Upload image_pretrained.py

image_pretrained.py

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+# -*- coding: utf-8 -*-
+"""
+Spyder Editor
+
+Use pretrained model to recognize an image.
+
+"""
+import os
+import numpy as np
+import pandas as pd
+import PIL
+import tensorflow as tf
+from tensorflow.keras.applications import EfficientNetV2S
+from tensorflow.keras.applications import Xception
+from tensorflow.keras.applications import MobileNetV3Small
+
+"""
+model = ResNet50(weights='imagenet')
+
+img_path = 'elephant.jpg'
+img = keras.utils.load_img(img_path, target_size=(224, 224))
+x = keras.utils.img_to_array(img)
+x = np.expand_dims(x, axis=0)
+x = preprocess_input(x)
+
+preds = model.predict(x)
+# decode the results into a list of tuples (class, description, probability)
+# (one such list for each sample in the batch)
+print('Predicted:', decode_predictions(preds, top=3)[0])
+"""
+
+# Take a pretrained model, its preprocess input functions, prediction function,
+# the image to be predicted.
+# Return the decoded prediction.
+
+
+def pretrained_model(image, model_name, preprocess_fn,
+                     decoded_predictions_fn):
+    # preprocess with the model's function
+    x = preprocess_fn(image)
+    # predictions
+    pred = model_name.predict(x)
+    # decode the results into a list of tuples (class, description, probability)
+    decoded_pred = decoded_predictions_fn(pred, top=3)
+    # Must put in the [0] first element of the
+    # decoded predictions.
+    pred_df = pd.DataFrame(
+        decoded_pred[0],
+        columns=["class", "description", "probability"]
+    )
+    return pred_df
+
+# Load the specific image size for the model
+# path = filepath of the image
+# size = size to be loaded
+
+
+def load_and_resize_image(img_path, size=(224, 224, 3)):
+    img = tf.keras.utils.load_img(
+        path=img_path, target_size=size)
+    x = tf.keras.utils.img_to_array(img)
+    x = np.expand_dims(x, axis=0)
+    print(f"Shape of x: {x.shape}")
+
+    return x
+
+
+def predict(img_path):
+    # Set the data directory
+    data_dir = "images"
+    image_list = ["cat.jpg", "mrt_train.jpg",
+                  "apples.jpg", "duck.jpg", "daisy.jpg"]
+
+    # Xception:  (299x299)
+    # EfficientNetV2S: (384, 384)
+    # MobileNetV2: (224, 224, 3)
+    # Load the image
+    # img_path = os.path.join(data_dir, image_list[4])
+    print(f"image path: {img_path}")
+
+    # Efficient Net V2 Small
+    img = load_and_resize_image(
+        img_path,
+        size=(384, 384))
+    eff_net = EfficientNetV2S(include_top=True,
+                              weights='imagenet',
+                              input_shape=(384, 384, 3))
+    pred_df_eff = pretrained_model(
+        img,
+        eff_net,
+        tf.keras.applications.efficientnet_v2.preprocess_input,
+        tf.keras.applications.efficientnet_v2.decode_predictions,
+    )
+    print(pred_df_eff[["description", "probability"]])
+
+    # Mobile Net V3 Small
+    img = load_and_resize_image(img_path, size=(224, 224))
+    mobile_net = MobileNetV3Small(include_top=True,
+                                  weights="imagenet",
+                                  input_shape=(224, 224, 3))
+    pred_df_mob = pretrained_model(
+        img,
+        eff_net,
+        tf.keras.applications.mobilenet_v3.preprocess_input,
+        tf.keras.applications.mobilenet_v3.decode_predictions,
+    )
+    print(pred_df_mob[["description", "probability"]])
+
+    # Xception
+    img = load_and_resize_image(img_path, size=(299, 299))
+    xcept = Xception(include_top=True,
+                     weights="imagenet",
+                     input_shape=(299, 299, 3))
+    pred_df_xcept = pretrained_model(
+        img,
+        xcept,
+        tf.keras.applications.xception.preprocess_input,
+        tf.keras.applications.xception.decode_predictions,
+    )
+    print(pred_df_xcept[["description", "probability"]])
+
+    # Format the output for gradio label
+    dict_eff = dict({
+        "Description": pred_df_eff["description"],
+        "Probability": pred_df_eff["probability"]
+    })
+    dict_mob = dict({
+        "Description": pred_df_mob["description"],
+        "Probability": pred_df_mob["probability"]
+    })
+    dict_xcept = dict({
+        "Description": pred_df_xcept["description"],
+        "Probability": pred_df_xcept["probability"]
+    })
+    return pred_df_eff[["description", "probability"]], \
+        pred_df_mob[["description", "probability"]], \
+        pred_df_xcept[["description", "probability"]]
+
+ # Main
+if __name__ == "__main__":
+    predict()