######## Picamera Object Detection Using Tensorflow Classifier #########

#

# Author: Evan Juras

# Date: 4/15/18

# Description:

# This program uses a TensorFlow classifier to perform object detection.

# It loads the classifier uses it to perform object detection on a Picamera feed.

# It draws boxes and scores around the objects of interest in each frame from

# the Picamera. It also can be used with a webcam by adding "--usbcam"

# when executing this script from the terminal.

## Some of the code is copied from Google's example at

## https://github.com/tensorflow/models/blob/master/research/object_detection/object_detection_tutorial.ipynb

## and some is copied from Dat Tran's example at

## https://github.com/datitran/object_detector_app/blob/master/object_detection_app.py

## but I changed it to make it more understandable to me.

# Import packages

import os

import cv2

import numpy as np

from picamera.array import PiRGBArray

from picamera import PiCamera

import tensorflow as tf

import argparse

import sys

from multiprocessing import Process

# Set up camera constants

IM_WIDTH = 1280

IM_HEIGHT = 720

#IM_WIDTH = 640 Use smaller resolution for

#IM_HEIGHT = 480 slightly faster framerate

# Select camera type (if user enters --usbcam when calling this script,

# a USB webcam will be used)

camera_type = 'picamera'

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'

# This is needed since the working directory is the object_detection folder.

sys.path.append('..')

# Import utilites

from utils import label_map_util

from utils import visualization_utils as vis_util

# Name of the directory containing the object detection module we're using

MODEL_NAME = 'ssdlite_mobilenet_v2_coco_2018_05_09'

# 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 frame rate calculation

frame_rate_calc = 1

freq = cv2.getTickFrequency()

font = cv2.FONT_HERSHEY_SIMPLEX

def classify_frame(detection_boxes, detection_scores, detection_classes, num_detections, image_tensor, frame_expanded):

#print("finished")

# 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.

# I know this is ugly, but I basically copy+pasted the code for the object

# detection loop twice, and made one work for Picamera and the other work

# for USB.

### Picamera ###

if camera_type == 'picamera':

# Initialize Picamera and grab reference to the raw capture

camera = PiCamera()

camera.resolution = (IM_WIDTH,IM_HEIGHT)

camera.framerate = 10

rawCapture = PiRGBArray(camera, size=(IM_WIDTH,IM_HEIGHT))

rawCapture.truncate(0)

p=None

firstTime = True

counter =0

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

counter = counter+1

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

frame = np.copy(frame1.array)

frame.setflags(write=1)

frame_expanded = np.expand_dims(frame, axis=0)

gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)

if counter%5 is 0:

if p is None:

p = Process(target=classify_frame, args=(detection_boxes, detection_scores, detection_classes, num_detections,image_tensor, frame_expanded,))

p.start()

#print("get in")

if not p.isAlive():

p.join()

print("finishes")

#print(finalBoxes)

#print(boxes

p = Process(target=classify_frame, args=(detection_boxes, detection_scores, detection_classes, num_detections,image_tensor, frame_expanded,))

p.start()

firstTime = False

if not firstTime:

vis_util.visualize_boxes_and_labels_on_image_array(

frame,

np.squeeze(finalBoxes),

np.squeeze(finalClasses).astype(np.int32),

np.squeeze(finalScores),

category_index,

use_normalized_coordinates=True,

line_thickness=8,

min_score_thresh=0.40)

t2 = cv2.getTickCount()

time1 = (t2-t1)/freq

frame_rate_calc = 1/time1

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)

# Press 'q' to quit

if cv2.waitKey(1) == ord('q'):

break

rawCapture.truncate(0)

#print("gets here")

camera.close()

### USB webcam ###

elif camera_type == 'usb':

# Initialize USB webcam feed

camera = cv2.VideoCapture(0)

ret = camera.set(3,IM_WIDTH)

ret = camera.set(4,IM_HEIGHT)

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()

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=0.85)

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)

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()