<reponame>coldenheart/123
"""Licensed under the Apache License, Version 2.0 (the "License");
You may not use this file except in compliance with the License.
You may obtain a copy of the License at

    http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License."""

import numpy as np


def sigmoid(x):
    """sigmoid"""
    x = x.astype("float32")
    return 1 / (1 + np.exp(-x))


def yolo_head(feats, anchors, num_classes, input_shape, calc_loss=False):
    """Convert final layer features to bounding box parameters."""

    feats = feats.astype("float32")
    num_anchors = len(anchors)  # 3
    anchors_tensor = np.reshape(anchors, [1, 1, 1, num_anchors, 2])

    grid_shape = np.shape(feats)[1:3]

    grid_y = np.tile(np.reshape(
        range(0, grid_shape[0]), [-1, 1, 1, 1]), [1, grid_shape[1], 1, 1])
    grid_x = np.tile(np.reshape(
        range(0, grid_shape[1]), [1, -1, 1, 1]), [grid_shape[0], 1, 1, 1])

    grid = np.concatenate([grid_x, grid_y], axis=-1)
    grid = grid.astype("float32")

    feats = np.reshape(
        feats, [-1, grid_shape[0], grid_shape[1], num_anchors, num_classes + 5])
    box_xy = (sigmoid(feats[..., :2]) + grid) / \
        np.cast[feats.dtype](grid_shape[::-1])
    box_wh = np.exp(feats[..., 2:4]) * anchors_tensor / \
        np.cast[feats.dtype](input_shape[::-1])

    box_confidence = sigmoid(feats[..., 4:5])
    box_class_probs = sigmoid(feats[..., 5:])

    box_wh = box_wh.astype("float32")

    ret = {
        "box_xy": box_xy,
        "box_wh": box_wh,
    }
    if calc_loss == True:
        ret["grid"] = grid
        ret["feats"] = feats
    else:
        ret["box_confidence"] = box_confidence
        ret["box_class_probs"] = box_class_probs
    return ret

def yolo_correct_boxes(box_xy, box_wh, input_shape, image_shape):
    """Get corrected boxes"""
    box_yx = box_xy[..., ::-1]
    box_hw = box_wh[..., ::-1]

    input_shape = np.cast[box_yx.dtype](input_shape)
    image_shape = np.cast[box_yx.dtype](image_shape)
    new_shape = np.round(image_shape * np.min(input_shape / image_shape))

    offset = (input_shape - new_shape) / 2. / input_shape
    scale = input_shape / new_shape

    box_yx = (box_yx - offset) * scale
    box_hw *= scale

    box_mins = box_yx - (box_hw / 2.)
    box_maxes = box_yx + (box_hw / 2.)

    boxes = np.concatenate([
        box_mins[..., 0:1],  # y_min
        box_mins[..., 1:2],  # x_min
        box_maxes[..., 0:1],  # y_max
        box_maxes[..., 1:2]  # x_max
    ], axis=-1)

    # Scale boxes back to original image shape.
    boxes *= np.concatenate([image_shape, image_shape], axis=-1)

    return boxes


def yolo_boxes_and_scores(feats, anchors, num_classes, input_shape, image_shape):
    """Process Conv layer output"""
    heads = yolo_head(feats, anchors, num_classes, input_shape)
    boxes = yolo_correct_boxes(heads['box_xy'], heads['box_wh'], input_shape, image_shape)
    boxes = np.reshape(boxes, [-1, 4])
    box_scores = heads['box_confidence'] * heads['box_class_probs']
    box_scores = np.reshape(box_scores, [-1, num_classes])

    return (boxes, box_scores)


def nms(bounding_boxes, confidence_score, threshold):
    """non maximum suppression for filter boxes"""
    # If no bounding boxes, return empty list
    if len(bounding_boxes) == 0:
        return [], []

    # Bounding boxes
    boxes = np.array(bounding_boxes)

    # coordinates of bounding boxes
    start_x = boxes[:, 0]
    start_y = boxes[:, 1]
    end_x = boxes[:, 2]
    end_y = boxes[:, 3]

    # Confidence scores of bounding boxes
    score = np.array(confidence_score)

    # Compute areas of bounding boxes
    areas = (end_x - start_x + 1) * (end_y - start_y + 1)

    # Sort by confidence score of bounding boxes
    order = np.argsort(score)[::-1]
    keep = []
    # Iterate bounding boxes
    while order.size > 0:
        # The index of largest confidence score
        index = order[0]
        keep.append(index)

        # Compute ordinates of intersection-over-union(IOU)
        x1 = np.maximum(start_x[index], start_x[order[:-1]])
        x2 = np.minimum(end_x[index], end_x[order[:-1]])
        y1 = np.maximum(start_y[index], start_y[order[:-1]])
        y2 = np.minimum(end_y[index], end_y[order[:-1]])

        # Compute areas of intersection-over-union
        w = np.maximum(0.0, x2 - x1 + 1)
        h = np.maximum(0.0, y2 - y1 + 1)
        intersection = w * h

        # Compute the ratio between intersection and union
        ratio = intersection / (areas[index] + areas[order[1:]] - intersection)
        inds = np.where(ratio <= threshold)[0]
        order = order[inds + 1]

    picked_boxes = [bounding_boxes[i] for i in keep]

    if not score.shape:
        picked_score = [score]
    else:
        picked_score = [score[i] for i in keep]

    return picked_boxes, picked_score


def yolo_eval(yolo_outputs, anchors, num_classes, image_shape, score_threshold=.5, iou_threshold=.45):
    """
    Obtain predicted boxes axis and corresponding scores

    Args:
        yolo_outputs: output (3 feature maps) of YOLO V3 model, sizes are 1*13*13*18; 1*26*26*18; 1*52*52*18 seperately
        anchors: anchors pre-calculated
        num_classes: only 1 class here, which is "head"
        image_shape: original image input

    Returns:
        predicted boxes axis and corresponding scores
    """
    num_layers = len(yolo_outputs)
    anchor_mask = [[6, 7, 8], [3, 4, 5], [0, 1, 2]]
    yolo_output_0 = yolo_outputs[0]
    input_shape = [yolo_output_0.shape[1] * 32, yolo_output_0.shape[2] * 32]
    input_shape = np.array(input_shape)
    boxes = []
    box_scores = []

    for l in range(num_layers):
        _boxes, _box_scores = yolo_boxes_and_scores(yolo_outputs[l],
                                                    anchors[anchor_mask[l]], num_classes, input_shape, image_shape)
        boxes.append(_boxes)
        box_scores.append(_box_scores)

    boxes = np.concatenate(boxes, axis=0)
    box_scores = np.concatenate(box_scores, axis=0)

    mask = box_scores >= score_threshold

    class_boxes = boxes[np.nonzero(box_scores * mask)[0], :]
    class_box_scores = box_scores[np.nonzero(box_scores * mask)[0], :]

    class_box_scores = np.squeeze(class_box_scores)

    box, score = nms(class_boxes, class_box_scores, iou_threshold)

    return box, score


def get_box_img(image, box_axis):
    """
    Pack detected head area and corresponding location in the source image for WHENet

    Args:
        image: source image read from camera
        box_axis: location of boxes detected in YOLOV3
        box_score: scores of boxes detected in YOLOV3

    Returns:
        nparryList: head area 
        boxList: location in the source image
    """
    nparryList = []
    boxList = []

    for i in range(len(box_axis)):

        top, left, bottom, right = box_axis[i]
        top_modified = top - abs(top - bottom) / 10
        bottom_modified = bottom + abs(top - bottom) / 10
        left_modified = left - abs(left - right) / 5
        right_modified = right + abs(left - right) / 5

        top_modified = max(0, np.round(top_modified).astype('int32'))
        left_modified = max(0, np.round(left_modified).astype('int32'))
        bottom_modified = min(image.shape[0], np.round(
            bottom_modified).astype('int32'))
        right_modified = min(image.shape[1], np.round(
            right_modified).astype('int32'))

        boxList.append([left_modified, right_modified,
                       top_modified, bottom_modified])
        nparryList.append(
            image[top_modified:bottom_modified, left_modified:right_modified])

    return nparryList, boxList

"""Licensed under the Apache License, Version 2.0 (the "License");
You may not use this file except in compliance with the License.
You may obtain a copy of the License at

    http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License."""

import numpy as np


def sigmoid(x):
    """sigmoid"""
    x = x.astype("float32")
    return 1 / (1 + np.exp(-x))


def yolo_head(feats, anchors, num_classes, input_shape, calc_loss=False):
    """Convert final layer features to bounding box parameters."""

    feats = feats.astype("float32")
    num_anchors = len(anchors)  # 3
    anchors_tensor = np.reshape(anchors, [1, 1, 1, num_anchors, 2])

    grid_shape = np.shape(feats)[1:3]

    grid_y = np.tile(np.reshape(
        range(0, grid_shape[0]), [-1, 1, 1, 1]), [1, grid_shape[1], 1, 1])
    grid_x = np.tile(np.reshape(
        range(0, grid_shape[1]), [1, -1, 1, 1]), [grid_shape[0], 1, 1, 1])

    grid = np.concatenate([grid_x, grid_y], axis=-1)
    grid = grid.astype("float32")

    feats = np.reshape(
        feats, [-1, grid_shape[0], grid_shape[1], num_anchors, num_classes + 5])
    box_xy = (sigmoid(feats[..., :2]) + grid) / \
        np.cast[feats.dtype](grid_shape[::-1])
    box_wh = np.exp(feats[..., 2:4]) * anchors_tensor / \
        np.cast[feats.dtype](input_shape[::-1])

    box_confidence = sigmoid(feats[..., 4:5])
    box_class_probs = sigmoid(feats[..., 5:])

    box_wh = box_wh.astype("float32")

    ret = {
        "box_xy": box_xy,
        "box_wh": box_wh,
    }
    if calc_loss == True:
        ret["grid"] = grid
        ret["feats"] = feats
    else:
        ret["box_confidence"] = box_confidence
        ret["box_class_probs"] = box_class_probs
    return ret

def yolo_correct_boxes(box_xy, box_wh, input_shape, image_shape):
    """Get corrected boxes"""
    box_yx = box_xy[..., ::-1]
    box_hw = box_wh[..., ::-1]

    input_shape = np.cast[box_yx.dtype](input_shape)
    image_shape = np.cast[box_yx.dtype](image_shape)
    new_shape = np.round(image_shape * np.min(input_shape / image_shape))

    offset = (input_shape - new_shape) / 2. / input_shape
    scale = input_shape / new_shape

    box_yx = (box_yx - offset) * scale
    box_hw *= scale

    box_mins = box_yx - (box_hw / 2.)
    box_maxes = box_yx + (box_hw / 2.)

    boxes = np.concatenate([
        box_mins[..., 0:1],  # y_min
        box_mins[..., 1:2],  # x_min
        box_maxes[..., 0:1],  # y_max
        box_maxes[..., 1:2]  # x_max
    ], axis=-1)

    # Scale boxes back to original image shape.
    boxes *= np.concatenate([image_shape, image_shape], axis=-1)

    return boxes


def yolo_boxes_and_scores(feats, anchors, num_classes, input_shape, image_shape):
    """Process Conv layer output"""
    heads = yolo_head(feats, anchors, num_classes, input_shape)
    boxes = yolo_correct_boxes(heads['box_xy'], heads['box_wh'], input_shape, image_shape)
    boxes = np.reshape(boxes, [-1, 4])
    box_scores = heads['box_confidence'] * heads['box_class_probs']
    box_scores = np.reshape(box_scores, [-1, num_classes])

    return (boxes, box_scores)


def nms(bounding_boxes, confidence_score, threshold):
    """non maximum suppression for filter boxes"""
    # If no bounding boxes, return empty list
    if len(bounding_boxes) == 0:
        return [], []

    # Bounding boxes
    boxes = np.array(bounding_boxes)

    # coordinates of bounding boxes
    start_x = boxes[:, 0]
    start_y = boxes[:, 1]
    end_x = boxes[:, 2]
    end_y = boxes[:, 3]

    # Confidence scores of bounding boxes
    score = np.array(confidence_score)

    # Compute areas of bounding boxes
    areas = (end_x - start_x + 1) * (end_y - start_y + 1)

    # Sort by confidence score of bounding boxes
    order = np.argsort(score)[::-1]
    keep = []
    # Iterate bounding boxes
    while order.size > 0:
        # The index of largest confidence score
        index = order[0]
        keep.append(index)

        # Compute ordinates of intersection-over-union(IOU)
        x1 = np.maximum(start_x[index], start_x[order[:-1]])
        x2 = np.minimum(end_x[index], end_x[order[:-1]])
        y1 = np.maximum(start_y[index], start_y[order[:-1]])
        y2 = np.minimum(end_y[index], end_y[order[:-1]])

        # Compute areas of intersection-over-union
        w = np.maximum(0.0, x2 - x1 + 1)
        h = np.maximum(0.0, y2 - y1 + 1)
        intersection = w * h

        # Compute the ratio between intersection and union
        ratio = intersection / (areas[index] + areas[order[1:]] - intersection)
        inds = np.where(ratio <= threshold)[0]
        order = order[inds + 1]

    picked_boxes = [bounding_boxes[i] for i in keep]

    if not score.shape:
        picked_score = [score]
    else:
        picked_score = [score[i] for i in keep]

    return picked_boxes, picked_score


def yolo_eval(yolo_outputs, anchors, num_classes, image_shape, score_threshold=.5, iou_threshold=.45):
    """
    Obtain predicted boxes axis and corresponding scores

    Args:
        yolo_outputs: output (3 feature maps) of YOLO V3 model, sizes are 1*13*13*18; 1*26*26*18; 1*52*52*18 seperately
        anchors: anchors pre-calculated
        num_classes: only 1 class here, which is "head"
        image_shape: original image input

    Returns:
        predicted boxes axis and corresponding scores
    """
    num_layers = len(yolo_outputs)
    anchor_mask = [[6, 7, 8], [3, 4, 5], [0, 1, 2]]
    yolo_output_0 = yolo_outputs[0]
    input_shape = [yolo_output_0.shape[1] * 32, yolo_output_0.shape[2] * 32]
    input_shape = np.array(input_shape)
    boxes = []
    box_scores = []

    for l in range(num_layers):
        _boxes, _box_scores = yolo_boxes_and_scores(yolo_outputs[l],
                                                    anchors[anchor_mask[l]], num_classes, input_shape, image_shape)
        boxes.append(_boxes)
        box_scores.append(_box_scores)

    boxes = np.concatenate(boxes, axis=0)
    box_scores = np.concatenate(box_scores, axis=0)

    mask = box_scores >= score_threshold

    class_boxes = boxes[np.nonzero(box_scores * mask)[0], :]
    class_box_scores = box_scores[np.nonzero(box_scores * mask)[0], :]

    class_box_scores = np.squeeze(class_box_scores)

    box, score = nms(class_boxes, class_box_scores, iou_threshold)

    return box, score


def get_box_img(image, box_axis):
    """
    Pack detected head area and corresponding location in the source image for WHENet

    Args:
        image: source image read from camera
        box_axis: location of boxes detected in YOLOV3
        box_score: scores of boxes detected in YOLOV3

    Returns:
        nparryList: head area 
        boxList: location in the source image
    """
    nparryList = []
    boxList = []

    for i in range(len(box_axis)):

        top, left, bottom, right = box_axis[i]
        top_modified = top - abs(top - bottom) / 10
        bottom_modified = bottom + abs(top - bottom) / 10
        left_modified = left - abs(left - right) / 5
        right_modified = right + abs(left - right) / 5

        top_modified = max(0, np.round(top_modified).astype('int32'))
        left_modified = max(0, np.round(left_modified).astype('int32'))
        bottom_modified = min(image.shape[0], np.round(
            bottom_modified).astype('int32'))
        right_modified = min(image.shape[1], np.round(
            right_modified).astype('int32'))

        boxList.append([left_modified, right_modified,
                       top_modified, bottom_modified])
        nparryList.append(
            image[top_modified:bottom_modified, left_modified:right_modified])

    return nparryList, boxList
Licensed under the Apache License, Version 2.0 (the "License"); You may not use this file except in compliance with the License. You may obtain a copy of the License at      http://www.apache.org/licenses/LICENSE-2.0  Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. sigmoid Convert final layer features to bounding box parameters. # 3 Get corrected boxes # y_min # x_min # y_max # x_max # Scale boxes back to original image shape. Process Conv layer output non maximum suppression for filter boxes # If no bounding boxes, return empty list # Bounding boxes # coordinates of bounding boxes # Confidence scores of bounding boxes # Compute areas of bounding boxes # Sort by confidence score of bounding boxes # Iterate bounding boxes # The index of largest confidence score # Compute ordinates of intersection-over-union(IOU) # Compute areas of intersection-over-union # Compute the ratio between intersection and union Obtain predicted boxes axis and corresponding scores      Args:         yolo_outputs: output (3 feature maps) of YOLO V3 model, sizes are 1*13*13*18; 1*26*26*18; 1*52*52*18 seperately         anchors: anchors pre-calculated         num_classes: only 1 class here, which is "head"         image_shape: original image input      Returns:         predicted boxes axis and corresponding scores Pack detected head area and corresponding location in the source image for WHENet      Args:         image: source image read from camera         box_axis: location of boxes detected in YOLOV3         box_score: scores of boxes detected in YOLOV3      Returns:         nparryList: head area          boxList: location in the source image