Added EDI

app.py

@@ -1,6 +1,8 @@
 import gradio as gr
 from PIL import Image
 import os
+import numpy as np
+import cv2
 
 # Existing imports
 from models.lancros_interpolation import upsample_lancros
@@ -12,18 +14,64 @@ from models.sr_gan import srgan_upscale
 # ✅ New import for Random Forest Super Resolution
 from CV_Project.models.random_forest_sr import random_forest_upscale
 
+# ✅ EDI (Edge Directed Interpolation) method
+def edge_directed_interpolation(lr_img_pil, scale=2):
+    # Convert to grayscale numpy array
+    lr_img = np.array(lr_img_pil.convert("L"))
+    h, w = lr_img.shape
+    hr_h, hr_w = h * scale, w * scale
+
+    hr_img = np.zeros((hr_h, hr_w), dtype=np.uint8)
+
+    # Copy original pixels to even positions
+    for i in range(h):
+        for j in range(w):
+            hr_img[i * scale, j * scale] = lr_img[i, j]
+
+    # Interpolate diagonal pixels
+    for i in range(0, hr_h, scale):
+        for j in range(0, hr_w, scale):
+            if i + scale < hr_h and j + scale < hr_w:
+                p1 = hr_img[i, j]
+                p2 = hr_img[i, j + scale]
+                p3 = hr_img[i + scale, j]
+                p4 = hr_img[i + scale, j + scale]
+
+                d1 = abs(int(p1) - int(p4))
+                d2 = abs(int(p2) - int(p3))
+
+                interp = (int(p1) + int(p4)) // 2 if d1 < d2 else (int(p2) + int(p3)) // 2
+                hr_img[i + scale // 2, j + scale // 2] = interp
+
+    # Fill remaining zero pixels
+    for i in range(0, hr_h):
+        for j in range(0, hr_w):
+            if hr_img[i, j] == 0:
+                neighbors = []
+                if i - 1 >= 0:
+                    neighbors.append(hr_img[i - 1, j])
+                if i + 1 < hr_h:
+                    neighbors.append(hr_img[i + 1, j])
+                if j - 1 >= 0:
+                    neighbors.append(hr_img[i, j - 1])
+                if j + 1 < hr_w:
+                    neighbors.append(hr_img[i, j + 1])
+                if neighbors:
+                    hr_img[i, j] = np.mean(neighbors).astype(np.uint8)
+
+    return Image.fromarray(hr_img)
+
+# === Interfaces === #
 lancros_page = gr.Interface(
     fn=upsample_lancros,
-    inputs=[gr.Image(label="Low Resolution Image"),
-            gr.Slider(2, 6, step=1, value=2, label="Upscaling Factor"),],
+    inputs=[gr.Image(label="Low Resolution Image"), gr.Slider(2, 6, step=1, value=2, label="Upscaling Factor")],
     outputs=gr.Image(type="pil", label="High Resolution Images"),
     title="Lancros Upsampling"
 )
 
 fourier_page = gr.Interface(
     fn=fourier_upscale,
-    inputs=[gr.Image(label="Low Resolution Image"),
-            gr.Slider(2, 6, step=1, value=2, label="Upscaling Factor"),],
+    inputs=[gr.Image(label="Low Resolution Image"), gr.Slider(2, 6, step=1, value=2, label="Upscaling Factor")],
     outputs=gr.Image(type="pil", label="High Resolution Images"),
     title="Fourier Upsampling"
 )
@@ -49,7 +97,6 @@ srgan_page = gr.Interface(
     title="GAN based Super Resolution"
 )
 
-# ✅ Random Forest page
 random_forest_page = gr.Interface(
     fn=random_forest_upscale,
     inputs=[gr.Image(label="Low Resolution Image")],
@@ -57,9 +104,19 @@ random_forest_page = gr.Interface(
     title="Random Forest based Super Resolution"
 )
 
+# ✅ EDI Page
+edi_page = gr.Interface(
+    fn=edge_directed_interpolation,
+    inputs=[gr.Image(label="Low Resolution Image"), gr.Slider(2, 4, step=1, value=2, label="Upscaling Factor")],
+    outputs=gr.Image(type="pil", label="High Resolution Image"),
+    title="Edge Directed Interpolation"
+)
+
+# === Tabs === #
 demo = gr.TabbedInterface(
-    [lancros_page, fourier_page, autoencoder_page, srgan_page,espcn_page,random_forest_page],
-    ["Lancros Interpolation", "Fourier Interpolation", "Autoencoder based Super Resolution", "GAN based Super Resolution","EspCN Super Resolution","Random Forest based Super Resolution"],
+    [lancros_page, fourier_page, autoencoder_page, srgan_page, espcn_page, random_forest_page, edi_page],
+    ["Lancros Interpolation", "Fourier Interpolation", "Autoencoder based Super Resolution", "GAN based Super Resolution",
+     "EspCN Super Resolution", "Random Forest based Super Resolution", "Edge Directed Interpolation"],
     title="Image Super Resolution"
 )
 

models/edge_directed_interpolation.py

@@ -0,0 +1,49 @@
+import numpy as np
+import cv2
+
+def edge_directed_interpolation(lr_img_pil, scale=2):
+    lr_img = np.array(lr_img_pil.convert("L"))
+    h, w = lr_img.shape
+    hr_h, hr_w = h * scale, w * scale
+
+    hr_img = np.zeros((hr_h, hr_w), dtype=np.uint8)
+
+    for i in range(h):
+        for j in range(w):
+            hr_img[i * scale, j * scale] = lr_img[i, j]
+
+    for i in range(0, hr_h, scale):
+        for j in range(0, hr_w, scale):
+            if i + scale < hr_h and j + scale < hr_w:
+                p1 = hr_img[i, j]
+                p2 = hr_img[i, j + scale]
+                p3 = hr_img[i + scale, j]
+                p4 = hr_img[i + scale, j + scale]
+
+                d1 = abs(int(p1) - int(p4))
+                d2 = abs(int(p2) - int(p3))
+
+                if d1 < d2:
+                    interp = (int(p1) + int(p4)) // 2
+                else:
+                    interp = (int(p2) + int(p3)) // 2
+
+                hr_img[i + scale // 2, j + scale // 2] = interp
+
+    for i in range(hr_h):
+        for j in range(hr_w):
+            if hr_img[i, j] == 0:
+                neighbors = []
+                if i - 1 >= 0:
+                    neighbors.append(hr_img[i - 1, j])
+                if i + 1 < hr_h:
+                    neighbors.append(hr_img[i + 1, j])
+                if j - 1 >= 0:
+                    neighbors.append(hr_img[i, j - 1])
+                if j + 1 < hr_w:
+                    neighbors.append(hr_img[i, j + 1])
+                if neighbors:
+                    hr_img[i, j] = np.mean(neighbors).astype(np.uint8)
+
+    hr_img_pil = Image.fromarray(hr_img)
+    return hr_img_pil