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March 3, 2020 15:19
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# 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 | |
from PIL import Image | |
from PIL import ImageDraw | |
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()] | |
import cv2 | |
import numpy as np | |
cap = cv2.VideoCapture(0) | |
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 cv2pil(image): | |
''' OpenCV型 -> PIL型 ''' | |
new_image = image.copy() | |
if new_image.ndim == 2: # モノクロ | |
pass | |
elif new_image.shape[2] == 3: # カラー | |
new_image = cv2.cvtColor(new_image, cv2.COLOR_BGR2RGB) | |
elif new_image.shape[2] == 4: # 透過 | |
new_image = cv2.cvtColor(new_image, cv2.COLOR_BGRA2RGBA) | |
new_image = Image.fromarray(new_image) | |
return new_image | |
def pil2cv(image): | |
''' PIL型 -> OpenCV型 ''' | |
new_image = np.array(image, dtype=np.uint8) | |
if new_image.ndim == 2: # モノクロ | |
pass | |
elif new_image.shape[2] == 3: # カラー | |
new_image = cv2.cvtColor(new_image, cv2.COLOR_RGB2BGR) | |
elif new_image.shape[2] == 4: # 透過 | |
new_image = cv2.cvtColor(new_image, cv2.COLOR_RGBA2BGRA) | |
return new_image | |
def main(): | |
labels = load_labels("models/coco_labels.txt") | |
interpreter = make_interpreter("models/mobilenet_ssd_v2_coco_quant_postprocess_edgetpu.tflite") | |
interpreter.allocate_tensors() | |
print('----INFERENCE TIME----') | |
print('Note: The first inference is slow because it includes', | |
'loading the model into Edge TPU memory.') | |
while True: | |
ret, frame = cap.read() | |
image = cv2pil(frame) | |
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, 0.4, scale) | |
print('%.2f ms' % (inference_time * 1000)) | |
print('-------RESULTS--------') | |
if not objs: | |
print('No objects detected') | |
for obj in objs: | |
print(labels.get(obj.id, obj.id)) | |
# print(' id: ', obj.id) | |
print('score: ', obj.score) | |
# print(' bbox: ', obj.bbox) | |
image = image.convert('RGB') | |
draw_objects(ImageDraw.Draw(image), objs, labels) | |
# cv2.putText(image, inference_time, (0,50), cv2.FONT_HERSHEY_PLAIN, 3, (0, 255,0), 3, cv2.LINE_AA) | |
cv2.imshow('Frame', pil2cv(image)) | |
k = cv2.waitKey(1) | |
if k == 27: | |
break | |
cap.release() | |
cv2.destroyAllWindows() | |
if __name__ == '__main__': | |
main() |
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