Create app.py

app.py

@@ -0,0 +1,51 @@
+import numpy as np
+import matplotlib.pyplot as plt
+import pywt
+import gradio as gr
+
+LEVEL_THRESHOLDS = {
+    1: 20,
+    2: 40,
+    3: 60,
+    4: 80
+}
+
+def threshold_single_level(coeffs_level, threshold):
+    LH, HL, HH = coeffs_level
+    new_LH = np.where(np.abs(LH) < threshold, 0, LH)
+    new_HL = np.where(np.abs(HL) < threshold, 0, HL)
+    new_HH = np.where(np.abs(HH) < threshold, 0, HH)
+    return (new_LH, new_HL, new_HH)
+
+def wavelet_compress(img, level):
+    if img.ndim == 3:
+        img = img[..., :3]
+        img = np.dot(img, [0.299, 0.587, 0.114])
+    img = img.astype(np.uint8)
+    h, w = img.shape
+    threshold = LEVEL_THRESHOLDS[level]
+    coeffs = pywt.wavedec2(img, "haar", level=level)
+    new_coeffs = [coeffs[0]]
+    for level_tuple in coeffs[1:]:
+        new_tuple = threshold_single_level(level_tuple, threshold)
+        new_coeffs.append(new_tuple)
+    recon = pywt.waverec2(new_coeffs, "haar")
+    recon = recon[:h, :w]
+    recon = np.clip(recon, 0, 255).astype(np.uint8)
+    return recon
+
+def interface(img, level):
+    return wavelet_compress(img, level)
+
+demo = gr.Interface(
+    fn=interface,
+    inputs=[
+        gr.Image(type="numpy", label="Upload Image"),
+        gr.Slider(1, 4, step=1, value=1, label="Wavelet Level")
+    ],
+    outputs=gr.Image(label="Reconstructed Image"),
+    title="Wavelet Image Compression (A-Version)",
+    description="Higher wavelet levels use larger thresholds, resulting in stronger distortion."
+)
+
+demo.launch()