color histogram

.vscode/settings.json

+{
+    "git.ignoreLimitWarning": true
+}
\ No newline at end of file

ML/.ipynb_checkpoints/fruit_recognition-checkpoint.ipynb

@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -22,7 +22,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -35,6 +35,30 @@
     "            cells_per_block=(1, 1), visualize=True, multichannel=False)\n",
     "    return ftr"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def FtrExtractColorHist(img):\n",
+    "    chans = cv2.split(img)\n",
+    "    colors = (\"h\", \"s\", \"v\")\n",
+    "    features = []\n",
+    "\n",
+    "    for (chan, color) in zip(chans, colors):\n",
+    "        hist = cv2.calcHist([chan], [0], None, [256], [0, 256])\n",
+    "        features.extend(hist)\n",
+    "    return np.array(features).flatten()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {

ML/fruit_recognition.ipynb

@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -22,7 +22,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -35,6 +35,30 @@
     "            cells_per_block=(1, 1), visualize=True, multichannel=False)\n",
     "    return ftr"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def FtrExtractColorHist(img):\n",
+    "    chans = cv2.split(img)\n",
+    "    colors = (\"h\", \"s\", \"v\")\n",
+    "    features = []\n",
+    "\n",
+    "    for (chan, color) in zip(chans, colors):\n",
+    "        hist = cv2.calcHist([chan], [0], None, [256], [0, 256])\n",
+    "        features.extend(hist)\n",
+    "    return np.array(features).flatten()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {