tf_post_processing.py

# -*- coding: utf-8 -*-
"""

Created on Mon Sep  4 16:03:42 2023



@author: SABARI

"""
import time
import tensorflow as tf
import numpy as np
#from lsnms import nms, wbc

def box_iou(box1, box2, eps=1e-7):
    """

    Calculate intersection-over-union (IoU) of boxes.

    Both sets of boxes are expected to be in (x1, y1, x2, y2) format.

    Args:

    box1 (tf.Tensor): A tensor of shape (N, 4) representing N bounding boxes.

    box2 (tf.Tensor): A tensor of shape (M, 4) representing M bounding boxes.

    eps (float, optional): A small value to avoid division by zero. Defaults to 1e-7.

    Returns:

    (tf.Tensor): An NxM tensor containing the pairwise IoU values for every element in box1 and box2.

    """
    
    a1, a2 = tf.split(box1, 2, axis=1)
    b1, b2 = tf.split(box2, 2, axis=1)
    
    inter = tf.reduce_prod(tf.maximum(tf.minimum(a2, b2) - tf.maximum(a1, b1), 0), axis=1)
    
    return inter / (tf.reduce_prod(a2 - a1, axis=1) + tf.reduce_prod(b2 - b1, axis=1) - inter + eps)


def xywh2xyxy(x):
    """

    Convert bounding box coordinates from (x, y, width, height) format to (x1, y1, x2, y2) format where (x1, y1) is the

    top-left corner and (x2, y2) is the bottom-right corner.

    Args:

    x (tf.Tensor): The input bounding box coordinates in (x, y, width, height) format.

    Returns:

    y (tf.Tensor): The bounding box coordinates in (x1, y1, x2, y2) format.

    """
    # Assuming x is a NumPy array
    y = np.copy(x)
    y[..., 0] = x[..., 0] - x[..., 2] / 2  # top left x
    y[..., 1] = x[..., 1] - x[..., 3] / 2  # top left y
    y[..., 2] = x[..., 0] + x[..., 2] / 2  # bottom right x
    y[..., 3] = x[..., 1] + x[..., 3] / 2  # bottom right y
    return y


def non_max_suppression(prediction, conf_thres=0.25, iou_thres=0.45, agnostic=False,

                        multi_label=False, max_det=300, nc=0,  # number of classes (optional)

                        max_time_img=0.05,

                        max_nms=100,

                        max_wh=7680):
    """

    Perform non-maximum suppression (NMS) on a set of boxes, with support for masks and multiple labels per box.

    Arguments:

    prediction (tf.Tensor): A tensor of shape (batch_size, num_classes + 4 + num_masks, num_boxes)

    containing the predicted boxes, classes, and masks. The tensor should be in the format

    output by a model, such as YOLO.

    conf_thres (float): The confidence threshold below which boxes will be filtered out.

    Valid values are between 0.0 and 1.0.

    iou_thres (float): The IoU threshold below which boxes will be filtered out during NMS.

    Valid values are between 0.0 and 1.0.

    agnostic (bool): If True, the model is agnostic to the number of classes, and all

    classes will be considered as one.

    multi_label (bool): If True, each box may have multiple labels.

    max_det (int): The maximum number of boxes to keep after NMS.

    nc (int): (optional) The number of classes output by the model. Any indices after this will be considered masks.

    max_time_img (float): The maximum time (seconds) for processing one image.

    max_nms (int): The maximum number of boxes into tf.image.combined_non_max_suppression().

    max_wh (int): The maximum box width and height in pixels

    Returns:

    (List[tf.Tensor]): A list of length batch_size, where each element is a tensor of

    shape (num_boxes, 6 + num_masks) containing the kept boxes, with columns

    (x1, y1, x2, y2, confidence, class, mask1, mask2, ...).

    """

    # Checks
    assert 0 <= conf_thres <= 1, f'Invalid Confidence threshold {conf_thres}, valid values are between 0.0 and 1.0'
    assert 0 <= iou_thres <= 1, f'Invalid IoU {iou_thres}, valid values are between 0.0 and 1.0'
    if isinstance(prediction, (list, tuple)):  # YOLOv8 model in validation model, output = (inference_out, loss_out)
        prediction = prediction[0]  # select only inference output

    bs = np.shape(prediction)[0]  # batch size
    nc = nc or (np.shape(prediction)[1] - 4)  # number of classes
    nm = np.shape(prediction)[1] - nc - 4
    mi = 4 + nc  # mask start index
    #xc = tf.math.reduce_any(prediction[:, 4:mi] > conf_thres, axis=1)  # candidates
    xc = np.amax(prediction[:, 4:mi], axis=1) > conf_thres

    # Settings
    # min_wh = 2  # (pixels) minimum box width and height
    time_limit = 0.5 + max_time_img * tf.cast(bs, tf.float32)  # seconds to quit after
    multi_label &= nc > 1  # multiple labels per box (adds 0.5ms/img)

    t = time.time()
    output = [np.zeros((0, 6 + nm))] * bs
    for xi, x in enumerate(prediction):  # image index, image inference
        # Apply constraints
        # x = tf.where(tf.math.logical_or(x[:, 2:4] < min_wh, x[:, 2:4] > max_wh), tf.constant(0, dtype=tf.float32), x)  # width-height
        #x = tf.boolean_mask(x, xc[xi])
        #x = x.transpose(0, -1)[xc[xi]]  # confidence
        # Assuming x, xc, and xi are NumPy arrays
        x = np.transpose(x)

        #x = x.transpose()[:, xc[xi]]
        x = x[xc[xi]]

        # If none remain process next image
        if np.shape(x)[0] == 0:
            continue

        # Detections matrix nx6 (xyxy, conf, cls)
        #box, cls, mask = tf.split(x, [4, nc, nm], axis=1)
        
        # Assuming x is a NumPy array
        box = x[:, :4]
        cls = x[:, 4:4 + nc]
        mask = x[:, 4 + nc:]
        box = xywh2xyxy(box)  # center_x, center_y, width, height) to (x1, y1, x2, y2)

        # Assuming cls is a NumPy array
        if multi_label:
            i, j = np.where(cls > conf_thres)
            x = np.concatenate([box[i], np.expand_dims(cls[i, j], axis=-1), np.expand_dims(j, axis=-1).astype(np.float32), mask[i]], axis=1)
        else:
            conf = np.max(cls, axis=1)
            j = np.argmax(cls, axis=1)
            keep = np.where(conf > conf_thres)[0]
            x = np.concatenate([box[keep], np.expand_dims(conf[keep], axis=-1), np.expand_dims(j[keep], axis=-1).astype(np.float32), mask[keep]], axis=1)

        # Check shape
        n = np.shape(x)[0]  # number of boxes
        if n == 0:  # no boxes
            continue
        #x = x[tf.argsort(x[:, 4], direction='DESCENDING')[:max_nms]]  # sort by confidence and remove excess boxes
        sorted_indices = np.argsort(x[:, 4])[::-1]  # Sort indices in descending order of confidence
        x = x[sorted_indices[:max_nms]]  # Keep the top max_nms boxes
        
        # Batched NMS
        c = x[:, 5:6] * (0.0 if agnostic else tf.cast(max_wh, tf.float32))  # classes
        boxes, scores = x[:, :4] + c, x[:, 4]  # boxes (offset by class), scores
        i = tf.image.non_max_suppression(boxes, scores, max_nms, iou_threshold=iou_thres)  # NMS
        i = i.numpy()
        i = i[:max_det]  # limit detections

        output[xi] =  x[i,:]
        
        if (time.time() - t) > time_limit:
            break  # time limit exceeded

    return output

import numpy as np

def optimized_object_detection(prediction, conf_thres=0.25, iou_thres=0.45, agnostic=False,

                                multi_label=False, max_det=300, nc=0, max_time_img=0.05,

                                max_nms=100, max_wh=7680):
    
    assert 0 <= conf_thres <= 1, f'Invalid Confidence threshold {conf_thres}, valid values are between 0.0 and 1.0'
    assert 0 <= iou_thres <= 1, f'Invalid IoU {iou_thres}, valid values are between 0.0 and 1.0'
    
    if isinstance(prediction, (list, tuple)):
        prediction = prediction[0]
    
    bs, _, _ = prediction.shape  # Get batch size and dimensions
    
    if nc == 0:
        nc = prediction.shape[1] - 4
    
    nm = prediction.shape[1] - nc - 4
    mi = 4 + nc
    
    xc = np.amax(prediction[:, 4:mi], axis=1) > conf_thres
    
    time_limit = 0.5 + max_time_img * bs
    
    multi_label &= nc > 1
    
    t = time.time()
    output = [np.zeros((0, 6 + nm))] * bs
    
    for xi, x in enumerate(prediction):
        x = np.transpose(x)
        x = x[xc[xi]]
        
        if np.shape(x)[0] == 0:
            continue

        box = x[:, :4]
        cls = x[:, 4:4 + nc]
        mask = x[:, 4 + nc:]
        box = xywh2xyxy(box)

        if multi_label:
            i, j = np.where(cls > conf_thres)
            x = np.concatenate([box[i], np.expand_dims(cls[i, j], axis=-1), np.expand_dims(j, axis=-1).astype(np.float32), mask[i]], axis=1)
        else:
            conf = np.max(cls, axis=1)
            j = np.argmax(cls, axis=1)
            keep = np.where(conf > conf_thres)[0]
            x = np.concatenate([box[keep], np.expand_dims(conf[keep], axis=-1), np.expand_dims(j[keep], axis=-1).astype(np.float32), mask[keep]], axis=1)

        n = np.shape(x)[0]
        if n == 0:
            continue
        
        sorted_indices = np.argsort(x[:, 4])[::-1]
        x = x[sorted_indices[:max_nms]]
        
        c = x[:, 5:6] * (0.0 if agnostic else max_wh)
        boxes, scores = x[:, :4] + c, x[:, 4]
        i = tf.image.non_max_suppression(boxes, scores, max_nms, iou_threshold=iou_thres)
        
        #keep = nms(boxes, scores, iou_threshold=iou_thres)

        i = i.numpy()
        i = i[:max_det]
        
        output[xi] = x[keep,:]
        
        if (time.time() - t) > time_limit:
            break
    
    return output


#output_numpy = np.load(r"D:\object_face_person_detection\yolov8_tf_results\gustavo-alves-YOXSC4zRcxw-unsplash.npy")

#detections = non_max_suppression(output_numpy, conf_thres=0.4, iou_thres=0.4)[0]

#print(detections)