conveying_belt_recognition/deep-hough-transform-master/data/prepare_data_NKL.py

import numpy as np
import cv2
from PIL import Image
import argparse
import os, sys
from os.path import join, split, splitext, abspath, isfile
sys.path.insert(0, abspath(".."))
sys.path.insert(0, abspath("."))
from mask_component_utils import Line, LineAnnotation, line2hough
import matplotlib
import matplotlib.pyplot as plt
from skimage.measure import label, regionprops

parser = argparse.ArgumentParser(description="Prepare semantic line data format.")
parser.add_argument('--root', type=str, required=True, help='the data root dir.')
parser.add_argument('--label', type=str, required=True, help='the label root dir.')
parser.add_argument('--save-dir', type=str, required=True, help='save-dir')
parser.add_argument('--fixsize', type=int, default=None, help='fix resize of images and annotations')
parser.add_argument('--numangle', type=int, default=100)
parser.add_argument('--numrho', type=int, default=100)
args = parser.parse_args()

label_path = abspath(args.label)
image_dir = abspath(args.root)
save_dir = abspath(args.save_dir)
os.makedirs(save_dir, exist_ok=True)
def nearest8(x):
    return int(np.round(x/8)*8)

def vis_anno(image, annotation):
    mask = annotation.oriental_mask()
    mask_sum = mask.sum(axis=0).astype(bool)
    image_cp = image.copy()
    image_cp[mask_sum, ...] = [0, 255, 0]
    mask = np.zeros((image.shape[0], image.shape[1]))
    mask[mask_sum] = 1
    return image_cp, mask

def check(y, x, H, W):
    x = max(0, x)
    y = max(0, y)
    x = min(x, W-1)
    y = min(y, H-1)
    return y, x

labels_files = [i for i in os.listdir(label_path) if i.endswith(".txt")]

num_samples = len(labels_files)

stastic = np.zeros(20)
total_nums = 0
angle_stastic = np.zeros(180)
total_lines = 0


for idx, label_file in enumerate(labels_files):
    filename, _ = splitext(label_file)
    print("Processing %s [%d/%d]..." % (filename, idx+1, len(labels_files)))
    if isfile(join(image_dir, filename+".jpg")):
        im = cv2.imread(join(image_dir, filename+".jpg"))
        H, W = im.shape[0], im.shape[1]
        scale_H, scale_W = args.fixsize / H, args.fixsize / W
        im = cv2.resize(im, (args.fixsize, args.fixsize))
    else:
        print("Warning: image %s doesnt exist!" % join(image_dir, filename+".jpg"))
        continue
    for argument in range(2):
        if argument == 0:
            lines = []
            with open(join(label_path, label_file)) as f:
                data = f.readlines()[0].split(' ')
                nums = int(data[0])
                stastic[nums] += 1
                total_nums += nums
                
                if int(nums) == 0:
                    print("Warning: image has no semantic line : %s" % (filename))

                for i in range(nums):
                    y1, x1 = check(int(data[i*4+2]), int(data[i*4+1]), H, W)
                    y2, x2 = check(int(data[i*4+4]), int(data[i*4+3]), H, W)
                    line = Line([y1, x1, y2, x2])
                    angle = line.angle()
                    angle_stastic[int((angle / np.pi + 0.5) * 180)] += 1
                    total_lines += 1
                    line.rescale(scale_H, scale_W)
                    lines.append(line)
            
            annotation = LineAnnotation(size=[args.fixsize, args.fixsize], lines=lines)
        else:
            im = cv2.flip(im, 1)
            filename = filename + '_flip'
            lines = []
            with open(join(label_path, label_file)) as f:
                data = f.readlines()[0].split(' ')
                for i in range(int(data[0])):
                    y1, x1 = check(int(data[i*4+2]), W-1-int(data[i*4+1]), H, W)
                    y2, x2 = check(int(data[i*4+4]), W-1-int(data[i*4+3]), H, W)
                    line = Line([y1, x1, y2, x2])
                    line.rescale(scale_H, scale_W)
                    lines.append(line)
            
            annotation = LineAnnotation(size=[args.fixsize, args.fixsize], lines=lines)

        # resize image and annotations
        if args.fixsize is not None:
            newH = nearest8(args.fixsize)
            newW = nearest8(args.fixsize)
        else:
            newH = nearest8(H)
            newW = nearest8(W)

        im = cv2.resize(im, (newW, newH))
        annotation.resize(size=[newH, newW])
        vis, mask = vis_anno(im, annotation)

        hough_space_label = np.zeros((args.numangle, args.numrho))
        for l in annotation.lines:
            theta, r = line2hough(l, numAngle=args.numangle, numRho=args.numrho, size=(newH, newW))
            hough_space_label[theta, r] += 1

        hough_space_label = cv2.GaussianBlur(hough_space_label, (5,5), 0)

        if hough_space_label.max() > 0:
            hough_space_label = hough_space_label / hough_space_label.max()

        gt_coords = []
        for l in annotation.lines:
            gt_coords.append(l.coord)
        gt_coords = np.array(gt_coords)
        data = dict({
            "hough_space_label8": hough_space_label,
            "coords": gt_coords
        })

        save_name = os.path.join(save_dir, filename)

        np.save(save_name, data)
        cv2.imwrite(save_name + '.jpg', im)
        # cv2.imwrite(save_name + '_p_label.jpg', hough_space_label*255)
        # cv2.imwrite(save_name + '_vis.jpg', vis)
        cv2.imwrite(save_name + '_mask.jpg', mask*255)
#print(stastic)
#print(angle_stastic.astype(np.int))