{"action":"created","comment":{"url":"https://api.github.com/repos/mvondracek/VUT-FIT-POVa-2018-Pedestrian-Tracking/pulls/comments/245276055","pull_request_review_id":189339343,"id":245276055,"node_id":"MDI0OlB1bGxSZXF1ZXN0UmV2aWV3Q29tbWVudDI0NTI3NjA1NQ==","diff_hunk":"@@ -49,30 +55,232 @@ def detect(self, image, camera: Camera) -> List[PersonView]:\n         return detected\n \n \n-class OpenPoseDetectorUsingPrecompiledBinary(PeopleDetector):\n-    \"\"\"TODO\"\"\"\n-    def __init__(self, binary_path, use_gpu=True, net_resolution='480x240'):\n+class OpenPoseBinaryDetector(PeopleDetector):\n+    \"\"\"\n+    Detection using pre-compiled binary of OpenPose. Releases can be found at:\n+    https://github.com/CMU-Perceptual-Computing-Lab/openpose/releases\n+    \"\"\"\n+    class Models(Enumeration):\n+        coco = 'COCO'\n+        body_25 = 'BODY_25'\n+\n+    models = Models()  # enum for OP models\n+\n+    def __init__(self, binary_path, using_gpu, net_resolution='-1x240', force_op_model=None):\n+        \"\"\"\n+        1) Go to OpenPose releases: https://github.com/CMU-Perceptual-Computing-Lab/openpose/releases\n+        2) Download and extract OpenPose folder (referred as OP_HOME).\n+        3) Run OP_HOME/models/getModels.bat to download all OP models.\n+            [OPTIONAL] edit the getModels.bat to download only needed models.\n+        4) [OPTIONAL] Read https://github.com/CMU-Perceptual-Computing-Lab/openpose/blob/master/doc/quick_start.md\n+        :param binary_path: path to OpenPose binary\n+        :param using_gpu: True to use GPU, False to use CPU. To use GTX GPU, it should support OpenCL 1.2. Install CUDA\n+            drivers from: https://developer.nvidia.com/cuda-downloads\n+            Then follow cuDNN installation guide: https://docs.nvidia.com/deeplearning/sdk/cudnn-install/index.html\n+        :param net_resolution: Multiples of 16, e.g. 320x176. Increase ~ accuracy increase. Decrease ~ speed increase.\n+            For best results, keep the closest aspect ratio possible to the images processed. Using -1 in any of the\n+            dimensions, OP will choose the optimal aspect ratio depending on the input. E.g. the default -1x368 is\n+            equivalent to 656x368 for 16:9 resolutions (full HD 1980x1080, HD 1280x720 etc.). NOTE: Higher resolution\n+            means higher memory consumption (RAM or GPU memory). E.g. -1x336 (for full HD) takes 2 GB of GTX 760 memory.\n+        :param force_op_model: Manually select OpenPose model BODY_25/COCO. By default, OP model is BODY_25 for GPU,\n+        COCO for CPU. Because COCO is ~3x faster on CPU than BODY_25, but BODY_25 is ~40% faster on GPU.\n+            Make sure to have the requested model downloaded in OP_HOME/models folder.\n+        \"\"\"\n         if 'Windows' not in platform.system():\n             raise NotImplementedError(\"Only Windows binaries supported.\")\n \n-        assert os.path.isfile(binary_path), 'OpenPose binary not found. Path: {}'.format(binary_path)\n+        assert os.path.isfile(binary_path), \"OpenPose binary not found. Path: {}\".format(binary_path)\n+        self.binary_home = os.path.dirname(binary_path).rstrip('bin')  # OpenPoseDemo.exe is in bin/ subdirectory\n+\n+        if force_op_model and not self.models.contains(force_op_model):\n+            logger.error(\"Using an unknown OpenPose model: {0}. Select from {1}.models: {2}!\"\n+                         .format(force_op_model, type(self).__name__,  self.models.enum_values()))\n+            raise NotImplementedError(\"Using unknown OpenPose model!\")\n+\n+        # prepare tmp directory for input images and results; tmp dir is deleted in obj destructor\n+        tmp_dir_name = 'POVa_pedestrian_tracking_TEMP_DIR'\n         try:\n-            images_folder = os.path.join(os.environ['TEMP'], 'POVa_pedestrian_tracking', 'images')\n-            results_folder = os.path.join(os.environ['TEMP'], 'POVa_pedestrian_tracking', 'results')\n+            self.tmp_dir = os.path.join(os.environ['TEMP'], tmp_dir_name)\n         except KeyError:\n             logger.warning(\"Environment variable TEMP not found. Creating tmp folder in CWD.\")\n-            images_folder = os.path.join(os.getcwd(), 'POVa_pedestrian_tracking', 'images')\n-            results_folder = os.path.join(os.getcwd(), 'POVa_pedestrian_tracking', 'results')\n+            self.tmp_dir = os.path.join(os.getcwd(), tmp_dir_name)\n \n+        logger.debug('Detector created tmp dir at: {}'.format(self.tmp_dir))\n+        self.images_folder = os.path.join(self.tmp_dir, 'images')\n+        self.results_folder = os.path.join(self.tmp_dir, 'results')\n+        os.makedirs(self.images_folder, exist_ok=True)\n+        os.makedirs(self.results_folder, exist_ok=True)\n+\n+        # create a command to run binary file\n+        # out_res and render_pose disabled intentionally (even if no GUI at all), so they are not calculated -> faster\n         self.cmd = binary_path\n-        self.cmd += ' --image_dir {}'.format(images_folder)  # load images for detection from here\n-        self.cmd += ' --write_json {}'.format(results_folder)  # output JSONs are saved here\n+        self.cmd += ' --image_dir {}'.format(self.images_folder)  # load images for detection from here\n+        self.cmd += ' --write_json {}'.format(self.results_folder)  # output JSONs are saved here\n         self.cmd += ' --net_resolution {}'.format(net_resolution)  # lower means faster, but less accurate\n-        self.cmd += ' --num_gpu 1' if use_gpu else ''  # use only one GPU\n         self.cmd += ' --display 0'  # disable GUI mode -> speedup\n         self.cmd += ' --output_resolution 0x0'  # don't display the image -> speedup\n         self.cmd += ' --render_pose 0'  # don't draw result into the image -> speedup\n-        # out_res and render_pose disabled intentionally (even if no GUI at all), so it is not calculated -> faster\n+        if using_gpu is True:\n+            self.model = self.models.body_25\n+            self.cmd += ' --num_gpu 1' if using_gpu else ''  # use one GPU; no auto-detection -> 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