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08-TF2 model capturing from camera using openCV - Kria
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''' | |
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 | |
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) | |
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() | |
classes = ['dog','cat'] | |
print("loop to get values") | |
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)) | |
t1.join() | |
return | |
# 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() | |
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