From bbcff345eb34e6156b1b44e5550c5ba11b4e5044 Mon Sep 17 00:00:00 2001
From: Jayasahan Hansana <34449936+jayasahan@users.noreply.github.com>
Date: Wed, 8 Oct 2025 00:35:04 +0530
Subject: [PATCH] Create project.py in Computer vision

Created a new folder for expansion
Computer Vision

added a simple image sorter project
---
 .../Image Sorter/project.py                   | 69 +++++++++++++++++++
 1 file changed, 69 insertions(+)
 create mode 100644 Computer Vision Projects/Image Sorter/project.py

diff --git a/Computer Vision Projects/Image Sorter/project.py b/Computer Vision Projects/Image Sorter/project.py
new file mode 100644
index 0000000..511aa11
--- /dev/null
+++ b/Computer Vision Projects/Image Sorter/project.py	
@@ -0,0 +1,69 @@
+import os
+import shutil
+import numpy as np
+import matplotlib.pyplot as plt
+from tensorflow.keras.datasets import cifar10
+from tensorflow.keras.models import Sequential
+from tensorflow.keras.layers import Conv2D, MaxPooling2D, Flatten, Dense
+from tensorflow.keras.utils import to_categorical
+from PIL import Image
+
+# Class names for CIFAR-10
+class_names = [
+    "airplane", "automobile", "bird", "cat", "deer",
+    "dog", "frog", "horse", "ship", "truck"
+]
+
+# 1. Load the CIFAR-10 dataset (50,000 training, 10,000 test images)
+(x_train, y_train), (x_test, y_test) = cifar10.load_data()
+
+# Normalize the data (pixel values between 0 and 1)
+x_train = x_train.astype("float32") / 255.0
+x_test = x_test.astype("float32") / 255.0
+
+# One-hot encode the labels
+y_train_cat = to_categorical(y_train, 10)
+y_test_cat = to_categorical(y_test, 10)
+
+# 2. Build a simple CNN model
+model = Sequential([
+    Conv2D(32, (3, 3), activation="relu", input_shape=(32, 32, 3)),
+    MaxPooling2D((2, 2)),
+    Conv2D(64, (3, 3), activation="relu"),
+    MaxPooling2D((2, 2)),
+    Flatten(),
+    Dense(64, activation="relu"),
+    Dense(10, activation="softmax")  # 10 classes
+])
+
+# 3. Compile the model
+model.compile(optimizer="adam", loss="categorical_crossentropy", metrics=["accuracy"])
+
+# 4. Train the model (just 3 epochs to keep it quick)
+model.fit(x_train, y_train_cat, epochs=3, validation_split=0.2)
+
+# 5. Predict on test data
+predictions = model.predict(x_test)
+
+# 6. Create output directories for each class
+output_dir = "sorted_images"
+if os.path.exists(output_dir):
+    shutil.rmtree(output_dir)
+os.makedirs(output_dir)
+
+for class_name in class_names:
+    os.makedirs(os.path.join(output_dir, class_name))
+
+# 7. Save first 100 images into their predicted folders
+for i in range(100):
+    img_array = x_test[i]
+    prediction = np.argmax(predictions[i])
+    predicted_class = class_names[prediction]
+
+    # Convert from array to image and scale back to 0-255
+    img = Image.fromarray((img_array * 255).astype("uint8"))
+    img_path = os.path.join(output_dir, predicted_class, f"img_{i}.png")
+    img.save(img_path)
+
+print("✅ Done! Images saved in 'sorted_images/' folder.")
+