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Added tflite model & tflite python script

master
Samuel ORTION 1 year ago
parent
commit
41e2802aa0
  1. BIN
      models/converted_model.tflite
  2. 66
      src/run_tf_lite_classify_bird_pict.py

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models/converted_model.tflite

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src/run_tf_lite_classify_bird_pict.py

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#!/usr/bin/python
"""
Ressources :
* https://github.com/tensorflow/examples/tree/master/lite/examples/image_classification/raspberry_pi
"""
import tflite_runtime.interpreter as tflite
import argparse
import glob
import time
from PIL import Image
def set_input_tensor(interpreter, image):
tensor_index = interpreter.get_input_details()[0]['index']
input_tensor = interpreter.tensor(tensor_index)()[0]
input_tensor[:, :] = image
def classify_image(interpreter, image):
"""Returns a sorted array of classification results."""
set_input_tensor(interpreter, image)
interpreter.invoke()
output_details = interpreter.get_output_details()[0]
output = np.squeeze(interpreter.get_tensor(output_details['index']))
# If the model is quantized (uint8 data), then dequantize the results
if output_details['dtype'] == np.uint8:
scale, zero_point = output_details['quantization']
output = scale * (output - zero_point)
ordered = np.argpartition(-output, top_k)
return [(i, output[i]) for i in ordered[:top_k]]
def main():
parser = argparse.ArgumentParser(
formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument(
'--model', help='File path of .tflite file.', required=True)
# parser.add_argument(
# '--labels', help='File path of labels file.', required=True)
args = parser.parse_args()
interpreter = tflite.Interpreter(args.model)
interpreter.allocate_tensors()
_, height, width, _ = interpreter.get_input_details()[0]['shape']
CAPTURES_DIR = "/home/pi/captures"
image_paths = glob.glob(CAPTURES_DIR + "*.jpg")
for image_path in image_paths:
im = Image.open(image_path)
im = im.resize((width, height))
start_time = time.time()
results = classify_image(interpreter, im)
elapsed_time = (time.time() - start_time) * 1000
label_id, prob = results[0]
print(image_path)
print('-' * 20)
print(f"Prediction : {label_id} ({prob}) -- computed in {elapsed_time}ms.")
if __name__ == "__main__":
main()
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