def get_data()

in utils_cv/tracking/references/fairmot/datasets/dataset/jde.py [0:0]

• 64 lines of code
• 15 McCabe index (conditional complexity)

    def get_data(self, img_path, label_path):
        height = self.height
        width = self.width
        img = cv2.imread(img_path)  # BGR
        if img is None:
            raise ValueError('File corrupt {}'.format(img_path))
        augment_hsv = True
        if self.augment and augment_hsv:
            # SV augmentation by 50%
            fraction = 0.50
            img_hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
            S = img_hsv[:, :, 1].astype(np.float32)
            V = img_hsv[:, :, 2].astype(np.float32)

            a = (random.random() * 2 - 1) * fraction + 1
            S *= a
            if a > 1:
                np.clip(S, a_min=0, a_max=255, out=S)

            a = (random.random() * 2 - 1) * fraction + 1
            V *= a
            if a > 1:
                np.clip(V, a_min=0, a_max=255, out=V)

            img_hsv[:, :, 1] = S.astype(np.uint8)
            img_hsv[:, :, 2] = V.astype(np.uint8)
            cv2.cvtColor(img_hsv, cv2.COLOR_HSV2BGR, dst=img)

        h, w, _ = img.shape
        img, ratio, padw, padh = letterbox(img, height=height, width=width)

        # Load labels
        if os.path.isfile(label_path):
            labels0 = np.loadtxt(label_path, dtype=np.float32).reshape(-1, 6)

            # Normalized xywh to pixel xyxy format
            labels = labels0.copy()
            labels[:, 2] = ratio * w * (labels0[:, 2] - labels0[:, 4] / 2) + padw
            labels[:, 3] = ratio * h * (labels0[:, 3] - labels0[:, 5] / 2) + padh
            labels[:, 4] = ratio * w * (labels0[:, 2] + labels0[:, 4] / 2) + padw
            labels[:, 5] = ratio * h * (labels0[:, 3] + labels0[:, 5] / 2) + padh
        else:
            labels = np.array([])

        # Augment image and labels
        if self.augment:
            img, labels, M = random_affine(img, labels, degrees=(-5, 5), translate=(0.10, 0.10), scale=(0.50, 1.20))

        plotFlag = False
        if plotFlag:
            import matplotlib
            matplotlib.use('Agg')
            import matplotlib.pyplot as plt
            plt.figure(figsize=(50, 50))
            plt.imshow(img[:, :, ::-1])
            plt.plot(labels[:, [2, 4, 4, 2, 2]].T, labels[:, [3, 3, 5, 5, 3]].T, '.-')
            plt.axis('off')
            plt.savefig('test.jpg')
            time.sleep(10)

        nL = len(labels)
        if nL > 0:
            # convert xyxy to xywh
            labels[:, 2:6] = xyxy2xywh(labels[:, 2:6].copy())  # / height
            labels[:, 2] /= width
            labels[:, 3] /= height
            labels[:, 4] /= width
            labels[:, 5] /= height
        if self.augment:
            # random left-right flip
            lr_flip = True
            if lr_flip & (random.random() > 0.5):
                img = np.fliplr(img)
                if nL > 0:
                    labels[:, 2] = 1 - labels[:, 2]

        img = np.ascontiguousarray(img[:, :, ::-1])  # BGR to RGB

        if self.transforms is not None:
            img = self.transforms(img)

        return img, labels, img_path, (h, w)