license_plate_recognition/data/retinaface2yolo.py

import os
import os.path
import sys
import torch
import torch.utils.data as data
import cv2
import numpy as np

class WiderFaceDetection(data.Dataset):
    def __init__(self, txt_path, preproc=None):
        self.preproc = preproc
        self.imgs_path = []
        self.words = []
        f = open(txt_path,'r')
        lines = f.readlines()
        isFirst = True
        labels = []
        for line in lines:
            line = line.rstrip()
            if line.startswith('#'):
                if isFirst is True:
                    isFirst = False
                else:
                    labels_copy = labels.copy()
                    self.words.append(labels_copy)
                    labels.clear()
                path = line[2:]
                path = txt_path.replace('label.txt','images/') + path
                self.imgs_path.append(path)
            else:
                line = line.split(' ')
                label = [float(x) for x in line]
                labels.append(label)

        self.words.append(labels)

    def __len__(self):
        return len(self.imgs_path)

    def __getitem__(self, index):
        img = cv2.imread(self.imgs_path[index])
        height, width, _ = img.shape

        labels = self.words[index]
        annotations = np.zeros((0, 15))
        if len(labels) == 0:
            return annotations
        for idx, label in enumerate(labels):
            annotation = np.zeros((1, 15))
            # bbox
            annotation[0, 0] = label[0]  # x1
            annotation[0, 1] = label[1]  # y1
            annotation[0, 2] = label[0] + label[2]  # x2
            annotation[0, 3] = label[1] + label[3]  # y2

            # landmarks
            annotation[0, 4] = label[4]    # l0_x
            annotation[0, 5] = label[5]    # l0_y
            annotation[0, 6] = label[7]    # l1_x
            annotation[0, 7] = label[8]    # l1_y
            annotation[0, 8] = label[10]   # l2_x
            annotation[0, 9] = label[11]   # l2_y
            annotation[0, 10] = label[13]  # l3_x
            annotation[0, 11] = label[14]  # l3_y
            annotation[0, 12] = label[16]  # l4_x
            annotation[0, 13] = label[17]  # l4_y
            if (annotation[0, 4]<0):
                annotation[0, 14] = -1
            else:
                annotation[0, 14] = 1

            annotations = np.append(annotations, annotation, axis=0)
        target = np.array(annotations)
        if self.preproc is not None:
            img, target = self.preproc(img, target)

        return torch.from_numpy(img), target

def detection_collate(batch):
    """Custom collate fn for dealing with batches of images that have a different
    number of associated object annotations (bounding boxes).

    Arguments:
        batch: (tuple) A tuple of tensor images and lists of annotations

    Return:
        A tuple containing:
            1) (tensor) batch of images stacked on their 0 dim
            2) (list of tensors) annotations for a given image are stacked on 0 dim
    """
    targets = []
    imgs = []
    for _, sample in enumerate(batch):
        for _, tup in enumerate(sample):
            if torch.is_tensor(tup):
                imgs.append(tup)
            elif isinstance(tup, type(np.empty(0))):
                annos = torch.from_numpy(tup).float()
                targets.append(annos)

    return (torch.stack(imgs, 0), targets)

save_path = '/ssd_1t/derron/yolov5-face/data/widerface/train'
aa=WiderFaceDetection("/ssd_1t/derron/yolov5-face/data/widerface/widerface/train/label.txt")
for i in range(len(aa.imgs_path)):
    print(i, aa.imgs_path[i])
    img = cv2.imread(aa.imgs_path[i])
    base_img = os.path.basename(aa.imgs_path[i])
    base_txt = os.path.basename(aa.imgs_path[i])[:-4] +".txt"
    save_img_path = os.path.join(save_path, base_img)
    save_txt_path = os.path.join(save_path, base_txt)
    with open(save_txt_path, "w") as f:
        height, width, _ = img.shape
        labels = aa.words[i]
        annotations = np.zeros((0, 14))
        if len(labels) == 0:
            continue
        for idx, label in enumerate(labels):
            annotation = np.zeros((1, 14))
            # bbox
            label[0] = max(0, label[0])
            label[1] = max(0, label[1])
            label[2] = min(width -  1, label[2])
            label[3] = min(height - 1, label[3])
            annotation[0, 0] = (label[0] + label[2] / 2) / width  # cx
            annotation[0, 1] = (label[1] + label[3] / 2) / height  # cy
            annotation[0, 2] = label[2] / width  # w
            annotation[0, 3] = label[3] / height  # h
            #if (label[2] -label[0]) < 8 or (label[3] - label[1]) < 8:
            #    img[int(label[1]):int(label[3]), int(label[0]):int(label[2])] = 127
            #    continue
            # landmarks
            annotation[0, 4] = label[4] / width  # l0_x
            annotation[0, 5] = label[5] / height  # l0_y
            annotation[0, 6] = label[7] / width  # l1_x
            annotation[0, 7] = label[8]  / height # l1_y
            annotation[0, 8] = label[10] / width  # l2_x
            annotation[0, 9] = label[11] / height  # l2_y
            annotation[0, 10] = label[13] / width  # l3_x
            annotation[0, 11] = label[14] / height  # l3_y
            annotation[0, 12] = label[16] / width  # l4_x
            annotation[0, 13] = label[17] / height  # l4_y
            str_label="0 "
            for i in range(len(annotation[0])):
                str_label =str_label+" "+str(annotation[0][i])
            str_label = str_label.replace('[', '').replace(']', '')
            str_label = str_label.replace(',', '') + '\n'
            f.write(str_label)
    cv2.imwrite(save_img_path, img)