| {"nwo":"CalciferZh\/minimal-hand","sha":"da91d777a36207ea67d87f06fc881a4806312ef0","path":"prepare_mano.py","language":"python","identifier":"prepare_mano","parameters":"()","argument_list":"","return_statement":"","docstring":"Use this function to convert a mano model (from MANO-Hand Project) to the hand\n model we want to use in the project.","docstring_summary":"Use this function to convert a mano model (from MANO-Hand Project) to the hand\n model we want to use in the project.","docstring_tokens":["Use","this","function","to","convert","a","mano","model","(","from","MANO","-","Hand","Project",")","to","the","hand","model","we","want","to","use","in","the","project","."],"function":"def prepare_mano():\n \"\"\"\n Use this function to convert a mano model (from MANO-Hand Project) to the hand\n model we want to use in the project.\n \"\"\"\n with open(OFFICIAL_MANO_PATH, 'rb') as f:\n data = pickle.load(f, encoding='latin1')\n\n output = {}\n output['verts'] = np.array(data['v_template'])\n output['faces'] = np.array(data['f'])\n output['mesh_basis'] = np.transpose(data['shapedirs'], (2, 0, 1))\n\n j_regressor = np.zeros([21, 778])\n j_regressor[:16] = data['J_regressor'].toarray()\n for k, v in MANOHandJoints.mesh_mapping.items():\n j_regressor[k, v] = 1\n output['j_regressor'] = j_regressor\n output['joints'] = np.matmul(output['j_regressor'], output['verts'])\n\n raw_weights = data['weights']\n weights = [None] * 21\n weights[0] = raw_weights[:, 0]\n for j in 'IMLRT':\n weights[MANOHandJoints.labels.index(j + '0')] = np.zeros(778)\n for k in [1, 2, 3]:\n src_idx = MANOHandJoints.labels.index(j + str(k - 1))\n tar_idx = MANOHandJoints.labels.index(j + str(k))\n weights[tar_idx] = raw_weights[:, src_idx]\n output['weights'] = np.expand_dims(np.stack(weights, -1), -1)\n with open(HAND_MESH_MODEL_PATH, 'wb') as f:\n pickle.dump(output, f)","function_tokens":["def","prepare_mano","(",")",":","with","open","(","OFFICIAL_MANO_PATH",",","'rb'",")","as","f",":","data","=","pickle",".","load","(","f",",","encoding","=","'latin1'",")","output","=","{","}","output","[","'verts'","]","=","np",".","array","(","data","[","'v_template'","]",")","output","[","'faces'","]","=","np",".","array","(","data","[","'f'","]",")","output","[","'mesh_basis'","]","=","np",".","transpose","(","data","[","'shapedirs'","]",",","(","2",",","0",",","1",")",")","j_regressor","=","np",".","zeros","(","[","21",",","778","]",")","j_regressor","[",":","16","]","=","data","[","'J_regressor'","]",".","toarray","(",")","for","k",",","v","in","MANOHandJoints",".","mesh_mapping",".","items","(",")",":","j_regressor","[","k",",","v","]","=","1","output","[","'j_regressor'","]","=","j_regressor","output","[","'joints'","]","=","np",".","matmul","(","output","[","'j_regressor'","]",",","output","[","'verts'","]",")","raw_weights","=","data","[","'weights'","]","weights","=","[","None","]","*","21","weights","[","0","]","=","raw_weights","[",":",",","0","]","for","j","in","'IMLRT'",":","weights","[","MANOHandJoints",".","labels",".","index","(","j","+","'0'",")","]","=","np",".","zeros","(","778",")","for","k","in","[","1",",","2",",","3","]",":","src_idx","=","MANOHandJoints",".","labels",".","index","(","j","+","str","(","k","-","1",")",")","tar_idx","=","MANOHandJoints",".","labels",".","index","(","j","+","str","(","k",")",")","weights","[","tar_idx","]","=","raw_weights","[",":",",","src_idx","]","output","[","'weights'","]","=","np",".","expand_dims","(","np",".","stack","(","weights",",","-","1",")",",","-","1",")","with","open","(","HAND_MESH_MODEL_PATH",",","'wb'",")","as","f",":","pickle",".","dump","(","output",",","f",")"],"url":"https:\/\/github.com\/CalciferZh\/minimal-hand\/blob\/da91d777a36207ea67d87f06fc881a4806312ef0\/prepare_mano.py#L7-L38"} | |
| {"nwo":"CalciferZh\/minimal-hand","sha":"da91d777a36207ea67d87f06fc881a4806312ef0","path":"wrappers.py","language":"python","identifier":"ModelDet.__init__","parameters":"(self, model_path)","argument_list":"","return_statement":"","docstring":"Parameters\n ----------\n model_path : str\n Path to the trained model.","docstring_summary":"Parameters\n ----------\n model_path : str\n Path to the trained model.","docstring_tokens":["Parameters","----------","model_path",":","str","Path","to","the","trained","model","."],"function":"def __init__(self, model_path):\n \"\"\"\n Parameters\n ----------\n model_path : str\n Path to the trained model.\n \"\"\"\n self.graph = tf.Graph()\n with self.graph.as_default():\n with tf.variable_scope('prior_based_hand'):\n config = tf.ConfigProto()\n config.gpu_options.allow_growth = True\n self.sess = tf.Session(config=config)\n self.input_ph = tf.placeholder(tf.uint8, [128, 128, 3])\n self.feed_img = \\\n tf.cast(tf.expand_dims(self.input_ph, 0), tf.float32) \/ 255\n self.hmaps, self.dmaps, self.lmaps = \\\n detnet(self.feed_img, 1, False)\n\n self.hmap = self.hmaps[-1]\n self.dmap = self.dmaps[-1]\n self.lmap = self.lmaps[-1]\n\n self.uv = tf_hmap_to_uv(self.hmap)\n self.delta = tf.gather_nd(\n tf.transpose(self.dmap, [0, 3, 1, 2, 4]), self.uv, batch_dims=2\n )[0]\n self.xyz = tf.gather_nd(\n tf.transpose(self.lmap, [0, 3, 1, 2, 4]), self.uv, batch_dims=2\n )[0]\n\n self.uv = self.uv[0]\n tf.train.Saver().restore(self.sess, model_path)","function_tokens":["def","__init__","(","self",",","model_path",")",":","self",".","graph","=","tf",".","Graph","(",")","with","self",".","graph",".","as_default","(",")",":","with","tf",".","variable_scope","(","'prior_based_hand'",")",":","config","=","tf",".","ConfigProto","(",")","config",".","gpu_options",".","allow_growth","=","True","self",".","sess","=","tf",".","Session","(","config","=","config",")","self",".","input_ph","=","tf",".","placeholder","(","tf",".","uint8",",","[","128",",","128",",","3","]",")","self",".","feed_img","=","tf",".","cast","(","tf",".","expand_dims","(","self",".","input_ph",",","0",")",",","tf",".","float32",")","\/","255","self",".","hmaps",",","self",".","dmaps",",","self",".","lmaps","=","detnet","(","self",".","feed_img",",","1",",","False",")","self",".","hmap","=","self",".","hmaps","[","-","1","]","self",".","dmap","=","self",".","dmaps","[","-","1","]","self",".","lmap","=","self",".","lmaps","[","-","1","]","self",".","uv","=","tf_hmap_to_uv","(","self",".","hmap",")","self",".","delta","=","tf",".","gather_nd","(","tf",".","transpose","(","self",".","dmap",",","[","0",",","3",",","1",",","2",",","4","]",")",",","self",".","uv",",","batch_dims","=","2",")","[","0","]","self",".","xyz","=","tf",".","gather_nd","(","tf",".","transpose","(","self",".","lmap",",","[","0",",","3",",","1",",","2",",","4","]",")",",","self",".","uv",",","batch_dims","=","2",")","[","0","]","self",".","uv","=","self",".","uv","[","0","]","tf",".","train",".","Saver","(",")",".","restore","(","self",".","sess",",","model_path",")"],"url":"https:\/\/github.com\/CalciferZh\/minimal-hand\/blob\/da91d777a36207ea67d87f06fc881a4806312ef0\/wrappers.py#L16-L48"} | |
| {"nwo":"CalciferZh\/minimal-hand","sha":"da91d777a36207ea67d87f06fc881a4806312ef0","path":"wrappers.py","language":"python","identifier":"ModelDet.process","parameters":"(self, img)","argument_list":"","return_statement":"return results","docstring":"Process a color image.\n\n Parameters\n ----------\n img : np.ndarray\n A 128x128 RGB image of **left hand** with dtype uint8.\n\n Returns\n -------\n np.ndarray, shape [21, 3]\n Normalized keypoint locations. The coordinates are relative to the M0\n joint and normalized by the length of the bone from wrist to M0. The\n order of keypoints is as `kinematics.MPIIHandJoints`.\n np.ndarray, shape [21, 2]\n The uv coordinates of the keypoints on the heat map, whose resolution is\n 32x32.","docstring_summary":"Process a color image.","docstring_tokens":["Process","a","color","image","."],"function":"def process(self, img):\n \"\"\"\n Process a color image.\n\n Parameters\n ----------\n img : np.ndarray\n A 128x128 RGB image of **left hand** with dtype uint8.\n\n Returns\n -------\n np.ndarray, shape [21, 3]\n Normalized keypoint locations. The coordinates are relative to the M0\n joint and normalized by the length of the bone from wrist to M0. The\n order of keypoints is as `kinematics.MPIIHandJoints`.\n np.ndarray, shape [21, 2]\n The uv coordinates of the keypoints on the heat map, whose resolution is\n 32x32.\n \"\"\"\n results = self.sess.run([self.xyz, self.uv], {self.input_ph: img})\n return results","function_tokens":["def","process","(","self",",","img",")",":","results","=","self",".","sess",".","run","(","[","self",".","xyz",",","self",".","uv","]",",","{","self",".","input_ph",":","img","}",")","return","results"],"url":"https:\/\/github.com\/CalciferZh\/minimal-hand\/blob\/da91d777a36207ea67d87f06fc881a4806312ef0\/wrappers.py#L50-L70"} | |
| {"nwo":"CalciferZh\/minimal-hand","sha":"da91d777a36207ea67d87f06fc881a4806312ef0","path":"wrappers.py","language":"python","identifier":"ModelIK.__init__","parameters":"(self, input_size, network_fn, model_path, net_depth, net_width)","argument_list":"","return_statement":"","docstring":"Parameters\n ----------\n input_size : int\n Number of joints to be used, e.g. 21, 42.\n network_fn : function\n Network function from `network.py`.\n model_path : str\n Path to the trained model.\n net_depth : int\n Number of layers.\n net_width : int\n Number of neurons in each layer.","docstring_summary":"Parameters\n ----------\n input_size : int\n Number of joints to be used, e.g. 21, 42.\n network_fn : function\n Network function from `network.py`.\n model_path : str\n Path to the trained model.\n net_depth : int\n Number of layers.\n net_width : int\n Number of neurons in each layer.","docstring_tokens":["Parameters","----------","input_size",":","int","Number","of","joints","to","be","used","e",".","g",".","21","42",".","network_fn",":","function","Network","function","from","network",".","py",".","model_path",":","str","Path","to","the","trained","model",".","net_depth",":","int","Number","of","layers",".","net_width",":","int","Number","of","neurons","in","each","layer","."],"function":"def __init__(self, input_size, network_fn, model_path, net_depth, net_width):\n \"\"\"\n Parameters\n ----------\n input_size : int\n Number of joints to be used, e.g. 21, 42.\n network_fn : function\n Network function from `network.py`.\n model_path : str\n Path to the trained model.\n net_depth : int\n Number of layers.\n net_width : int\n Number of neurons in each layer.\n \"\"\"\n self.graph = tf.Graph()\n with self.graph.as_default():\n self.input_ph = tf.placeholder(tf.float32, [1, input_size, 3])\n with tf.name_scope('network'):\n self.theta = \\\n network_fn(self.input_ph, net_depth, net_width, training=False)[0]\n config = tf.ConfigProto()\n config.gpu_options.allow_growth = True\n self.sess = tf.Session(config=config)\n tf.train.Saver().restore(self.sess, model_path)","function_tokens":["def","__init__","(","self",",","input_size",",","network_fn",",","model_path",",","net_depth",",","net_width",")",":","self",".","graph","=","tf",".","Graph","(",")","with","self",".","graph",".","as_default","(",")",":","self",".","input_ph","=","tf",".","placeholder","(","tf",".","float32",",","[","1",",","input_size",",","3","]",")","with","tf",".","name_scope","(","'network'",")",":","self",".","theta","=","network_fn","(","self",".","input_ph",",","net_depth",",","net_width",",","training","=","False",")","[","0","]","config","=","tf",".","ConfigProto","(",")","config",".","gpu_options",".","allow_growth","=","True","self",".","sess","=","tf",".","Session","(","config","=","config",")","tf",".","train",".","Saver","(",")",".","restore","(","self",".","sess",",","model_path",")"],"url":"https:\/\/github.com\/CalciferZh\/minimal-hand\/blob\/da91d777a36207ea67d87f06fc881a4806312ef0\/wrappers.py#L77-L101"} | |
| {"nwo":"CalciferZh\/minimal-hand","sha":"da91d777a36207ea67d87f06fc881a4806312ef0","path":"wrappers.py","language":"python","identifier":"ModelIK.process","parameters":"(self, joints)","argument_list":"","return_statement":"return theta","docstring":"Estimate joint rotations from locations.\n\n Parameters\n ----------\n joints : np.ndarray, shape [N, 3]\n Input joint locations (and other information e.g. bone orientation).\n\n Returns\n -------\n np.ndarray, shape [21, 4]\n Estimated global joint rotations in quaternions.","docstring_summary":"Estimate joint rotations from locations.","docstring_tokens":["Estimate","joint","rotations","from","locations","."],"function":"def process(self, joints):\n \"\"\"\n Estimate joint rotations from locations.\n\n Parameters\n ----------\n joints : np.ndarray, shape [N, 3]\n Input joint locations (and other information e.g. bone orientation).\n\n Returns\n -------\n np.ndarray, shape [21, 4]\n Estimated global joint rotations in quaternions.\n \"\"\"\n theta = \\\n self.sess.run(self.theta, {self.input_ph: np.expand_dims(joints, 0)})\n if len(theta.shape) == 3:\n theta = theta[0]\n return theta","function_tokens":["def","process","(","self",",","joints",")",":","theta","=","self",".","sess",".","run","(","self",".","theta",",","{","self",".","input_ph",":","np",".","expand_dims","(","joints",",","0",")","}",")","if","len","(","theta",".","shape",")","==","3",":","theta","=","theta","[","0","]","return","theta"],"url":"https:\/\/github.com\/CalciferZh\/minimal-hand\/blob\/da91d777a36207ea67d87f06fc881a4806312ef0\/wrappers.py#L103-L121"} | |
| {"nwo":"CalciferZh\/minimal-hand","sha":"da91d777a36207ea67d87f06fc881a4806312ef0","path":"wrappers.py","language":"python","identifier":"ModelPipeline.process","parameters":"(self, frame)","argument_list":"","return_statement":"return xyz, theta","docstring":"Process a single frame.\n\n Parameters\n ----------\n frame : np.ndarray, shape [128, 128, 3], dtype np.uint8.\n Frame to be processed.\n\n Returns\n -------\n np.ndarray, shape [21, 3]\n Joint locations.\n np.ndarray, shape [21, 4]\n Joint rotations.","docstring_summary":"Process a single frame.","docstring_tokens":["Process","a","single","frame","."],"function":"def process(self, frame):\n \"\"\"\n Process a single frame.\n\n Parameters\n ----------\n frame : np.ndarray, shape [128, 128, 3], dtype np.uint8.\n Frame to be processed.\n\n Returns\n -------\n np.ndarray, shape [21, 3]\n Joint locations.\n np.ndarray, shape [21, 4]\n Joint rotations.\n \"\"\"\n xyz, _ = self.det_model.process(frame)\n delta, length = xyz_to_delta(xyz, MPIIHandJoints)\n delta *= length\n pack = np.concatenate(\n [xyz, delta, self.mpii_ref_xyz, self.mpii_ref_delta], 0\n )\n theta = self.ik_model.process(pack)\n\n return xyz, theta","function_tokens":["def","process","(","self",",","frame",")",":","xyz",",","_","=","self",".","det_model",".","process","(","frame",")","delta",",","length","=","xyz_to_delta","(","xyz",",","MPIIHandJoints",")","delta","*=","length","pack","=","np",".","concatenate","(","[","xyz",",","delta",",","self",".","mpii_ref_xyz",",","self",".","mpii_ref_delta","]",",","0",")","theta","=","self",".","ik_model",".","process","(","pack",")","return","xyz",",","theta"],"url":"https:\/\/github.com\/CalciferZh\/minimal-hand\/blob\/da91d777a36207ea67d87f06fc881a4806312ef0\/wrappers.py#L148-L172"} | |
| {"nwo":"CalciferZh\/minimal-hand","sha":"da91d777a36207ea67d87f06fc881a4806312ef0","path":"capture.py","language":"python","identifier":"OpenCVCapture.__init__","parameters":"(self)","argument_list":"","return_statement":"","docstring":"Init.","docstring_summary":"Init.","docstring_tokens":["Init","."],"function":"def __init__(self):\n \"\"\"\n Init.\n \"\"\"\n self.cap = cv2.VideoCapture(0)","function_tokens":["def","__init__","(","self",")",":","self",".","cap","=","cv2",".","VideoCapture","(","0",")"],"url":"https:\/\/github.com\/CalciferZh\/minimal-hand\/blob\/da91d777a36207ea67d87f06fc881a4806312ef0\/capture.py#L9-L13"} | |
| {"nwo":"CalciferZh\/minimal-hand","sha":"da91d777a36207ea67d87f06fc881a4806312ef0","path":"capture.py","language":"python","identifier":"OpenCVCapture.read","parameters":"(self)","argument_list":"","return_statement":"return np.flip(frame, -1).copy()","docstring":"Read one frame. Note this function might be blocked by the sensor.\n\n Returns\n -------\n np.ndarray\n Read frame. Might be `None` is the webcam fails to get on frame.","docstring_summary":"Read one frame. Note this function might be blocked by the sensor.","docstring_tokens":["Read","one","frame",".","Note","this","function","might","be","blocked","by","the","sensor","."],"function":"def read(self):\n \"\"\"\n Read one frame. Note this function might be blocked by the sensor.\n\n Returns\n -------\n np.ndarray\n Read frame. Might be `None` is the webcam fails to get on frame.\n \"\"\"\n flag, frame = self.cap.read()\n if not flag:\n return None\n return np.flip(frame, -1).copy()","function_tokens":["def","read","(","self",")",":","flag",",","frame","=","self",".","cap",".","read","(",")","if","not","flag",":","return","None","return","np",".","flip","(","frame",",","-","1",")",".","copy","(",")"],"url":"https:\/\/github.com\/CalciferZh\/minimal-hand\/blob\/da91d777a36207ea67d87f06fc881a4806312ef0\/capture.py#L15-L27"} | |
| {"nwo":"CalciferZh\/minimal-hand","sha":"da91d777a36207ea67d87f06fc881a4806312ef0","path":"app.py","language":"python","identifier":"live_application","parameters":"(capture)","argument_list":"","return_statement":"","docstring":"Launch an application that reads from a webcam and estimates hand pose at\n real-time.\n\n The captured hand must be the right hand, but will be flipped internally\n and rendered.\n\n Parameters\n ----------\n capture : object\n An object from `capture.py` to read capture stream from.","docstring_summary":"Launch an application that reads from a webcam and estimates hand pose at\n real-time.","docstring_tokens":["Launch","an","application","that","reads","from","a","webcam","and","estimates","hand","pose","at","real","-","time","."],"function":"def live_application(capture):\n \"\"\"\n Launch an application that reads from a webcam and estimates hand pose at\n real-time.\n\n The captured hand must be the right hand, but will be flipped internally\n and rendered.\n\n Parameters\n ----------\n capture : object\n An object from `capture.py` to read capture stream from.\n \"\"\"\n ############ output visualization ############\n view_mat = axangle2mat([1, 0, 0], np.pi) # align different coordinate systems\n window_size = 1080\n\n hand_mesh = HandMesh(config.HAND_MESH_MODEL_PATH)\n mesh = o3d.geometry.TriangleMesh()\n mesh.triangles = o3d.utility.Vector3iVector(hand_mesh.faces)\n mesh.vertices = \\\n o3d.utility.Vector3dVector(np.matmul(view_mat, hand_mesh.verts.T).T * 1000)\n mesh.compute_vertex_normals()\n\n viewer = o3d.visualization.Visualizer()\n viewer.create_window(\n width=window_size + 1, height=window_size + 1,\n window_name='Minimal Hand - output'\n )\n viewer.add_geometry(mesh)\n\n view_control = viewer.get_view_control()\n cam_params = view_control.convert_to_pinhole_camera_parameters()\n extrinsic = cam_params.extrinsic.copy()\n extrinsic[0:3, 3] = 0\n cam_params.extrinsic = extrinsic\n cam_params.intrinsic.set_intrinsics(\n window_size + 1, window_size + 1, config.CAM_FX, config.CAM_FY,\n window_size \/\/ 2, window_size \/\/ 2\n )\n view_control.convert_from_pinhole_camera_parameters(cam_params)\n view_control.set_constant_z_far(1000)\n\n render_option = viewer.get_render_option()\n render_option.load_from_json('.\/render_option.json')\n viewer.update_renderer()\n\n ############ input visualization ############\n pygame.init()\n display = pygame.display.set_mode((window_size, window_size))\n pygame.display.set_caption('Minimal Hand - input')\n\n ############ misc ############\n mesh_smoother = OneEuroFilter(4.0, 0.0)\n clock = pygame.time.Clock()\n model = ModelPipeline()\n\n while True:\n frame_large = capture.read()\n if frame_large is None:\n continue\n if frame_large.shape[0] > frame_large.shape[1]:\n margin = int((frame_large.shape[0] - frame_large.shape[1]) \/ 2)\n frame_large = frame_large[margin:-margin]\n else:\n margin = int((frame_large.shape[1] - frame_large.shape[0]) \/ 2)\n frame_large = frame_large[:, margin:-margin]\n\n frame_large = np.flip(frame_large, axis=1).copy()\n frame = imresize(frame_large, (128, 128))\n\n _, theta_mpii = model.process(frame)\n theta_mano = mpii_to_mano(theta_mpii)\n\n v = hand_mesh.set_abs_quat(theta_mano)\n v *= 2 # for better visualization\n v = v * 1000 + np.array([0, 0, 400])\n v = mesh_smoother.process(v)\n mesh.triangles = o3d.utility.Vector3iVector(hand_mesh.faces)\n mesh.vertices = o3d.utility.Vector3dVector(np.matmul(view_mat, v.T).T)\n mesh.paint_uniform_color(config.HAND_COLOR)\n mesh.compute_triangle_normals()\n mesh.compute_vertex_normals()\n viewer.update_geometry(mesh)\n\n viewer.poll_events()\n\n display.blit(\n pygame.surfarray.make_surface(\n np.transpose(\n imresize(frame_large, (window_size, window_size)\n ), (1, 0, 2))\n ),\n (0, 0)\n )\n pygame.display.update()\n\n if keyboard.is_pressed(\"esc\"):\n break\n\n clock.tick(30)","function_tokens":["def","live_application","(","capture",")",":","############ output visualization ############","view_mat","=","axangle2mat","(","[","1",",","0",",","0","]",",","np",".","pi",")","# align different coordinate systems","window_size","=","1080","hand_mesh","=","HandMesh","(","config",".","HAND_MESH_MODEL_PATH",")","mesh","=","o3d",".","geometry",".","TriangleMesh","(",")","mesh",".","triangles","=","o3d",".","utility",".","Vector3iVector","(","hand_mesh",".","faces",")","mesh",".","vertices","=","o3d",".","utility",".","Vector3dVector","(","np",".","matmul","(","view_mat",",","hand_mesh",".","verts",".","T",")",".","T","*","1000",")","mesh",".","compute_vertex_normals","(",")","viewer","=","o3d",".","visualization",".","Visualizer","(",")","viewer",".","create_window","(","width","=","window_size","+","1",",","height","=","window_size","+","1",",","window_name","=","'Minimal Hand - 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| {"nwo":"CalciferZh\/minimal-hand","sha":"da91d777a36207ea67d87f06fc881a4806312ef0","path":"utils.py","language":"python","identifier":"imresize","parameters":"(img, size)","argument_list":"","return_statement":"return cv2.resize(img, size, cv2.INTER_LINEAR)","docstring":"Resize an image with cv2.INTER_LINEAR.\n\n Parameters\n ----------\n size: (width, height)","docstring_summary":"Resize an image with cv2.INTER_LINEAR.","docstring_tokens":["Resize","an","image","with","cv2",".","INTER_LINEAR","."],"function":"def imresize(img, size):\n \"\"\"\n Resize an image with cv2.INTER_LINEAR.\n\n Parameters\n ----------\n size: (width, height)\n\n \"\"\"\n return cv2.resize(img, size, cv2.INTER_LINEAR)","function_tokens":["def","imresize","(","img",",","size",")",":","return","cv2",".","resize","(","img",",","size",",","cv2",".","INTER_LINEAR",")"],"url":"https:\/\/github.com\/CalciferZh\/minimal-hand\/blob\/da91d777a36207ea67d87f06fc881a4806312ef0\/utils.py#L7-L16"} | |
| {"nwo":"CalciferZh\/minimal-hand","sha":"da91d777a36207ea67d87f06fc881a4806312ef0","path":"utils.py","language":"python","identifier":"load_pkl","parameters":"(path)","argument_list":"","return_statement":"return data","docstring":"Load pickle data.\n\n Parameter\n ---------\n path: Path to pickle file.\n\n Return\n ------\n Data in pickle file.","docstring_summary":"Load pickle data.","docstring_tokens":["Load","pickle","data","."],"function":"def load_pkl(path):\n \"\"\"\n Load pickle data.\n\n Parameter\n ---------\n path: Path to pickle file.\n\n Return\n ------\n Data in pickle file.\n\n \"\"\"\n with open(path, 'rb') as f:\n data = pickle.load(f)\n return data","function_tokens":["def","load_pkl","(","path",")",":","with","open","(","path",",","'rb'",")","as","f",":","data","=","pickle",".","load","(","f",")","return","data"],"url":"https:\/\/github.com\/CalciferZh\/minimal-hand\/blob\/da91d777a36207ea67d87f06fc881a4806312ef0\/utils.py#L19-L34"} | |
| {"nwo":"CalciferZh\/minimal-hand","sha":"da91d777a36207ea67d87f06fc881a4806312ef0","path":"hand_mesh.py","language":"python","identifier":"HandMesh.__init__","parameters":"(self, model_path)","argument_list":"","return_statement":"","docstring":"Init.\n\n Parameters\n ----------\n model_path : str\n Path to the MANO model file. This model is converted by `prepare_mano.py`\n from official release.","docstring_summary":"Init.","docstring_tokens":["Init","."],"function":"def __init__(self, model_path):\n \"\"\"\n Init.\n\n Parameters\n ----------\n model_path : str\n Path to the MANO model file. This model is converted by `prepare_mano.py`\n from official release.\n \"\"\"\n params = load_pkl(model_path)\n self.verts = params['verts']\n self.faces = params['faces']\n self.weights = params['weights']\n self.joints = params['joints']\n\n self.n_verts = self.verts.shape[0]\n self.n_faces = self.faces.shape[0]\n\n self.ref_pose = []\n self.ref_T = []\n for j in range(MANOHandJoints.n_joints):\n parent = MANOHandJoints.parents[j]\n if parent is None:\n self.ref_T.append(self.verts)\n self.ref_pose.append(self.joints[j])\n else:\n self.ref_T.append(self.verts - self.joints[parent])\n self.ref_pose.append(self.joints[j] - self.joints[parent])\n self.ref_pose = np.expand_dims(np.stack(self.ref_pose, 0), -1)\n self.ref_T = np.expand_dims(np.stack(self.ref_T, 1), -1)","function_tokens":["def","__init__","(","self",",","model_path",")",":","params","=","load_pkl","(","model_path",")","self",".","verts","=","params","[","'verts'","]","self",".","faces","=","params","[","'faces'","]","self",".","weights","=","params","[","'weights'","]","self",".","joints","=","params","[","'joints'","]","self",".","n_verts","=","self",".","verts",".","shape","[","0","]","self",".","n_faces","=","self",".","faces",".","shape","[","0","]","self",".","ref_pose","=","[","]","self",".","ref_T","=","[","]","for","j","in","range","(","MANOHandJoints",".","n_joints",")",":","parent","=","MANOHandJoints",".","parents","[","j","]","if","parent","is","None",":","self",".","ref_T",".","append","(","self",".","verts",")","self",".","ref_pose",".","append","(","self",".","joints","[","j","]",")","else",":","self",".","ref_T",".","append","(","self",".","verts","-","self",".","joints","[","parent","]",")","self",".","ref_pose",".","append","(","self",".","joints","[","j","]","-","self",".","joints","[","parent","]",")","self",".","ref_pose","=","np",".","expand_dims","(","np",".","stack","(","self",".","ref_pose",",","0",")",",","-","1",")","self",".","ref_T","=","np",".","expand_dims","(","np",".","stack","(","self",".","ref_T",",","1",")",",","-","1",")"],"url":"https:\/\/github.com\/CalciferZh\/minimal-hand\/blob\/da91d777a36207ea67d87f06fc881a4806312ef0\/hand_mesh.py#L13-L43"} | |
| {"nwo":"CalciferZh\/minimal-hand","sha":"da91d777a36207ea67d87f06fc881a4806312ef0","path":"hand_mesh.py","language":"python","identifier":"HandMesh.set_abs_quat","parameters":"(self, quat)","argument_list":"","return_statement":"return self.verts.copy()","docstring":"Set absolute (global) rotation for the hand.\n\n Parameters\n ----------\n quat : np.ndarray, shape [J, 4]\n Absolute rotations for each joint in quaternion.\n\n Returns\n -------\n np.ndarray, shape [V, 3]\n Mesh vertices after posing.","docstring_summary":"Set absolute (global) rotation for the hand.","docstring_tokens":["Set","absolute","(","global",")","rotation","for","the","hand","."],"function":"def set_abs_quat(self, quat):\n \"\"\"\n Set absolute (global) rotation for the hand.\n\n Parameters\n ----------\n quat : np.ndarray, shape [J, 4]\n Absolute rotations for each joint in quaternion.\n\n Returns\n -------\n np.ndarray, shape [V, 3]\n Mesh vertices after posing.\n \"\"\"\n mats = []\n for j in range(MANOHandJoints.n_joints):\n mats.append(quat2mat(quat[j]))\n mats = np.stack(mats, 0)\n\n pose = np.matmul(mats, self.ref_pose)\n joint_xyz = [None] * MANOHandJoints.n_joints\n for j in range(MANOHandJoints.n_joints):\n joint_xyz[j] = pose[j]\n parent = MANOHandJoints.parents[j]\n if parent is not None:\n joint_xyz[j] += joint_xyz[parent]\n joint_xyz = np.stack(joint_xyz, 0)[..., 0]\n\n T = np.matmul(np.expand_dims(mats, 0), self.ref_T)[..., 0]\n self.verts = [None] * MANOHandJoints.n_joints\n for j in range(MANOHandJoints.n_joints):\n self.verts[j] = T[:, j]\n parent = MANOHandJoints.parents[j]\n if parent is not None:\n self.verts[j] += joint_xyz[parent]\n self.verts = np.stack(self.verts, 1)\n self.verts = np.sum(self.verts * self.weights, 1)\n\n return self.verts.copy()","function_tokens":["def","set_abs_quat","(","self",",","quat",")",":","mats","=","[","]","for","j","in","range","(","MANOHandJoints",".","n_joints",")",":","mats",".","append","(","quat2mat","(","quat","[","j","]",")",")","mats","=","np",".","stack","(","mats",",","0",")","pose","=","np",".","matmul","(","mats",",","self",".","ref_pose",")","joint_xyz","=","[","None","]","*","MANOHandJoints",".","n_joints","for","j","in","range","(","MANOHandJoints",".","n_joints",")",":","joint_xyz","[","j","]","=","pose","[","j","]","parent","=","MANOHandJoints",".","parents","[","j","]","if","parent","is","not","None",":","joint_xyz","[","j","]","+=","joint_xyz","[","parent","]","joint_xyz","=","np",".","stack","(","joint_xyz",",","0",")","[","...",",","0","]","T","=","np",".","matmul","(","np",".","expand_dims","(","mats",",","0",")",",","self",".","ref_T",")","[","...",",","0","]","self",".","verts","=","[","None","]","*","MANOHandJoints",".","n_joints","for","j","in","range","(","MANOHandJoints",".","n_joints",")",":","self",".","verts","[","j","]","=","T","[",":",",","j","]","parent","=","MANOHandJoints",".","parents","[","j","]","if","parent","is","not","None",":","self",".","verts","[","j","]","+=","joint_xyz","[","parent","]","self",".","verts","=","np",".","stack","(","self",".","verts",",","1",")","self",".","verts","=","np",".","sum","(","self",".","verts","*","self",".","weights",",","1",")","return","self",".","verts",".","copy","(",")"],"url":"https:\/\/github.com\/CalciferZh\/minimal-hand\/blob\/da91d777a36207ea67d87f06fc881a4806312ef0\/hand_mesh.py#L45-L83"} | |
| {"nwo":"CalciferZh\/minimal-hand","sha":"da91d777a36207ea67d87f06fc881a4806312ef0","path":"kinematics.py","language":"python","identifier":"mpii_to_mano","parameters":"(mpii)","argument_list":"","return_statement":"return mano","docstring":"Map data from MPIIHandJoints order to MANOHandJoints order.\n\n Parameters\n ----------\n mpii : np.ndarray, [21, ...]\n Data in MPIIHandJoints order. Note that the joints are along axis 0.\n\n Returns\n -------\n np.ndarray\n Data in MANOHandJoints order.","docstring_summary":"Map data from MPIIHandJoints order to MANOHandJoints order.","docstring_tokens":["Map","data","from","MPIIHandJoints","order","to","MANOHandJoints","order","."],"function":"def mpii_to_mano(mpii):\n \"\"\"\n Map data from MPIIHandJoints order to MANOHandJoints order.\n\n Parameters\n ----------\n mpii : np.ndarray, [21, ...]\n Data in MPIIHandJoints order. Note that the joints are along axis 0.\n\n Returns\n -------\n np.ndarray\n Data in MANOHandJoints order.\n \"\"\"\n mano = []\n for j in range(MANOHandJoints.n_joints):\n mano.append(\n mpii[MPIIHandJoints.labels.index(MANOHandJoints.labels[j])]\n )\n mano = np.stack(mano, 0)\n return mano","function_tokens":["def","mpii_to_mano","(","mpii",")",":","mano","=","[","]","for","j","in","range","(","MANOHandJoints",".","n_joints",")",":","mano",".","append","(","mpii","[","MPIIHandJoints",".","labels",".","index","(","MANOHandJoints",".","labels","[","j","]",")","]",")","mano","=","np",".","stack","(","mano",",","0",")","return","mano"],"url":"https:\/\/github.com\/CalciferZh\/minimal-hand\/blob\/da91d777a36207ea67d87f06fc881a4806312ef0\/kinematics.py#L53-L73"} | |
| {"nwo":"CalciferZh\/minimal-hand","sha":"da91d777a36207ea67d87f06fc881a4806312ef0","path":"kinematics.py","language":"python","identifier":"mano_to_mpii","parameters":"(mano)","argument_list":"","return_statement":"return mpii","docstring":"Map data from MANOHandJoints order to MPIIHandJoints order.\n\n Parameters\n ----------\n mano : np.ndarray, [21, ...]\n Data in MANOHandJoints order. Note that the joints are along axis 0.\n\n Returns\n -------\n np.ndarray\n Data in MPIIHandJoints order.","docstring_summary":"Map data from MANOHandJoints order to MPIIHandJoints order.","docstring_tokens":["Map","data","from","MANOHandJoints","order","to","MPIIHandJoints","order","."],"function":"def mano_to_mpii(mano):\n \"\"\"\n Map data from MANOHandJoints order to MPIIHandJoints order.\n\n Parameters\n ----------\n mano : np.ndarray, [21, ...]\n Data in MANOHandJoints order. Note that the joints are along axis 0.\n\n Returns\n -------\n np.ndarray\n Data in MPIIHandJoints order.\n \"\"\"\n mpii = []\n for j in range(MPIIHandJoints.n_joints):\n mpii.append(\n mano[MANOHandJoints.labels.index(MPIIHandJoints.labels[j])]\n )\n mpii = np.stack(mpii, 0)\n return mpii","function_tokens":["def","mano_to_mpii","(","mano",")",":","mpii","=","[","]","for","j","in","range","(","MPIIHandJoints",".","n_joints",")",":","mpii",".","append","(","mano","[","MANOHandJoints",".","labels",".","index","(","MPIIHandJoints",".","labels","[","j","]",")","]",")","mpii","=","np",".","stack","(","mpii",",","0",")","return","mpii"],"url":"https:\/\/github.com\/CalciferZh\/minimal-hand\/blob\/da91d777a36207ea67d87f06fc881a4806312ef0\/kinematics.py#L76-L96"} | |
| {"nwo":"CalciferZh\/minimal-hand","sha":"da91d777a36207ea67d87f06fc881a4806312ef0","path":"kinematics.py","language":"python","identifier":"xyz_to_delta","parameters":"(xyz, joints_def)","argument_list":"","return_statement":"return delta, lengths","docstring":"Compute bone orientations from joint coordinates (child joint - parent joint).\n The returned vectors are normalized.\n For the root joint, it will be a zero vector.\n\n Parameters\n ----------\n xyz : np.ndarray, shape [J, 3]\n Joint coordinates.\n joints_def : object\n An object that defines the kinematic skeleton, e.g. MPIIHandJoints.\n\n Returns\n -------\n np.ndarray, shape [J, 3]\n The **unit** vectors from each child joint to its parent joint.\n For the root joint, it's are zero vector.\n np.ndarray, shape [J, 1]\n The length of each bone (from child joint to parent joint).\n For the root joint, it's zero.","docstring_summary":"Compute bone orientations from joint coordinates (child joint - parent joint).\n The returned vectors are normalized.\n For the root joint, it will be a zero vector.","docstring_tokens":["Compute","bone","orientations","from","joint","coordinates","(","child","joint","-","parent","joint",")",".","The","returned","vectors","are","normalized",".","For","the","root","joint","it","will","be","a","zero","vector","."],"function":"def xyz_to_delta(xyz, joints_def):\n \"\"\"\n Compute bone orientations from joint coordinates (child joint - parent joint).\n The returned vectors are normalized.\n For the root joint, it will be a zero vector.\n\n Parameters\n ----------\n xyz : np.ndarray, shape [J, 3]\n Joint coordinates.\n joints_def : object\n An object that defines the kinematic skeleton, e.g. MPIIHandJoints.\n\n Returns\n -------\n np.ndarray, shape [J, 3]\n The **unit** vectors from each child joint to its parent joint.\n For the root joint, it's are zero vector.\n np.ndarray, shape [J, 1]\n The length of each bone (from child joint to parent joint).\n For the root joint, it's zero.\n \"\"\"\n delta = []\n for j in range(joints_def.n_joints):\n p = joints_def.parents[j]\n if p is None:\n delta.append(np.zeros(3))\n else:\n delta.append(xyz[j] - xyz[p])\n delta = np.stack(delta, 0)\n lengths = np.linalg.norm(delta, axis=-1, keepdims=True)\n delta \/= np.maximum(lengths, np.finfo(xyz.dtype).eps)\n return delta, lengths","function_tokens":["def","xyz_to_delta","(","xyz",",","joints_def",")",":","delta","=","[","]","for","j","in","range","(","joints_def",".","n_joints",")",":","p","=","joints_def",".","parents","[","j","]","if","p","is","None",":","delta",".","append","(","np",".","zeros","(","3",")",")","else",":","delta",".","append","(","xyz","[","j","]","-","xyz","[","p","]",")","delta","=","np",".","stack","(","delta",",","0",")","lengths","=","np",".","linalg",".","norm","(","delta",",","axis","=","-","1",",","keepdims","=","True",")","delta","\/=","np",".","maximum","(","lengths",",","np",".","finfo","(","xyz",".","dtype",")",".","eps",")","return","delta",",","lengths"],"url":"https:\/\/github.com\/CalciferZh\/minimal-hand\/blob\/da91d777a36207ea67d87f06fc881a4806312ef0\/kinematics.py#L99-L131"} |