Add MediaPipe face detection back camera model

hollance · Feb 10, 2021 · d2f8c9e · d2f8c9e
1 parent 5af71b6
commit d2f8c9e
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diff --git a/Anchors.ipynb b/Anchors.ipynb
@@ -6,12 +6,24 @@
    "source": [
     "# Create anchor boxes\n",
     "\n",
-    "This is the **SsdAnchorsCalculator** stage from the MediaPipe graph. It computes a list of anchor boxes. This only needs to be done once, so we will store these anchors into a lookup table."
+    "This is the **SsdAnchorsCalculator** stage from the MediaPipe graph. It computes a list of anchor boxes. This only needs to be done once, so we will store these anchors into a lookup table.\n",
+    "\n",
+    "\n",
+    "Using conda environnement:\n",
+    "```\n",
+    "conda create -c pytorch -c conda-forge -n BlazeConv 'pytorch=1.6' jupyter opencv matplotlib\n",
+    "```\n",
+    "```\n",
+    "conda activate BlazeConv\n",
+    "```\n",
+    "```\n",
+    "pip install tflite\n",
+    "```"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -29,7 +41,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -49,6 +61,35 @@
     "}"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "These are the options from [face_detection_back_mobile_gpu.pbtxt](https://github.com/google/mediapipe/blob/master/mediapipe/graphs/face_detection/face_detection_back_mobile_gpu.pbtxt)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "anchor_back_options = {\n",
+    "    \"num_layers\": 4,\n",
+    "    \"min_scale\": 0.15625,\n",
+    "    \"max_scale\": 0.75,\n",
+    "    \"input_size_height\": 256,\n",
+    "    \"input_size_width\": 256,\n",
+    "    \"anchor_offset_x\": 0.5,\n",
+    "    \"anchor_offset_y\": 0.5,\n",
+    "    \"strides\": [16, 32, 32, 32],\n",
+    "    \"aspect_ratios\": [1.0],\n",
+    "    \"reduce_boxes_in_lowest_layer\": False,\n",
+    "    \"interpolated_scale_aspect_ratio\": 1.0,\n",
+    "    \"fixed_anchor_size\": True,\n",
+    "}"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -58,7 +99,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -142,13 +183,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [],
    "source": [
     "anchors = generate_anchors(anchor_options)\n",
     "\n",
-    "assert len(anchors) == 896"
+    "assert len(anchors) == 896\n",
+    "\n",
+    "anchors_back = generate_anchors(anchor_back_options)\n",
+    "\n",
+    "assert len(anchors_back) == 896\n"
    ]
   },
   {
@@ -160,8 +205,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
+   "execution_count": 8,
+   "metadata": {
+    "scrolled": true
+   },
    "outputs": [
     {
      "data": {
@@ -178,7 +225,7 @@
        " [0.28125, 0.03125, 1.0, 1.0]]"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -187,6 +234,35 @@
     "anchors[:10]"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[[0.03125, 0.03125, 1.0, 1.0],\n",
+       " [0.03125, 0.03125, 1.0, 1.0],\n",
+       " [0.09375, 0.03125, 1.0, 1.0],\n",
+       " [0.09375, 0.03125, 1.0, 1.0],\n",
+       " [0.15625, 0.03125, 1.0, 1.0],\n",
+       " [0.15625, 0.03125, 1.0, 1.0],\n",
+       " [0.21875, 0.03125, 1.0, 1.0],\n",
+       " [0.21875, 0.03125, 1.0, 1.0],\n",
+       " [0.28125, 0.03125, 1.0, 1.0],\n",
+       " [0.28125, 0.03125, 1.0, 1.0]]"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "anchors_back[:10]"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -282,11 +358,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [],
    "source": [
-    "np.save(\"anchors.npy\", anchors)"
+    "np.save(\"anchors.npy\", anchors)\n",
+    "np.save(\"anchorsback.npy\", anchors_back)"
    ]
   },
   {
@@ -313,7 +390,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.0"
+   "version": "3.8.5"
   }
  },
  "nbformat": 4,