From 8ce90346d992374cad3248c9cc34aeba9d55c57d Mon Sep 17 00:00:00 2001
From: Mikayla Dobson <93477693+innocuous-symmetry@users.noreply.github.com>
Date: Wed, 4 Jan 2023 20:24:02 -0600
Subject: [PATCH] init

---
 README.md  |  7 +++++++
 main.py    | 25 +++++++++++++++++++++++++
 predict.py | 19 +++++++++++++++++++
 3 files changed, 51 insertions(+)
 create mode 100644 README.md
 create mode 100644 main.py
 create mode 100644 predict.py

diff --git a/README.md b/README.md
new file mode 100644
index 0000000..a523e55
--- /dev/null
+++ b/README.md
@@ -0,0 +1,7 @@
+# Simple Image Recognition Module
+
+A simple implementation of image recognition software using the pretrained TensorFlow VGG16 model and Python.
+
+## Usage
+
+This tool runs from the command line and expects a single argument referring to a relative path. This program will execute within the provided directory, and will attempt to classify each image in the provided directory.
\ No newline at end of file
diff --git a/main.py b/main.py
new file mode 100644
index 0000000..f7860f5
--- /dev/null
+++ b/main.py
@@ -0,0 +1,25 @@
+# IMAGE RECOGNITION UTIL USING TF/KERAS
+# 
+# most of this application adapted from the following walkthrough:
+# https://towardsdatascience.com/how-to-use-a-pre-trained-model-vgg-for-image-classification-8dd7c4a4a517
+
+import sys, os
+from predict import predict
+from keras.applications.vgg16 import VGG16
+
+# declare model to be used for each prediction
+model = VGG16(weights='imagenet')
+
+# receive directory path as CLI argument and get a list of all files in path
+path = sys.argv[1]
+files = os.listdir(path)
+
+# store all results in one list
+all_results = []
+
+# for each file in directory, append its prediction result to main list
+for file in files:
+    result = predict(model, file)
+    all_results.append({ path: file, result: result })
+
+print(all_results)
diff --git a/predict.py b/predict.py
new file mode 100644
index 0000000..44ed1fb
--- /dev/null
+++ b/predict.py
@@ -0,0 +1,19 @@
+import numpy as np
+from keras.utils import load_img, img_to_array
+from keras.applications.vgg16 import preprocess_input, decode_predictions
+
+def predict(model, path):
+    # receive image path as CLI argument
+    img = load_img(path ,color_mode='rgb', target_size=(224, 224))
+
+    # loaded image to np array for model to read
+    x = img_to_array(img)
+    x.shape
+    x = np.expand_dims(x, axis=0)
+
+    # process array and make predictions
+    x = preprocess_input(x)
+    features = model.predict(x)
+    p = decode_predictions(features)
+
+    return p