119 lines
3.8 KiB
Python
119 lines
3.8 KiB
Python
# Lint as: python3
|
|
# Copyright 2019 Google LLC
|
|
#
|
|
# 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
|
|
#
|
|
# https://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.
|
|
"""Example using TF Lite to detect objects in a given image."""
|
|
|
|
import argparse
|
|
import time
|
|
import sys
|
|
from PIL import Image
|
|
from PIL import ImageDraw
|
|
from io import BytesIO, StringIO
|
|
import time
|
|
import base64
|
|
import json
|
|
import detect
|
|
import tflite_runtime.interpreter as tflite
|
|
import platform
|
|
|
|
EDGETPU_SHARED_LIB = {
|
|
'Linux': 'libedgetpu.so.1',
|
|
'Darwin': 'libedgetpu.1.dylib',
|
|
'Windows': 'edgetpu.dll'
|
|
}[platform.system()]
|
|
|
|
def load_labels(path, encoding='utf-8'):
|
|
"""Loads labels from file (with or without index numbers).
|
|
|
|
Args:
|
|
path: path to label file.
|
|
encoding: label file encoding.
|
|
Returns:
|
|
Dictionary mapping indices to labels.
|
|
"""
|
|
with open(path, 'r', encoding=encoding) as f:
|
|
lines = f.readlines()
|
|
if not lines:
|
|
return {}
|
|
|
|
if lines[0].split(' ', maxsplit=1)[0].isdigit():
|
|
pairs = [line.split(' ', maxsplit=1) for line in lines]
|
|
return {int(index): label.strip() for index, label in pairs}
|
|
else:
|
|
return {index: line.strip() for index, line in enumerate(lines)}
|
|
|
|
|
|
def make_interpreter(model_file):
|
|
model_file, *device = model_file.split('@')
|
|
return tflite.Interpreter(
|
|
model_path=model_file,
|
|
experimental_delegates=[
|
|
tflite.load_delegate(EDGETPU_SHARED_LIB,
|
|
{'device': device[0]} if device else {})
|
|
])
|
|
|
|
|
|
def draw_objects(draw, objs, labels):
|
|
"""Draws the bounding box and label for each object."""
|
|
for obj in objs:
|
|
bbox = obj.bbox
|
|
draw.rectangle([(bbox.xmin, bbox.ymin), (bbox.xmax, bbox.ymax)],
|
|
outline='red')
|
|
draw.text((bbox.xmin + 10, bbox.ymin + 10),
|
|
'%s\n%.2f' % (labels.get(obj.id, obj.id), obj.score),
|
|
fill='red')
|
|
|
|
|
|
def printInfo(text):
|
|
print(json.dumps({"type": "info", "data": text}))
|
|
|
|
def printError(text):
|
|
print(json.dumps({"type": "error", "data": text}))
|
|
|
|
def printData(array, time):
|
|
print(json.dumps({"type": "data", "data": array, "time": time}))
|
|
|
|
def main():
|
|
labels = load_labels("models/coco_labels.txt")
|
|
interpreter = make_interpreter("models/ssd_mobilenet_v2_coco_quant_postprocess_edgetpu.tflite")
|
|
interpreter.allocate_tensors()
|
|
threshold = 0.4
|
|
printInfo("ready")
|
|
while True:
|
|
line = sys.stdin.readline().rstrip("\n")
|
|
try:
|
|
rawImage = BytesIO(base64.b64decode(line))
|
|
image = Image.open(rawImage)
|
|
scale = detect.set_input(interpreter, image.size,
|
|
lambda size: image.resize(size, Image.ANTIALIAS))
|
|
|
|
start = time.perf_counter()
|
|
interpreter.invoke()
|
|
inference_time = time.perf_counter() - start
|
|
objs = detect.get_output(interpreter, threshold, scale)
|
|
output = []
|
|
for obj in objs:
|
|
label = labels.get(obj.id, obj.id)
|
|
labelID = obj[0]
|
|
score = obj[1]
|
|
bbox = obj[2]
|
|
output.append({"bbox": bbox, "class": label, "score": score})
|
|
printData(output, (inference_time * 1000))
|
|
except Exception as e:
|
|
printError(str(e))
|
|
|
|
|
|
if __name__ == '__main__':
|
|
main()
|