Python cv2.IMREAD_ANYDEPTH Examples
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code examples of cv2.IMREAD_ANYDEPTH().
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Example #1
Source File: doc3dwc_loader.py From DewarpNet with MIT License | 7 votes |
def __getitem__(self, index): im_name = self.files[self.split][index] # 1/824_8-cp_Page_0503-7Nw0001 im_path = pjoin(self.root, 'img', im_name + '.png') lbl_path=pjoin(self.root, 'wc', im_name + '.exr') im = m.imread(im_path,mode='RGB') im = np.array(im, dtype=np.uint8) lbl = cv2.imread(lbl_path, cv2.IMREAD_ANYCOLOR | cv2.IMREAD_ANYDEPTH) lbl = np.array(lbl, dtype=np.float) if 'val' in self.split: im, lbl=tight_crop(im/255.0,lbl) if self.augmentations: #this is for training, default false for validation\ tex_id=random.randint(0,len(self.txpths)-1) txpth=self.txpths[tex_id] tex=cv2.imread(os.path.join(self.root[:-7],txpth)).astype(np.uint8) bg=cv2.resize(tex,self.img_size,interpolation=cv2.INTER_NEAREST) im,lbl=data_aug(im,lbl,bg) if self.is_transform: im, lbl = self.transform(im, lbl) return im, lbl
Example #2
Source File: process.py From deepdiy with MIT License | 6 votes |
def run_tiff(file_path,progress_percent): progress_percent angle = 0 ret,video=cv2.imreadmulti(file_path,flags=cv2.IMREAD_ANYDEPTH) video_labeled,table=[],[] idx=1 for frame in video[:]: img_label,angle_new=process(frame) angle_new = float('{0:.2f}'.format(angle_new)) rotation=cal_rotation(angle,angle_new) rotation = float('{0:.2f}'.format(rotation)) table.append([angle,rotation,angle_new]) video_labeled.append(img_label) angle=angle_new idx+=1 progress_percent['value']=idx/len(video)*100 # print(table[-1]) # cv2.imshow('img',cv2.resize(img_label,(512,512))) # if cv2.waitKey(0) & 0xFF == ord('q'): # break return video_labeled,table
Example #3
Source File: dataset_util.py From Gated2Depth with MIT License | 6 votes |
def read_gated_image(base_dir, gta_pass, img_id, data_type, num_bits=10, scale_images=False, scaled_img_width=None, scaled_img_height=None, normalize_images=False): gated_imgs = [] normalizer = 2 ** num_bits - 1. for gate_id in range(3): gate_dir = os.path.join(base_dir, gta_pass, 'gated%d_10bit' % gate_id) img = cv2.imread(os.path.join(gate_dir, img_id + '.png'), cv2.IMREAD_ANYCOLOR | cv2.IMREAD_ANYDEPTH) if data_type == 'real': img = img[crop_size:(img.shape[0] - crop_size), crop_size:(img.shape[1] - crop_size)] img = img.copy() img[img > 2 ** 10 - 1] = normalizer img = np.float32(img / normalizer) gated_imgs.append(np.expand_dims(img, axis=2)) img = np.concatenate(gated_imgs, axis=2) if normalize_images: mean = np.mean(img, axis=2, keepdims=True) std = np.std(img, axis=2, keepdims=True) img = (img - mean) / (std + np.finfo(float).eps) if scale_images: img = cv2.resize(img, dsize=(scaled_img_width, scaled_img_height), interpolation=cv2.INTER_AREA) return np.expand_dims(img, axis=0)
Example #4
Source File: io.py From pydlt with BSD 3-Clause Clear License | 6 votes |
def imread(filename): """Reads an image file from disk into a Numpy Array (OpenCV view). Args: filename (str): Name of pfm image file. """ filename = process(filename) ext = os.path.splitext(filename)[1] if ext.lower() == '.pfm': return load_pfm(filename) elif ext.lower() == '.dng': return load_dng(filename) else: loaded = cv2.imread(filename, flags=cv2.IMREAD_ANYDEPTH + cv2.IMREAD_COLOR) if loaded is None: raise IOError('Could not read {0}'.format(filename)) else: return loaded
Example #5
Source File: depth_fill.py From sanet_relocal_demo with GNU General Public License v3.0 | 5 votes |
def read_7scenese_depth(png_file_path): depth = cv2.imread(png_file_path, cv2.IMREAD_ANYDEPTH).astype(np.float32) depth[depth >= 65535] = 0 return depth / 1000.0
Example #6
Source File: test_flownet_2012.py From DF-Net with MIT License | 5 votes |
def get_flow(path): bgr = cv2.imread(path, cv2.IMREAD_ANYCOLOR | cv2.IMREAD_ANYDEPTH) invalid = bgr[:, :, 0] == 0 out_flow = (bgr[:, :, 2:0:-1].astype('f4') - 2**15) / 64. out_flow[invalid] = 0 return out_flow, bgr[:, :, 0]
Example #7
Source File: test_flownet_2015.py From DF-Net with MIT License | 5 votes |
def get_flow(path): bgr = cv2.imread(path, cv2.IMREAD_ANYCOLOR | cv2.IMREAD_ANYDEPTH) invalid = bgr[:, :, 0] == 0 out_flow = (bgr[:, :, 2:0:-1].astype('f4') - 2**15) / 64. out_flow[invalid] = 0 return out_flow, bgr[:, :, 0]
Example #8
Source File: depth_io.py From sanet_relocal_demo with GNU General Public License v3.0 | 5 votes |
def load_depth_from_tiff(tiff_file_path, div_factor=1000.0): depth = cv2.imread(tiff_file_path, cv2.IMREAD_ANYDEPTH).astype(np.float32) return depth / div_factor
Example #9
Source File: depth_io.py From sanet_relocal_demo with GNU General Public License v3.0 | 5 votes |
def load_depth_from_png(png_file_path, div_factor=1000.0): depth = cv2.imread(png_file_path, cv2.IMREAD_ANYDEPTH).astype(np.float32) return depth / div_factor
Example #10
Source File: read_util.py From sanet_relocal_demo with GNU General Public License v3.0 | 5 votes |
def read_7scenese_depth(png_file_path): depth = cv2.imread(png_file_path, cv2.IMREAD_ANYDEPTH).astype(np.float32) depth[depth >= 65535] = 0 return depth / 1000.0
Example #11
Source File: read_util.py From sanet_relocal_demo with GNU General Public License v3.0 | 5 votes |
def read_7scenese_depth(png_file_path): depth = cv2.imread(png_file_path, cv2.IMREAD_ANYDEPTH).astype(np.float32) depth[depth >= 65535] = 0 return depth / 1000.0
Example #12
Source File: read_util.py From sanet_relocal_demo with GNU General Public License v3.0 | 5 votes |
def read_sun3d_depth(filename, min_depth_thres=1e-5): """ Read depth from a sun3d depth file :param filename: str :return depth as np.float32 array """ depth_pil = cv2.imread(filename, cv2.IMREAD_ANYDEPTH).astype(np.uint16) depth_shifted = (depth_pil >> 3) | (depth_pil << 13) depth_shifted = depth_shifted.astype(np.float32) depth_float = (depth_shifted / 1000) # depth_float[depth_float < min_depth_thres] = min_depth_thres return depth_float
Example #13
Source File: gen_lmdb_cache.py From sanet_relocal_demo with GNU General Public License v3.0 | 5 votes |
def read_raw_depth_uint16(filename): depth_pil = cv2.imread(filename, cv2.IMREAD_ANYDEPTH).astype(np.uint16) depth_shifted = (depth_pil >> 3) | (depth_pil << 13) return depth_shifted
Example #14
Source File: gen_lmdb_cache.py From sanet_relocal_demo with GNU General Public License v3.0 | 5 votes |
def read_depth(self, depth_key, min_depth_thres=1e-5): depth_str = np.fromstring(self.read_by_key(depth_key), dtype=np.uint8) depth = np.asarray(cv2.imdecode(depth_str, cv2.IMREAD_ANYDEPTH)).reshape((240, 320)) # print(depth.dtype) depth = depth.astype(np.float32) depth = (depth / 1000) # depth[depth < min_depth_thres] = min_depth_thres return depth
Example #15
Source File: util.py From in-silico-labeling with Apache License 2.0 | 5 votes |
def read_image(path: str) -> np.ndarray: """Reads a 16-bit grayscale image and converts to floating point.""" logging.info('Reading image: %s', path) image = cv2.imread(path, cv2.IMREAD_ANYDEPTH) assert image is not None assert len(image.shape) == 2, image.shape assert image.dtype == np.uint8 or image.dtype == np.uint16 image = image.astype(np.float32) / np.iinfo(image.dtype).max assert image.min() >= 0, image.min() assert image.max() <= 1.0, image.max() return image
Example #16
Source File: obj_utils.py From monopsr with MIT License | 5 votes |
def get_stereo_point_cloud(sample_name, calib_dir, disp_dir): """ Gets the point cloud for an image calculated from the disparity map :param sample_name: sample name :param calib_dir: directory with calibration files :param disp_dir: directory with disparity images :return: (3, N) point_cloud in the form [[x,...][y,...][z,...]] """ # Read calibration info frame_calib = calib_utils.get_frame_calib(calib_dir, sample_name) stereo_calibration_info = calib_utils.get_stereo_calibration(frame_calib.p2, frame_calib.p3) # Read disparity disp = cv2.imread(disp_dir + '/{}.png'.format(sample_name), cv2.IMREAD_ANYDEPTH) disp = np.float32(disp) disp = np.divide(disp, 256) disp[disp == 0] = 0.1 # Calculate the point cloud point_cloud = calib_utils.depth_from_disparity(disp, stereo_calibration_info) return point_cloud
Example #17
Source File: calib_utils.py From monopsr with MIT License | 5 votes |
def read_disparity(disp_dir, img_idx): """Reads in Disparity file from Kitti Dataset. Keyword Arguments: ------------------ calib_dir : Str Directory of the disparity files. img_idx : Int Index of the image. Returns: -------- disp_img : Numpy Array Contains the disparity image. [] : if file is not found """ disp_path = disp_dir + "/%06d_left_disparity.png" % img_idx if os.path.exists(disp_path): disp_img = cv2.imread(disp_path, cv2.IMREAD_ANYDEPTH) return disp_img else: raise FileNotFoundError('Disparity map not found')
Example #18
Source File: depth_map_utils.py From monopsr with MIT License | 5 votes |
def read_depth_map(depth_map_path): depth_image = cv2.imread(depth_map_path, cv2.IMREAD_ANYDEPTH) depth_map = depth_image / 256.0 # Discard depths less than 10cm from the camera depth_map[depth_map < 0.1] = 0.0 return depth_map.astype(np.float32)
Example #19
Source File: dataset_360D.py From SphericalViewSynthesis with BSD 2-Clause "Simplified" License | 5 votes |
def loadItemMono(self, idx): item = {} if (idx >= self.length): print("Index [{}] out of range. Dataset length: {}".format(idx, self.length)) else: dtmp = np.array(cv2.imread(self.sample["leftDepth"][idx], cv2.IMREAD_ANYDEPTH)) left_depth = torch.from_numpy(dtmp) left_depth.unsqueeze_(0) if self.rescaled: dtmp2 = cv2.resize(dtmp, (dtmp.shape[1] // 2, dtmp.shape[0] // 2)) dtmp4 = cv2.resize(dtmp, (dtmp.shape[1] // 4, dtmp.shape[0] // 4)) left_depth2 = torch.from_numpy(dtmp2) left_depth2.unsqueeze_(0) left_depth4 = torch.from_numpy(dtmp4) left_depth4.unsqueeze_(0) pilRGB = Image.open(self.sample["leftRGB"][idx]) rgb = self.pilToTensor(pilRGB) if self.rescaled: rgb2 = self.pilToTensor(self.resize2(pilRGB)) rgb4 = self.pilToTensor(self.resize4(pilRGB)) item = { "leftRGB": rgb, "leftRGB2": rgb2, "leftRGB4": rgb4, "leftDepth": left_depth, "leftDepth2": left_depth2, "leftDepth4": left_depth4, "leftDepth_filename": os.path.basename(self.sample["leftDepth"][idx][:-4]) } if self.rescaled else { "leftRGB": rgb, "leftDepth": left_depth, "leftDepth_filename": os.path.basename(self.sample["leftDepth"][idx][:-4]) } return item # loads sample from dataset lr mode
Example #20
Source File: util.py From hdr-expandnet with BSD 3-Clause Clear License | 5 votes |
def __getitem__(self, index): dpoint = cv2.imread( self.file_list[index], flags=cv2.IMREAD_ANYDEPTH + cv2.IMREAD_COLOR ) if self.preprocess is not None: dpoint = self.preprocess(dpoint) return dpoint
Example #21
Source File: create_kitti_tf_record.py From motion-rcnn with MIT License | 5 votes |
def _read_flow(flow_fn): "Convert from .png to (h, w, 2) (flow_x, flow_y) float32 array" # read png to bgr in 16 bit unsigned short bgr = cv2.imread(flow_fn, cv2.IMREAD_ANYCOLOR | cv2.IMREAD_ANYDEPTH) rgb = cv2.cvtColor(bgr, cv2.COLOR_BGR2RGB) h, w, _c = rgb.shape assert rgb.dtype == np.uint16 and _c == 3 invalid = rgb[:, :, 2] == 0 # g,r == flow_y,x normalized by height,width and scaled to [0;2**16 - 1] out_flow = (rgb[:, :, :2] - 2 ** 15) / 64.0 print(out_flow.shape, invalid.shape) out_flow[invalid] = np.nan # 0 or another value (e.g., np.nan) return out_flow
Example #22
Source File: create_kitti_tf_record.py From motion-rcnn with MIT License | 5 votes |
def _read_image(filename, rgb=False): "Read (h, w, 3) image from .png." if not rgb: with open(filename, 'rb') as f: image = f.read() return image image = cv2.imread(filename, cv2.IMREAD_ANYCOLOR | cv2.IMREAD_ANYDEPTH) h, w, _c = image.shape assert image.dtype == np.uint8 and _c == 3 if rgb: image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) return image
Example #23
Source File: create_kitti_tf_record.py From motion-rcnn with MIT License | 5 votes |
def _read_disparity_image(filename): "Read (h, w, 1) uint16 KITTI disparity image from .png." image = cv2.imread(filename, cv2.IMREAD_ANYCOLOR | cv2.IMREAD_ANYDEPTH) h, w = image.shape[:2] assert image.dtype == np.uint16 and len(image.shape) == 2 return image
Example #24
Source File: create_vkitti_tf_record.py From motion-rcnn with MIT License | 5 votes |
def _read_flow(flow_fn): "Convert from .png to (h, w, 2) (flow_x, flow_y) float32 array" # read png to bgr in 16 bit unsigned short bgr = cv2.imread(flow_fn, cv2.IMREAD_ANYCOLOR | cv2.IMREAD_ANYDEPTH) h, w, _c = bgr.shape assert bgr.dtype == np.uint16 and _c == 3 # b == invalid flow flag == 0 for sky or other invalid flow invalid = bgr[..., 0] == 0 # g,r == flow_y,x normalized by height,width and scaled to [0;2**16 - 1] out_flow = 2.0 / (2**16 - 1.0) * bgr[..., 2:0:-1].astype('f4') - 1 out_flow[..., 0] *= w - 1 out_flow[..., 1] *= h - 1 out_flow[invalid] = np.nan # 0 or another value (e.g., np.nan) return out_flow
Example #25
Source File: create_vkitti_tf_record.py From motion-rcnn with MIT License | 5 votes |
def _read_image(filename, rgb=False): "Read (h, w, 3) image from .png." if not rgb: with open(filename, 'rb') as f: image = f.read() return image image = cv2.imread(filename, cv2.IMREAD_ANYCOLOR | cv2.IMREAD_ANYDEPTH) h, w, _c = image.shape assert image.dtype == np.uint8 and _c == 3 if rgb: image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) return image
Example #26
Source File: nyuv2.py From DeepV2D with BSD 3-Clause "New" or "Revised" License | 5 votes |
def __getitem__(self, index): data_blob = self.dataset_index[index] num_frames = data_blob['n_frames'] num_samples = self.n_frames inds = np.random.choice(num_frames, num_samples, replace=False) keyframe_index = inds[0] images = [] for i in inds: image_file = data_blob['images'][i] images.append(cv2.imread(image_file)) depth_file = data_blob['depths'][keyframe_index] depth = cv2.imread(depth_file, cv2.IMREAD_ANYDEPTH) depth = (depth.astype(np.float32)) / 5000.0 filled = fill_depth(depth) frameid = data_blob['ids'][keyframe_index] frameid = np.int32(frameid) poses = [] for i in inds: pose_vec = data_blob['poses'][i] pose_mat = pose_vec2mat(pose_vec) poses.append(np.linalg.inv(pose_mat)) images = np.stack(images, axis=0).astype(np.uint8) poses = np.stack(poses, axis=0).astype(np.float32) kvec = intrinsics.copy() return images, poses, depth, filled, filled, kvec, frameid
Example #27
Source File: utls.py From MBLLEN with Apache License 2.0 | 5 votes |
def imread_color(path): img = cv.imread(path, cv.IMREAD_COLOR | cv.IMREAD_ANYDEPTH) / 255. b, g, r = cv.split(img) img_rgb = cv.merge([r, g, b]) return img_rgb # return scipy.misc.imread(path, mode='RGB').astype(np.float) / 255.
Example #28
Source File: dataset_sparsity.py From ip_basic with MIT License | 5 votes |
def main(): input_depth_dir = os.path.expanduser( '~/Kitti/depth/val_selection_cropped/velodyne_raw') images_to_use = sorted(glob.glob(input_depth_dir + '/*')) # Process depth images num_images = len(images_to_use) all_sparsities = np.zeros(num_images) for i in range(num_images): # Print progress sys.stdout.write('\rProcessing index {} / {}'.format(i, num_images - 1)) sys.stdout.flush() depth_image_path = images_to_use[i] # Load depth from image depth_image = cv2.imread(depth_image_path, cv2.IMREAD_ANYDEPTH) # Divide by 256 depth_map = depth_image / 256.0 num_valid_pixels = len(np.where(depth_map > 0.0)[0]) num_pixels = depth_image.shape[0] * depth_image.shape[1] sparsity = num_valid_pixels / (num_pixels * 2/3) all_sparsities[i] = sparsity print('') print('Sparsity') print('Min: ', np.amin(all_sparsities)) print('Max: ', np.amax(all_sparsities)) print('Mean: ', np.mean(all_sparsities)) print('Median: ', np.median(all_sparsities)) plt.hist(all_sparsities, bins=20) plt.show()
Example #29
Source File: distill_dataset.py From Learning-Monocular-Depth-by-Stereo with MIT License | 5 votes |
def read_image_scale(image_path, scale): img = cv2.imread(image_path, cv2.IMREAD_ANYDEPTH) if img is None: print("not finding {}".format(image_path)) img = img.astype(np.float32) / scale return img
Example #30
Source File: create_npy.py From vkitti3D-dataset with MIT License | 5 votes |
def process_frame(image_path: str) -> Tuple[np.ndarray, np.ndarray, str, str]: """ fix given frame :param image_path: path to frame which should be fixed :return: fixed frame """ seq_no = image_path.split('/')[-3] img_no = image_path.split('/')[-1].split('.')[0] depth_path = f"{depth_root}/{seq_no}/clone/{img_no}.png" semantic_path = f"{labels_root}/{seq_no}/clone/{img_no}.png" # BGR -> RGB rgb_map = cv2.imread(image_path)[:, :, (2, 1, 0)] # convert centimeters to meters depth_map = cv2.imread(depth_path, cv2.IMREAD_ANYCOLOR | cv2.IMREAD_ANYDEPTH) / 100. # semantic image semantic_map = cv2.imread(semantic_path)[:, :, (2, 1, 0)] label_map = np.apply_along_axis(lambda r: rgb2label[tuple(r)], 2, semantic_map) # backprojection to camera space x3 = (xv - center_x) / focal_x * depth_map y3 = (yv - center_y) / focal_y * depth_map erg = np.stack((depth_map, -x3, -y3), axis=-1).reshape((-1, 3)) erg = np.hstack((erg, rgb_map.reshape(-1, 3), label_map.reshape(-1, 1))) # delete sky points erg = distance_cutoff(erg, g_cutoff) if g_is_v1: return None, erg, seq_no, img_no else: erg = remove_car_shadows(erg, img_no, g_bb_eps) worldspace = transform2worldspace(erg, img_no) return worldspace, erg, seq_no, img_no