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104 changes: 97 additions & 7 deletions 2. Training and Detection.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -3160,13 +3160,103 @@
" break"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 11. Real Time Detections from your Webcam"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This part helps you to use your cellphone camera to do the detection\n",
"1-Install IP Webcam on your cellphone from app store\n",
"2-Run the app and copy the IP that is shown on the screen in the \"URL\" section in the cell below.\n",
"3-Run the cell and detect using your cellphone webcam"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"!pip uninstall opencv-python-headless -y"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import requests\n",
"import cv2\n",
"import numpy as np\n",
"import imutils\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"url = \"http://192.168.137.34:8080/shot.jpg\"\n",
"cap = cv2.VideoCapture(0)\n",
"width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))\n",
"height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))\n",
"\n",
"while True:\n",
" img_resp = requests.get(url)\n",
" img_arr = np.array(bytearray(img_resp.content), dtype=np.uint8)\n",
" img = cv2.imdecode(img_arr, -1)\n",
" #img = imutils.resize(img, width=800, height=600)\n",
" #cv2.imshow(\"Android_cam\", img)\n",
" image_np = np.array(img)\n",
" \n",
" input_tensor = tf.convert_to_tensor(np.expand_dims(image_np, 0), dtype=tf.float32)\n",
" detections = detect_fn(input_tensor)\n",
" \n",
" num_detections = int(detections.pop('num_detections'))\n",
" detections = {key: value[0, :num_detections].numpy()\n",
" for key, value in detections.items()}\n",
" detections['num_detections'] = num_detections\n",
"\n",
" # detection_classes should be ints.\n",
" detections['detection_classes'] = detections['detection_classes'].astype(np.int64)\n",
"\n",
" label_id_offset = 1\n",
" image_np_with_detections = image_np.copy()\n",
"\n",
" viz_utils.visualize_boxes_and_labels_on_image_array(\n",
" image_np_with_detections,\n",
" detections['detection_boxes'],\n",
" detections['detection_classes']+label_id_offset,\n",
" detections['detection_scores'],\n",
" category_index,\n",
" use_normalized_coordinates=True,\n",
" max_boxes_to_draw=50,\n",
" min_score_thresh=.8,\n",
" agnostic_mode=False)\n",
"\n",
" cv2.imshow('object detection', cv2.resize(image_np_with_detections, (800, 600)))\n",
" \n",
" if cv2.waitKey(10) & 0xFF == ord('q'):\n",
" cap.release()\n",
" cv2.destroyAllWindows()\n",
" break"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "rzlM4jt0pfDJ"
},
"source": [
"# 10. Freezing the Graph"
"# 12. Freezing the Graph"
]
},
{
Expand Down Expand Up @@ -3435,7 +3525,7 @@
"id": "wTPmdqaXpfDK"
},
"source": [
"# 11. Conversion to TFJS"
"# 13. Conversion to TFJS"
]
},
{
Expand Down Expand Up @@ -3676,7 +3766,7 @@
"id": "VtUw73FHpfDK"
},
"source": [
"# 12. Conversion to TFLite"
"# 14. Conversion to TFLite"
]
},
{
Expand Down Expand Up @@ -4125,7 +4215,7 @@
"id": "5NQqZRdA21Uc"
},
"source": [
"# 13. Zip and Export Models "
"# 15. Zip and Export Models "
]
},
{
Expand Down Expand Up @@ -4163,9 +4253,9 @@
"provenance": []
},
"kernelspec": {
"display_name": "tfod",
"display_name": "tfodj",
"language": "python",
"name": "tfod"
"name": "tfodj"
},
"language_info": {
"codemirror_mode": {
Expand All @@ -4177,7 +4267,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.3"
"version": "3.9.1"
}
},
"nbformat": 4,
Expand Down