Added tflite model & tflite python script

src/run_tf_lite_classify_bird_pict.py

@@ -0,0 +1,66 @@
+#!/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()