def main():
parser = argparse.ArgumentParser()
parser.add_argument("--model", help="File path of Tflite model.", required=True)
parser.add_argument(
"--image", help="File path of the image to be recognized.", required=True
)
parser.add_argument(
"--num", help="Number of inference executions.", default=10, type=int
)
args = parser.parse_args()
inference_time = []
img = Image.open(args.image)
for i in range(args.num):
# Initialize engine.
engine = ClassificationEngine(args.model)
# Run inference.
result1 = engine.ClassifyWithImage(img, top_k=3)
# Get Inference time.
inference_time.append(engine.get_inference_time())
# delete Engine
del engine
# Print avg
print("Model inference time avg: {0:.4f}".format(statistics.mean(inference_time)))
def main():
parser = argparse.ArgumentParser()
parser.add_argument("--model1",
help="File path of Tflite model.",
required=True)
parser.add_argument("--model2",
help="File path of Tflite model.",
required=True)
parser.add_argument("--image",
help="File path of the image to be recognized.",
required=True)
parser.add_argument("--num",
help="Number of inference executions.",
default=100,
type=int)
args = parser.parse_args()
# Initialize engine.
engine1 = ClassificationEngine(args.model1)
engine2 = ClassificationEngine(args.model2)
# Run inference.
inference_time1 = []
inference_time2 = []
for i in range(num + 1):
img = Image.open(args.image)
result1 = engine1.ClassifyWithImage(img, top_k=3)
result2 = engine2.ClassifyWithImage(img, top_k=3)
# Get Inference time.
if i > 0:
inference_time1.append(engine1.get_inference_time())
inference_time2.append(engine2.get_inference_time())
# Avg
print("Model1 inference time avg: {0:.4f}".format(
statistics.mean(inference_time1)))
print("Model2 inference time avg: {0:.4f}".format(
statistics.mean(inference_time2)))
for split in ['train', 'validation', 'test']:
for label in ['positive', 'negative']:
data_paths = glob.glob('data/' + split + '/' + label + '/*.png')
predictions['path'].extend(data_paths)
predictions['set'].extend([split] * len(data_paths))
predictions['truth'].extend([int(label == 'positive')] * len(data_paths))
inference_times = {}
model_paths = glob.glob('models/*/model_edgetpu.tflite')
for model_path in model_paths:
classifier = ClassificationEngine(model_path)
model_name = model_path.split('/')[1]
input_shape = [int(dim) for dim in model_name.split('_')[:3]]
predictions[model_name] = []
inference_times[model_name] = []
for path in predictions['path']:
image = Image.open(path)
# Set threshold to smaller than 0 to receive each prediction in range [0, 1]
prediction = classifier.classify_with_image(image, threshold=-1)
inference_time = classifier.get_inference_time()
# Predictions are returned as [(label_id, confidence_score)]
predictions[model_name].append(prediction[0][1].astype(float))
inference_times[model_name].append(inference_time)
with open('results/predictions_edgetpu.json', 'w') as fp:
json.dump(predictions, fp)
with open('results/inference_times_edgetpu.json', 'w') as fp:
json.dump(inference_times, fp)
p = True
camera.start_preview()
while p:
if GPIO.input(23) == GPIO.LOW:
os.system("./jsay.sh \"かしゃ\"")
#camera.capture('./image.jpg')
name = "./image/image" + str(cntr) + ".jpg"
camera.capture(name)
cntr = cntr + 1
img = Image.open(name)
img2 = img.crop((280, 0, 720, 720))
start = time.time()
inf = engine.classify_with_image(img2, top_k=3)
eltime = time.time() - start
iftime = engine.get_inference_time()
print('num i = ', len(inf))
if (len(inf) == 0):
lab2 = u"よくわかりません"
else:
for result in inf:
print('---------------------------')
print(labels[result[0]])
print('Score : ', result[1])
print('elTime : ', eltime)
print('ifTime : ', iftime)
result = inf[0]
if result[1] >= 0.4:
lab2 = u"これは" + labels[result[0]] + "です"
else:
lab2 = u"これはたぶん" + labels[result[0]] + "かもしれません"
frame = imutils.resize(frame, width=500)
orig = frame.copy()
# prepare the image for classification by converting from a NumPy
# array to PIL image format
frame = Image.fromarray(frame)
# make predictions on the input frame
results = model.classify_with_image(frame, top_k=1)
# ensure at least one result was found
if len(results) > 0:
# draw the predicted class label and probability on the
# output frame
(classID, score) = results[0]
text = "{}: {:.2f}% ({:.4f} sec)".format(classNames[classID],
score * 100, model.get_inference_time() / 1000)
cv2.putText(orig, text, (10, 30), cv2.FONT_HERSHEY_SIMPLEX,
0.5, (0, 0, 255), 2)
# show the output frame and wait for a key press
cv2.imshow("Frame", orig)
key = cv2.waitKey(1) & 0xFF
# if the `q` key was pressed, break from the loop
if key == ord("q"):
break
# stop the timer and display FPS information
fps.stop()
print("[INFO] elapsed time: {:.2f}".format(fps.elapsed()))
