Update lpb

a1/.gitignore

@@ -1,2 +1,3 @@
-flake.lock
 ears/**
+pred/**
+__pycache__/**

a1/dataloader.py

@@ -47,7 +47,6 @@ class EarDataClass():
         label = self.labels[idx]
         image = cv2.imread(image_path)
         image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
-        image = image.astype('float32') / 255.0
         return image, label
 
     def __len__(self):

a1/flake.lock

@@ -0,0 +1,61 @@
+{
+  "nodes": {
+    "flake-utils": {
+      "inputs": {
+        "systems": "systems"
+      },
+      "locked": {
+        "lastModified": 1694529238,
+        "narHash": "sha256-zsNZZGTGnMOf9YpHKJqMSsa0dXbfmxeoJ7xHlrt+xmY=",
+        "owner": "numtide",
+        "repo": "flake-utils",
+        "rev": "ff7b65b44d01cf9ba6a71320833626af21126384",
+        "type": "github"
+      },
+      "original": {
+        "owner": "numtide",
+        "repo": "flake-utils",
+        "type": "github"
+      }
+    },
+    "nixpkgs": {
+      "locked": {
+        "lastModified": 1699169573,
+        "narHash": "sha256-cvUb1xZkvOp3W2SzylStrTirhVd9zCeo5utJl9nSIhw=",
+        "owner": "NixOS",
+        "repo": "nixpkgs",
+        "rev": "aeefe2054617cae501809b82b44a8e8f7be7cc4b",
+        "type": "github"
+      },
+      "original": {
+        "owner": "NixOS",
+        "ref": "nixos-23.05",
+        "repo": "nixpkgs",
+        "type": "github"
+      }
+    },
+    "root": {
+      "inputs": {
+        "flake-utils": "flake-utils",
+        "nixpkgs": "nixpkgs"
+      }
+    },
+    "systems": {
+      "locked": {
+        "lastModified": 1681028828,
+        "narHash": "sha256-Vy1rq5AaRuLzOxct8nz4T6wlgyUR7zLU309k9mBC768=",
+        "owner": "nix-systems",
+        "repo": "default",
+        "rev": "da67096a3b9bf56a91d16901293e51ba5b49a27e",
+        "type": "github"
+      },
+      "original": {
+        "owner": "nix-systems",
+        "repo": "default",
+        "type": "github"
+      }
+    }
+  },
+  "root": "root",
+  "version": 7
+}

a1/flake.nix

@@ -1,5 +1,5 @@
 {
-  description = "Python development environment with Matplotlib and OpenCV";
+  description = "Python development environment";
 
   inputs = {
     nixpkgs.url = "github:NixOS/nixpkgs/nixos-23.05";
@@ -18,6 +18,8 @@
           opencv4
           pip
           pillow
+          tqdm
+          numpy
         ]);
       in {
         devShells.default = pkgs.mkShell {

a1/pipeline.py

@@ -0,0 +1,102 @@
+import cv2
+from dataloader import EarDataClass
+import os
+from tqdm import tqdm
+import numpy as np
+
+
+class ViolaJones:
+    def __init__(
+        self,
+        left_ear_cascade="haarcascade_mcs_leftear.xml",
+        right_ear_cascade="haarcascade_mcs_rightear.xml",
+        scale_factor=1.05,  # how much is image reduced at each image scale
+        min_neighbors=2,  # How many candidates to classify as square
+    ):
+        self.left_ear_cascade = cv2.CascadeClassifier(left_ear_cascade)
+        self.right_ear_cascade = cv2.CascadeClassifier(right_ear_cascade)
+        self.scale_factor = scale_factor
+        self.min_neighbors = min_neighbors
+
+    def _extract_ear(self, image, ear):
+        x, y, w, h = ear
+        return image[y : y + h, x : x + w]
+
+    def forward(self, image):
+        left_ears = self.left_ear_cascade.detectMultiScale(
+            image, self.scale_factor, self.min_neighbors
+        )
+        right_ears = self.right_ear_cascade.detectMultiScale(
+            image, self.scale_factor, self.min_neighbors
+        )
+
+        if len(left_ears) > 0:
+            # Crop out the region of interest
+            return left_ears[0], self._extract_ear(image, left_ears[0])
+        elif len(right_ears) > 0:
+            return right_ears[0], self._extract_ear(image, right_ears[0])
+        else:
+            return None, None
+
+
+class LBP:
+    def __init__(self, P=8, R=1):
+        self.P = P
+        self.R = R
+        pass
+
+    def _bilinear_interpolation(self, image, x, y):
+        x1, y1 = int(x), int(y)
+        x2, y2 = min(x1 + 1, image.shape[1] - 1), min(y1 + 1, image.shape[0] - 1)
+
+        f11 = image[y1, x1]
+        f12 = image[y1, x2]
+        f21 = image[y2, x1]
+        f22 = image[y2, x2]
+
+        return (
+            f11 * (x2 - x) * (y2 - y)
+            + f21 * (x - x1) * (y2 - y)
+            + f12 * (x2 - x) * (y - y1)
+            + f22 * (x - x1) * (y - y1)
+        )
+
+    def forward(self, img):
+        gray_image = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+        lbp = np.zeros_like(gray_image)
+        for i in range(self.R, gray_image.shape[0] - self.R):
+            for j in range(self.R, gray_image.shape[1] - self.R):
+                center = gray_image[i, j]
+                lbp_code = 0
+                for p in range(self.P):
+                    # Angle in radians
+                    theta = 2 * np.pi * p / self.P
+                    x = j + self.R * np.cos(theta)
+                    y = i - self.R * np.sin(
+                        theta
+                    )  # y coordinates go from top to bottom
+                    pixel_value = self._bilinear_interpolation(gray_image, x, y)
+                    lbp_code |= (1 << p) if pixel_value >= center else 0
+                lbp[i, j] = lbp_code
+        return lbp
+
+
+def main():
+    dat = EarDataClass(root_dir="./ears", annot_file="identites.txt", mode="train")
+    vj = ViolaJones()
+    lbp = LBP()
+    for i in tqdm(range(len(dat))):
+        img, label = dat[i]
+        ear_lab, ear_img = vj.forward(img)
+        if ear_lab is not None:
+            # Save the image
+            print(lbp.forward(ear_img).shape)
+            os.makedirs("./pred", exist_ok=True)
+            # Save the image
+            cv2.imwrite(f"./pred/{i}.jpg", ear_img)
+
+    pass
+
+
+if __name__ == "__main__":
+    main()