Code for sending commands from Kria to the Pet Feeder

app_mt_mod_rtsp_v2.py

'''
Copyright 2020 Xilinx Inc.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
    http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
'''

from ctypes import *
from typing import List
import cv2
import numpy as np
import vart
import os
import xir
import threading, queue
import time
import argparse
import socket

os.environ['OPENCV_FFMPEG_CAPTURE_OPTIONS'] = 'rtsp_transport;udp'

# define a video capture object
vid = cv2.VideoCapture("rtsp://tapoadmin:160816@192.168.1.16/stream1")
cv2.namedWindow("output", cv2.WINDOW_NORMAL)


width = int(vid.get(3))  # float `width`
height = int(vid.get(4))  # float `height`

print("width: ", width)
print("height: ", height)
# # define a video capture object
vid.set(cv2.CAP_PROP_BUFFERSIZE, 10)

FEEDMSG_open = "n\n"
FEEDMSG_close = "p\n"
UDP_IP = "192.168.1.13"
UDP_PORT = 2390

divider = '------------------------------------'

def preprocess_fn(image_path, fix_scale):
    '''
    Image pre-processing.
    Rearranges from BGR to RGB then normalizes to range 0:1
    and then scales by input quantization scaling factor
    input arg: path of image file
    return: numpy array
    '''
    image = cv2.imread(image_path)
    image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
    image = image * (1/255.0) * fix_scale
    image = image.astype(np.int8)
    return image


def get_child_subgraph_dpu(graph: "Graph") -> List["Subgraph"]:
    assert graph is not None, "'graph' should not be None."
    root_subgraph = graph.get_root_subgraph()
    assert (root_subgraph is not None), "Failed to get root subgraph of input Graph object."
    if root_subgraph.is_leaf:
        return []
    child_subgraphs = root_subgraph.toposort_child_subgraph()
    assert child_subgraphs is not None and len(child_subgraphs) > 0
    return [
        cs
        for cs in child_subgraphs
        if cs.has_attr("device") and cs.get_attr("device").upper() == "DPU"
    ]


def runDPU(dpu,q):
    FPSP = 1 / 5
    capture = time.time()
    '''get tensor'''
    inputTensors = dpu.get_input_tensors()
    outputTensors = dpu.get_output_tensors()
    input_fixpos = dpu.get_input_tensors()[0].get_attr("fix_point")
    input_scale = 2**input_fixpos
    input_ndim = tuple(inputTensors[0].dims)
    output_ndim = tuple(outputTensors[0].dims)

    # we can avoid output scaling if use argmax instead of softmax
    #output_fixpos = outputTensors[0].get_attr("fix_point")
    #output_scale = 1 / (2**output_fixpos)

    outputData = []
    outputData.append([np.empty(output_ndim, dtype=np.int8, order="C")])
    inputData = [np.empty(input_ndim, dtype=np.int8, order="C")]
    
    while True:
        if time.time() - capture >= FPSP:
            ret, frame = vid.read()
            # feeder = frame[int(height / 2) - 100:height - 100, int(width / 2):width - 200]
            # feeder = frame[int(height / 4) - 100:height - 100, int(width / 4)-200:width - 100]
            image = cv2.resize(frame, (250, 200), interpolation=cv2.INTER_CUBIC)                # Resize image
            cv2.imshow('output', image)
            # image = image * (1/255.0) * input_scale
            image = image.astype(np.int8)

            imageRun = inputData[0]
            imageRun[0, ...] = image.reshape(input_ndim[1:])
            job_id = dpu.execute_async(inputData,outputData[0])
            dpu.wait(job_id)
            index = 0
            pos = np.argmax(outputData[index][0][0])
            value = outputData[index][0][0][pos]
            q.put_nowait((pos,value))
            capture = time.time()

        if cv2.waitKey(1) & 0xFF == ord('q'):
            break


def app(model):

    g = xir.Graph.deserialize(model)
    subgraphs = get_child_subgraph_dpu(g)
    all_dpu_runners = []
    all_dpu_runners.append(vart.Runner.create_runner(subgraphs[0], "run"))

    '''run a single thread '''
    # t1 = threading.Thread(target=runDPU, args=(i,start,all_dpu_runners[0], in_q))
    q = queue.Queue()
    t1 = threading.Thread(target=runDPU, args=(all_dpu_runners[0],q))
    t1.start()
    # t1.join()

    print("loop to get values")

    sock = socket.socket(socket.AF_INET, socket.SOCK_DGRAM) # UDP
    classes = ['dog','cat']
    feederpos = False
    feedTimer = time.time()
    while True:
        try:
            res = q.get(timeout=2)  # 3s timeout
        except queue.Empty:
            print("Image feed ended")
            break
        except KeyboardInterrupt:
            break
        q.task_done()
        prediction = classes[res[0]]
        threshold = res[1]
        if threshold <= 15:
            # print("not a dog or a cat, th=%d" % threshold)
            continue
        else:
            print("classification as %s" % prediction)
            print("has threshold: %s" % repr(threshold))
            if prediction == "dog":
                continue
            if feederpos == False:
                feed_my_pet(sock, True)
                feederpos = True
                feedTimer = time.time()
            else:
                feedTimer = time.time() # reinitialize the feeder timer

        if feederpos == True:
            if time.time()-feedTimer >= 120.0:
                feederpos = False
                feed_my_pet(sock, False)

    t1.join()
    
    return

def feed_my_pet(sock, state):
    if state is True:
        print("Smart feeder is open for pets")
        sock.sendto(bytes(FEEDMSG_open, "utf-8"), (UDP_IP, UDP_PORT))
    else:
        print("Smart feeder is close for pets")
        sock.sendto(bytes(FEEDMSG_open, "utf-8"), (UDP_IP, UDP_PORT))


# only used if script is run as 'main' from command line
def main():

  # construct the argument parser and parse the arguments
  ap = argparse.ArgumentParser()  
  ap.add_argument('-m', '--model',     type=str, default='customcnn.xmodel', help='Path of xmodel. Default is customcnn.xmodel')

  args = ap.parse_args()  
  
  print ('Command line options:')
  print (' --model     : ', args.model)

  app(args.model)

if __name__ == '__main__':
  main()