######## Webcam Object Detection Using Tensorflow-trained Classifier #########
#
# Author: Evan Juras
# Date: 10/27/19
# 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.

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

import subprocess
import digitalio
import board
from PIL import Image, ImageDraw, ImageFont
import adafruit_rgb_display.st7789 as st7789

print("classifier started")


# Configuration for CS and DC pins (these are FeatherWing defaults on M0/M4):
cs_pin = digitalio.DigitalInOut(board.CE0)
dc_pin = digitalio.DigitalInOut(board.D25)
reset_pin = None

# Config for display baudrate (default max is 24mhz):
BAUDRATE = 64000000

# Setup SPI bus using hardware SPI:
spi = board.SPI()

# Create the ST7789 display:
disp = st7789.ST7789(
    spi,
    cs=cs_pin,
    dc=dc_pin,
    rst=reset_pin,
    baudrate=BAUDRATE,
    width=135,
    height=240,
    x_offset=53,
    y_offset=40,
)

# Create blank image for drawing.
# Make sure to create image with mode 'RGB' for full color.
height = disp.width  # we swap height/width to rotate it to landscape!
width = disp.height
image = Image.new("RGB", (width, height))
rotation = 90

# Get drawing object to draw on image.
draw = ImageDraw.Draw(image)

# Draw a black filled box to clear the image.
draw.rectangle((0, 0, width, height), outline=0, fill=(0, 0, 0))
disp.image(image, rotation)
# Draw some shapes.
# First define some constants to allow easy resizing of shapes.
padding = -2
top = padding
bottom = height - padding
# Move left to right keeping track of the current x position for drawing shapes.
x = 0


# Alternatively load a TTF font.  Make sure the .ttf font file is in the
# same directory as the python script!
# Some other nice fonts to try: http://www.dafont.com/bitmap.php
font = ImageFont.truetype("/usr/share/fonts/truetype/dejavu/DejaVuSans.ttf", 24)

# Turn on the backlight
backlight = digitalio.DigitalInOut(board.D22)
backlight.switch_to_output()
backlight.value = True


# 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')

args = parser.parse_args()

MODEL_NAME = 'tflite1/Sample_TFLite_model' #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 = True #args.edgetpu

# Import TensorFlow libraries
# If tflite_runtime is 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('tflite_runtime')
if pkg:
    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 = '/home/pi/tflite1/Sample_TFLite_model/edgetpu.tflite'#os.path.join(CWD_PATH,MODEL_NAME,GRAPH_NAME)

#print(PATH_TO_CKPT)


# Path to label map file
PATH_TO_LABELS = '/home/pi/tflite1/Sample_TFLite_model/labelmap.txt'#os.path.join(CWD_PATH,MODEL_NAME,LABELMAP_NAME)

#print(PATH_TO_LABELS)

# 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('USING TPU*******')
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]

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

input_mean = 127.5
input_std = 127.5

# 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)

cmd = "cat /sys/class/thermal/thermal_zone0/temp |  awk '{printf \"%.1f\", $(NF-0) / 1000}'"  # pylint: disable=line-too-long
Temp = (9 * float(subprocess.check_output(cmd, shell=True).decode("utf-8"))/5) + 32

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

    # Draw a black filled box to clear the image.
    draw.rectangle((0, 0, width, height), outline=0, fill=0)
    
    # Start timer (for calculating frame rate)
    t1 = cv2.getTickCount()

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

    #frame1 = cv2.flip(frame1, -1)

    # 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
        
    if (t1%100==0):
        Temp = (9 * float(subprocess.check_output(cmd, shell=True).decode("utf-8"))/5) + 32
        #print(Temp)

    # 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)

    largestsize = 0;
    largestlabel = ""
    largestpercent = 0
    screensize = imH * imW
    # 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
            #print(label,xmax,xmin,ymax,ymin,(xmax-xmin)*(ymax-ymin))
            currentsize = ((xmax-xmin)*(ymax-ymin))
            
            if (currentsize >= largestsize):
                largestlabel = object_name
                largestsize = currentsize
                largestpercent = int(scores[i]*100)
                
                
    #cmd = "cat /sys/class/thermal/thermal_zone0/temp |  awk '{printf \"CPU Temp: %.1f C\", $(NF-0) / 1000}'"  # pylint: disable=line-too-long
    #Temp = subprocess.check_output(cmd, shell=True).decode("utf-8")
    
    accuracylabel = ' %s: %d%%' % (largestlabel, largestpercent) # Example: 'person: 72%'
    screensizelabel = ' %s: %d%%' % ("Size ratio", int(largestsize/screensize*100)) # Example: 'person: 72%'
    fpslabel = ' FPS: {0:.2f}'.format(frame_rate_calc)
    templabel = ' Temp: {0:.2f}F'.format(Temp)    
    #print("largest:", accuracylabel, screensizelabel)
    
    y = top
    draw.text((x, y), accuracylabel, font=font, fill="#FFFFFF")
    y += 30
    draw.text((x, y), screensizelabel, font=font, fill="#FFFF00")
    y += 50
    draw.text((x, y), fpslabel, font=font, fill="#00FF00")   
    y += 30
    draw.text((x, y), templabel, font=font, fill="#FF00FF")

    
        
    # Display image.
    disp.image(image, rotation)
                

    # Draw framerate in corner of frame
    cv2.putText(frame,fpslabel,(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

    # Press 'q' to quit
    if cv2.waitKey(1) == ord('q'):
        
        break

# Clean up

# Draw a black filled box to clear the image.
draw.rectangle((0, 0, width, height), outline=0, fill=0)
# Display image.
disp.image(image, rotation)
cv2.destroyAllWindows()
videostream.stop()
