def get_list_file(file_dir): #train_feature_list = file_io.get_listfile(file_dir + "Resized_images/","resnet_hypercolumn") train_feature_list = file_io.get_listfile(file_dir + "Resized_images/", "jpg") train_label = file_io.get_listfile(file_dir + "Resized_GTdensity/", "npy") train_list = list() for tf in train_feature_list: #tf_label = tf.replace(".resnet_hypercolumn","dots.png.npy").replace("images","GTdensity") tf_label = tf.replace(".jpg", "dots.png.npy").replace("images", "GTdensity") assert (tf_label in train_label) train_list.append(tf + " " + tf_label) return train_list
def sample(avid_dir_list): avi_dir_list = file_io.get_dir_list(avid_dir_list) print(avi_dir_list) for avi_dir in avi_dir_list: avi_file_list = file_io.get_listfile(avi_dir, ".avi") avi_file_list.sort() for avi in avi_file_list: image_dir = avi.replace(".avi", "") command = "ffmpeg -i " + avi + " " + image_dir + "/%06d.jpg" os.system(command)
def main(_): maybe_download_and_extract() video_dir = "/home/mscvadmin/traffic_video_analysis/data/Cam691/" video_list = file_io.get_listfile(video_dir, ".avi") for video in video_list: image_dir = video.replace(".avi", "/") name_list = os.listdir(image_dir) sess = tf.Session() feature_tensor = init_tensor(sess) for n in name_list: if n.endswith("_resize.jpg"): image = (FLAGS.image_file if FLAGS.image_file else os.path.join(FLAGS.model_dir, image_dir + n)) feature_tensor_v = run_sess(sess, feature_tensor, image) feature_name = image_dir + n.replace(".jpg", ".mixed10") save_feature(feature_tensor_v, feature_name)
mask = np.array(img) return mask if __name__ == "__main__": mask_dir_list = file_io.get_dir_list("data/Cam253") mask_list = [] for mask_dir in mask_dir_list: if (mask_dir.endswith(".msk")): mask_list.append(mask_dir) print(mask_list[0]) for mask in mask_list: image_dir_name = mask.replace(".msk", "") image_list = file_io.get_listfile(image_dir_name, "jpg") if (mask == "data/data_new/Training_Data/Cam181/01.msk"): mask_bin = cv2.imread(mask_dir + "/01_mask.jpg") mask_bin = mask_bin[:, :, 1] mask_bin /= 255 else: try: mask_pts = load_mask(mask) mask_bin = gen_mask_image(mask_pts) except: mask_pts = load_mask(mask) print(mask) print("mask is wrong") exit(1) continue
import file_io if __name__ == "__main__": file_list_dir = "../file_list/" data_ext = "_resize.jpg" label_ext = "_resize.desmap" file_dir = "../data/" cam_dir_list = file_io.get_dir_list(file_dir) data_list = list() for cam_dir in cam_dir_list: video_list = file_io.get_listfile(cam_dir, ".avi") for file_name in video_list: file_dir_name = file_name.replace(".avi", "/") data_list += file_io.get_listfile(file_dir_name, data_ext) file_io.save_file(data_list, file_list_dir + 'image_name_list.txt', False)
import scipy.io as sio import numpy as np import file_io import cv2 mat_dir = "../data/toJeff/Train/Resized_GTdensity/" mat_list = file_io.get_listfile(mat_dir, "mat") for f in mat_list: mat = sio.loadmat(f) np_array = mat['gtDensities'] np_array = np_array.astype(np.float32) np_array = cv2.resize(np_array, (227,227)) np_name = f.replace(".mat",".npy") np_array.tofile(np_name)