import os import sys import torch import argparse import numpy as np import os.path as osp import re from imageio import imread, imwrite import torch.nn.functional as F sys.path.append('.') from utils.flow_generation.liteflownet.run import estimate def read(file): if file.endswith('.float3'): return readFloat(file) elif file.endswith('.flo'): return readFlow(file) elif file.endswith('.ppm'): return readImage(file) elif file.endswith('.pgm'): return readImage(file) elif file.endswith('.png'): return readImage(file) elif file.endswith('.jpg'): return readImage(file) elif file.endswith('.pfm'): return readPFM(file)[0] else: raise Exception('don\'t know how to read %s' % file) def write(file, data): if file.endswith('.float3'): return writeFloat(file, data) elif file.endswith('.flo'): return writeFlow(file, data) elif file.endswith('.ppm'): return writeImage(file, data) elif file.endswith('.pgm'): return writeImage(file, data) elif file.endswith('.png'): return writeImage(file, data) elif file.endswith('.jpg'): return writeImage(file, data) elif file.endswith('.pfm'): return writePFM(file, data) else: raise Exception('don\'t know how to write %s' % file) def readPFM(file): file = open(file, 'rb') color = None width = None height = None scale = None endian = None header = file.readline().rstrip() if header.decode("ascii") == 'PF': color = True elif header.decode("ascii") == 'Pf': color = False else: raise Exception('Not a PFM file.') dim_match = re.match(r'^(\d+)\s(\d+)\s$', file.readline().decode("ascii")) if dim_match: width, height = list(map(int, dim_match.groups())) else: raise Exception('Malformed PFM header.') scale = float(file.readline().decode("ascii").rstrip()) if scale < 0: endian = '<' scale = -scale else: endian = '>' data = np.fromfile(file, endian + 'f') shape = (height, width, 3) if color else (height, width) data = np.reshape(data, shape) data = np.flipud(data) return data, scale def writePFM(file, image, scale=1): file = open(file, 'wb') color = None if image.dtype.name != 'float32': raise Exception('Image dtype must be float32.') image = np.flipud(image) if len(image.shape) == 3 and image.shape[2] == 3: color = True elif len(image.shape) == 2 or len(image.shape) == 3 and image.shape[2] == 1: color = False else: raise Exception('Image must have H x W x 3, H x W x 1 or H x W dimensions.') file.write('PF\n' if color else 'Pf\n'.encode()) file.write('%d %d\n'.encode() % (image.shape[1], image.shape[0])) endian = image.dtype.byteorder if endian == '<' or endian == '=' and sys.byteorder == 'little': scale = -scale file.write('%f\n'.encode() % scale) image.tofile(file) def readFlow(name): if name.endswith('.pfm') or name.endswith('.PFM'): return readPFM(name)[0][:,:,0:2] f = open(name, 'rb') header = f.read(4) if header.decode("utf-8") != 'PIEH': raise Exception('Flow file header does not contain PIEH') width = np.fromfile(f, np.int32, 1).squeeze() height = np.fromfile(f, np.int32, 1).squeeze() flow = np.fromfile(f, np.float32, width * height * 2).reshape((height, width, 2)) return flow.astype(np.float32) def readImage(name): if name.endswith('.pfm') or name.endswith('.PFM'): data = readPFM(name)[0] if len(data.shape)==3: return data[:,:,0:3] else: return data return imread(name) def writeImage(name, data): if name.endswith('.pfm') or name.endswith('.PFM'): return writePFM(name, data, 1) return imwrite(name, data) def writeFlow(name, flow): f = open(name, 'wb') f.write('PIEH'.encode('utf-8')) np.array([flow.shape[1], flow.shape[0]], dtype=np.int32).tofile(f) flow = flow.astype(np.float32) flow.tofile(f) def readFloat(name): f = open(name, 'rb') if(f.readline().decode("utf-8")) != 'float\n': raise Exception('float file %s did not contain keyword' % name) dim = int(f.readline()) dims = [] count = 1 for i in range(0, dim): d = int(f.readline()) dims.append(d) count *= d dims = list(reversed(dims)) data = np.fromfile(f, np.float32, count).reshape(dims) if dim > 2: data = np.transpose(data, (2, 1, 0)) data = np.transpose(data, (1, 0, 2)) return data def writeFloat(name, data): f = open(name, 'wb') dim=len(data.shape) if dim>3: raise Exception('bad float file dimension: %d' % dim) f.write(('float\n').encode('ascii')) f.write(('%d\n' % dim).encode('ascii')) if dim == 1: f.write(('%d\n' % data.shape[0]).encode('ascii')) else: f.write(('%d\n' % data.shape[1]).encode('ascii')) f.write(('%d\n' % data.shape[0]).encode('ascii')) for i in range(2, dim): f.write(('%d\n' % data.shape[i]).encode('ascii')) data = data.astype(np.float32) if dim==2: data.tofile(f) else: np.transpose(data, (2, 0, 1)).tofile(f) def check_dim_and_resize(tensor_list): shape_list = [] for t in tensor_list: shape_list.append(t.shape[2:]) if len(set(shape_list)) > 1: desired_shape = shape_list[0] print(f'Inconsistent size of input video frames. All frames will be resized to {desired_shape}') resize_tensor_list = [] for t in tensor_list: resize_tensor_list.append(torch.nn.functional.interpolate(t, size=tuple(desired_shape), mode='bilinear')) tensor_list = resize_tensor_list return tensor_list parser = argparse.ArgumentParser( prog = 'AMT', description = 'Flow generation', ) parser.add_argument('-r', '--root', default='../data/vimeo_triplet') args = parser.parse_args() vimeo90k_dir = args.root vimeo90k_sequences_dir = osp.join(vimeo90k_dir, 'sequences') vimeo90k_flow_dir = osp.join(vimeo90k_dir, 'flow') def pred_flow(img1, img2): img1 = torch.from_numpy(img1).float().permute(2, 0, 1) / 255.0 img2 = torch.from_numpy(img2).float().permute(2, 0, 1) / 255.0 flow = estimate(img1, img2) flow = flow.permute(1, 2, 0).cpu().numpy() return flow print('Built Flow Path') if not osp.exists(vimeo90k_flow_dir): os.makedirs(vimeo90k_flow_dir) for sequences_path in sorted(os.listdir(vimeo90k_sequences_dir)): vimeo90k_sequences_path_dir = osp.join(vimeo90k_sequences_dir, sequences_path) vimeo90k_flow_path_dir = osp.join(vimeo90k_flow_dir, sequences_path) if not osp.exists(vimeo90k_flow_path_dir): os.mkdir(vimeo90k_flow_path_dir) for sequences_id in sorted(os.listdir(vimeo90k_sequences_path_dir)): vimeo90k_flow_id_dir = osp.join(vimeo90k_flow_path_dir, sequences_id) if not osp.exists(vimeo90k_flow_id_dir): os.mkdir(vimeo90k_flow_id_dir) for sequences_path in sorted(os.listdir(vimeo90k_sequences_dir)): vimeo90k_sequences_path_dir = os.path.join(vimeo90k_sequences_dir, sequences_path) vimeo90k_flow_path_dir = os.path.join(vimeo90k_flow_dir, sequences_path) for sequences_id in sorted(os.listdir(vimeo90k_sequences_path_dir)): vimeo90k_sequences_id_dir = os.path.join(vimeo90k_sequences_path_dir, sequences_id) vimeo90k_flow_id_dir = os.path.join(vimeo90k_flow_path_dir, sequences_id) img0_path = vimeo90k_sequences_id_dir + '/im1.png' imgt_path = vimeo90k_sequences_id_dir + '/im2.png' img1_path = vimeo90k_sequences_id_dir + '/im3.png' flow_t0_path = vimeo90k_flow_id_dir + '/flow_t0.flo' flow_t1_path = vimeo90k_flow_id_dir + '/flow_t1.flo' img0 = read(img0_path) imgt = read(imgt_path) img1 = read(img1_path) flow_t0 = pred_flow(imgt, img0) flow_t1 = pred_flow(imgt, img1) write(flow_t0_path, flow_t0) write(flow_t1_path, flow_t1) print('Written Sequences {}'.format(sequences_path))