str(datetime.datetime.now()) + ".jpg"
                path = str(outdir) + '/'  + str(datetime.datetime.now()) + ".jpg"
    
                status = cv2.imwrite(path, frame)


                # Press 'q' to quit
                if cv2.waitKey(1) == ord('q') or led_on and not GPIO.input(17):
                    print(f"Saved images to: {outdir}")
                    GPIO.output(4, False)
                    led_on = False
                    # Clean up
                    cv2.destroyAllWindows()
                    videostream.stop()
                    time.sleep(2)
                    #print(str(sum(f)/len(f)))
                    break
finally:
    GPIO.output(4, False)
    GPIO.cleanup()

    # Clean up
    cv2.destroyAllWindows()
    videostream.stop()
    print(str(sum(f)/len(f)))