sujoy edge demo (#158)

Edge detection using Sujoy Filter

Author: amarbit

Project.toml

@@ -9,6 +9,7 @@ Documenter = "e30172f5-a6a5-5a46-863b-614d45cd2de4"
 FileIO = "5789e2e9-d7fb-5bc7-8068-2c6fae9b9549"
 FixedPointNumbers = "53c48c17-4a7d-5ca2-90c5-79b7896eea93"
 ImageAxes = "2803e5a7-5153-5ecf-9a86-9b4c37f5f5ac"
+ImageBinarization = "cbc4b850-ae4b-5111-9e64-df94c024a13d"
 ImageContrastAdjustment = "f332f351-ec65-5f6a-b3d1-319c6670881a"
 ImageCore = "a09fc81d-aa75-5fe9-8630-4744c3626534"
 ImageDraw = "4381153b-2b60-58ae-a1ba-fd683676385f"
@@ -40,6 +41,7 @@ Documenter = "0.25"
 FileIO = "1"
 FixedPointNumbers = "0"
 ImageAxes = "0"
+ImageBinarization = "0.2"
 ImageContrastAdjustment = "0.3"
 ImageCore = "0"
 ImageDraw = "0"

docs/examples/contours/sujoy_edge_demo.jl

@@ -0,0 +1,71 @@
+# ---
+# cover: assets/sedge.png
+# title: Edge detection using Sujoy Filter
+# author: Sujoy Kumar Goswami; Partha Ghosh
+# date: 2020-09-22
+# ---
+
+using Images
+using Statistics
+using TestImages
+using ImageBinarization
+
+# Sujoy's Edge Detection Algorithm is a better & more generic approach (first derivative) for edge
+# detection than the other commonly used first-derivative methods (like Robert’s operator, Prewitt
+# operator, Sobel operator etc.).
+
+# [Paper Link](https://www.ijert.org/research/a-better-first-derivative-approach-for-edge-detection-IJERTV2IS110616.pdf)
+
+"""
+    edges = sujoy(img; four_connectivity=true)
+
+Compute edges of an image using the Sujoy algorithm.
+
+## Parameters
+
+* `img` (Required): any gray image
+* `four_connectivity=true`: if true, kernel is based on 4-neighborhood, else, kernel is based on
+   8-neighborhood,
+
+## Returns
+
+* `edges` : gray image
+"""
+function sujoy(img; four_connectivity=true)
+    img_channel = Gray.(img)
+
+    min_val = minimum(img_channel)
+    img_channel = img_channel .- min_val
+    max_val = maximum(img_channel)
+
+    if max_val == 0
+        return img
+    end
+
+    img_channel = img_channel./max_val
+
+    if four_connectivity
+        krnl_h = centered(Gray{Float32}[0 -1 -1 -1 0; 0 -1 -1 -1 0; 0 0 0 0 0; 0 1 1 1 0; 0 1 1 1 0]./12)
+        krnl_v = centered(Gray{Float32}[0 0 0 0 0; -1 -1 0 1 1;-1 -1 0 1 1;-1 -1 0 1 1;0 0 0 0 0 ]./12)
+    else
+        krnl_h = centered(Gray{Float32}[0 0 -1 0 0; 0 -1 -1 -1 0; 0 0 0 0 0; 0 1 1 1 0; 0 0 1 0 0]./8)
+        krnl_v = centered(Gray{Float32}[0 0 0 0 0;  0 -1 0 1 0; -1 -1 0 1 1;0 -1 0 1 0; 0 0 0 0 0 ]./8)
+    end
+
+    grad_h = imfilter(img_channel, krnl_h')
+    grad_v = imfilter(img_channel, krnl_v')
+
+    grad = (grad_h.^2) .+ (grad_v.^2)
+
+    return grad
+end
+
+img = testimage("cameraman")
+img_edge = sujoy(img, four_connectivity=true)
+img_edge₀₁ = binarize(img_edge, Otsu()) # or use other binarization methods provided in ImageBinarization
+
+mosaicview(img, img_edge, img_edge₀₁; nrow = 1)
+
+
+# save covers #src
+save("assets/sedge.png", img_edge) #src