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

#

# Author dhq # Date: 9/12/18

# removed usb camera

# 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.

## Based on code from Evan Juras # Date: 4/15/1

## 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

# 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

# Set up camera constants

IM_WIDTH = 160

IM_HEIGHT = 128

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

sys.path.append('research')

# Import utilites

#from utils import label_map_util

#from utils import visualization_utils as vis_util

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 = '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('research/object_detection',MODEL_NAME,'frozen_inference_graph.pb')

# Path to label map file

PATH_TO_LABELS = os.path.join('research/object_detection','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

# Initialize camera and perform object detection.

# 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)

import time

import pygame

import pygame.camera

import pygame.image

from datetime import datetime, timedelta

resolution=(IM_WIDTH,IM_HEIGHT)

pygame.init()

pygame.camera.init()

l= pygame.camera.list_cameras()

print('cameras',l)

camera=pygame.camera.Camera(l[0],resolution,'RGB')

camera.start()

framerate=10

frame=None

on= True

image_d = 3

print('WebcamVideoStream loaded.. .warning camera')

time.sleep(2)

#To save video file

#path = ('/home/pi/Documents/Obje.avi')

#fourcc = cv2.VideoWriter_fourcc(*'XVID')

#video_writer = cv2.VideoWriter(path,-1, 1, (160,128), True)

# if not video_writer :

# print ("!!! Failed VideoWriter: invalid parameters")

# sys.exit(1)

#camera.start_recording('/home/pi/Documents/Obje.h264')

while on:

start=datetime.now()

if camera.query_image():

snapshot= camera.get_image()

snapshot1=pygame.transform.scale(snapshot, resolution)

frame=pygame.surfarray.pixels3d(pygame.transform.rotate(pygame.transform.flip(snapshot1, True, False), 90))

if image_d ==1:

frame = rgb2gray(frame)

#for frame1 in camera.capture_continuous(rawCapture, format="bgr",use_video_port=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

# frame = frame1.array

# frame.setflags(write=1)

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)

#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)

#video_writer.write(frame)

t2 = cv2.getTickCount()

time1 = (t2-t1)/freq

frame_rate_calc = 1/time1

# Press 'q' to quit

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

break

on= False

# rawCapture.truncate(0)

#camera.close()

#camera.stop_recording()

# stop = datetime.now()

# s=1 / framerate - (stop - start).total_seconds()

# if s > 0:

# time.sleep(s)

camera.stop()

cv2.destroyAllWindows()

#video_writer.release()