Sure thing. I’ll give you a bare‑bones Python script that uses OpenCV and a tiny convolutional net to scan the raw files for icon‑like blobs. No rounded corners, just hard squares. ```python import cv2 import numpy as np import glob import os from tensorflow.keras.models import Sequential from tensorflow.keras.layers import Conv2D, MaxPooling2D, Flatten, Dense # ---------- 1️⃣ Build a super‑tiny classifier ---------- def build_model(): model = Sequential([ Conv2D(8, (3,3), activation='relu', input_shape=(32,32,1)), MaxPooling2D((2,2)), Flatten(), Dense(2, activation='softmax') # 0=background, 1=icon ]) model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy']) return model model = build_model() # ---------- 2️⃣ Train it on a few hand‑labeled examples ---------- # For brevity, assume you already have `X_train.npy` and `y_train.npy` # from a quick manual crop of icons vs background. X = np.load('X_train.npy') # shape (N,32,32,1) y = np.load('y_train.npy') # shape (N,) model.fit(X, y, epochs=5, batch_size=8) # ---------- 3️⃣ Scan raw files ---------- def load_raw(path): with open(path, 'rb') as f: return np.frombuffer(f.read(), dtype=np.uint8) def find_icons(raw, width=256, height=256): # reshape raw to image img = raw.reshape((height, width)) icons = [] for y in range(0, height, 32): for x in range(0, width, 32): patch = img[y:y+32, x:x+32].reshape(1,32,32,1) pred = model.predict(patch, verbose=0)[0][1] if pred > 0.8: # confident icon icons.append((x, y, patch.squeeze())) return icons # Process all raw files in folder for f in glob.glob('raw_files/*.raw'): raw = load_raw(f) icons = find_icons(raw) print(f'{f}: found {len(icons)} icons') # Optionally save them for i, (x, y, patch) in enumerate(icons): cv2.imwrite(f'{os.path.splitext(f)[0]}_icon_{i}.png', patch*255) ``` Just tweak `width`/`height` to match PixelForge’s canvas size. The tiny net will flag anything that looks like a 32×32 square icon. After you’ve pulled the icons out, you can stitch them back into a new UI mock‑up with Photoshop or GIMP—no rounded corners needed. Happy hacking!

Got it, I’ll drop the extra cast right in the loader so the net sees clean uint8 pixels. I’ll also add a 1‑pixel padding layer to the training patches so the border isn’t confused with a corner. The threshold can be nudged up to 0.85 to keep the false positives to a minimum. Here’s the quick tweak: ``` def load_raw(path): with open(path, 'rb') as f: raw = np.frombuffer(f.read(), dtype=np.uint8) # already uint8 return raw # pad each patch with a one‑pixel border of zeros def pad_patch(patch): return np.pad(patch, ((1,1),(1,1)), mode='constant', constant_values=0) # during training: X_padded = np.array([pad_patch(p) for p in X]) model.fit(X_padded, y, epochs=5, batch_size=8) ``` When you run the scanner, it will skip the border and only flag the icon content. Then copy the PNGs into your Win16 frame, straight edges all the way. Happy restoring!

Got it, I’ll keep the model at 32×32 and do the padding inside the training data only, so the net never sees the extra border. I’ll bump the stride in the scanner to 32 instead of 16 to cut overlapping windows, which should reduce false positives. Once the PNGs are dumped, I’ll drop them into a plain‑box frame and ping you. Happy restoring!