Export parameters for model from keras yolov8

Hi erik ,
Yes, I have checked them, and after some experiments, I figured out the desired image preproccesing
- value range 0 - 255, mean should be 0 and scale should be 1?
- brg order, should I add a flag in conversion or just read data in brg format from the camera?
- my model input shape is 1,3,640,640
- and it requires float32 tensor type for input, so --ip should be FP32?
Thanks, Oleksandr

erik ,
I changed my layers to float16, will it work if I set --ip FP16?
When I use getFrame, I get data as it passed to nn?
I have tried inRgb.getFrame(), and put it to onnx model, but it outputs nothing…
I can share code and output to provide example

Hi erik,
Sorry for the delay
Can you please provide an example how to get frame from passthrough?
Here are MRE, python script, blob and onnx model, and the image of cubes(model should recognize them) )

#!/usr/bin/env python3
from pathlib import Path
import sys
import cv2
import depthai as dai
import numpy as np
import time
import tensorflow as tf
import keras_cv
import keras
import onnxruntime
nnPath = str((Path('./models/YOLO KERAS/model_fp16_full.blob')).resolve().absolute())
nnPath_onnx = str((Path('./models/YOLO KERAS/model_fp16_full.onnx')).resolve().absolute())
session= onnxruntime.InferenceSession(nnPath_onnx)
input_name=session.get_inputs()[0].name
output_name0=session.get_outputs()[0].name
output_name1=session.get_outputs()[1].name
image_path = "all.jpg"
BOX_REGRESSION_CHANNELS=64
def decode_regression_to_boxes(preds):
    """Decodes the results of the YOLOV8Detector forward-pass into boxes.
    Returns left / top / right / bottom predictions with respect to anchor
    points.
    Each coordinate is encoded with 16 predicted values. Those predictions are
    softmaxed and multiplied by [0..15] to make predictions. The resulting
    predictions are relative to the stride of an anchor box (and correspondingly
    relative to the scale of the feature map from which the predictions came).
    """
    preds_bbox = keras.layers.Reshape((-1, 4, BOX_REGRESSION_CHANNELS // 4))(
        preds
    )
    preds_bbox = tf.nn.softmax(preds_bbox, axis=-1) * tf.range(
        BOX_REGRESSION_CHANNELS // 4, dtype="float32"
    )
    return tf.reduce_sum(preds_bbox, axis=-1)
def dist2bbox(distance, anchor_points):
    """Decodes distance predictions into xyxy boxes.
    Input left / top / right / bottom predictions are transformed into xyxy box
    predictions based on anchor points.
    The resulting xyxy predictions must be scaled by the stride of their
    corresponding anchor points to yield an absolute xyxy box.
    """
    left_top, right_bottom = tf.split(distance, 2, axis=-1)
    x1y1 = anchor_points - left_top
    x2y2 = anchor_points + right_bottom
    return tf.concat((x1y1, x2y2), axis=-1)  # xyxy bbox
def get_anchors(
    image_shape,
    strides=[8, 16, 32],
    base_anchors=[0.5, 0.5],
):
    """Gets anchor points for YOLOV8.
    YOLOV8 uses anchor points representing the center of proposed boxes, and
    matches ground truth boxes to anchors based on center points.
    Args:
        image_shape: tuple or list of two integers representing the height and
            width of input images, respectively.
        strides: tuple of list of integers, the size of the strides across the
            image size that should be used to create anchors.
        base_anchors: tuple or list of two integers representing the offset from
            (0,0) to start creating the center of anchor boxes, relative to the
            stride. For example, using the default (0.5, 0.5) creates the first
            anchor box for each stride such that its center is half of a stride
            from the edge of the image.
    Returns:
        A tuple of anchor centerpoints and anchor strides. Multiplying the
        two together will yield the centerpoints in absolute x,y format.
    """
    base_anchors = tf.constant(base_anchors, dtype="float32")
    all_anchors = []
    all_strides = []
    for stride in strides:
        hh_centers = tf.range(0, image_shape[0], stride)
        ww_centers = tf.range(0, image_shape[1], stride)
        ww_grid, hh_grid = tf.meshgrid(ww_centers, hh_centers)
        grid = tf.cast(
            tf.reshape(tf.stack([hh_grid, ww_grid], 2), [-1, 1, 2]),
            "float32",
        )
        anchors = (
            tf.expand_dims(
                base_anchors * tf.constant([stride, stride], "float32"), 0
            )
            + grid
        )
        anchors = tf.reshape(anchors, [-1, 2])
        all_anchors.append(anchors)
        all_strides.append(tf.repeat(stride, anchors.shape[0]))
    all_anchors = tf.cast(tf.concat(all_anchors, axis=0), "float32")
    all_strides = tf.cast(tf.concat(all_strides, axis=0), "float32")
    all_anchors = all_anchors / all_strides[:, None]
    # Swap the x and y coordinates of the anchors.
    all_anchors = tf.concat(
        [all_anchors[:, 1, None], all_anchors[:, 0, None]], axis=-1
    )
    return all_anchors, all_strides
def decode_predictions(
        boxes_,
        scores_,
        images,
    ):
        boxes = boxes_
        scores = scores_
        boxes = decode_regression_to_boxes(boxes)
        anchor_points, stride_tensor = get_anchors(image_shape=(640,640,3))
        stride_tensor = tf.expand_dims(stride_tensor, axis=-1)
        box_preds = dist2bbox(boxes, anchor_points) * stride_tensor
        prediction_decoder = keras_cv.layers.MultiClassNonMaxSuppression(
                    bounding_box_format="xyxy",
                    from_logits=False,
                    iou_threshold = 0.5, confidence_threshold = 0.5
                )
      
        return prediction_decoder(box_preds, scores)
# Get argument first
labelMap = [
    "green",         "pink",    "orange"
]
# Create pipeline
pipeline = dai.Pipeline()
camRgb = pipeline.create(dai.node.ColorCamera)
camRgb.setPreviewSize(640, 640)
camRgb.setResolution(dai.ColorCameraProperties.SensorResolution.THE_1080_P)
camRgb.setInterleaved(False)
camRgb.setColorOrder(dai.ColorCameraProperties.ColorOrder.BGR)
camRgb.setFp16(True) # Model requires FP16 input
# NN that detects faces in the image
nn = pipeline.create(dai.node.NeuralNetwork)
nn.setBlobPath(nnPath)
nn.setNumInferenceThreads(2)
camRgb.preview.link(nn.input)
# Send bouding box from the NN to the host via XLink
nn_xout = pipeline.create(dai.node.XLinkOut)
nn_xout.setStreamName("nn")
nn.out.link(nn_xout.input)
# Send rgb frames to the host
rgb_xout = pipeline.create(dai.node.XLinkOut)
rgb_xout.setStreamName("rgb")
nn.passthrough.link(rgb_xout.input)
# Connect to device and start pipeline
with dai.Device(pipeline) as device:
    # Output queues will be used to get the rgb frames and nn data from the outputs defined above
    qRgb = device.getOutputQueue(name="rgb", maxSize=4, blocking=False)
    qDet = device.getOutputQueue(name="nn", maxSize=4, blocking=False)
    detections = []
    while True:
        inRgb = qRgb.get()
        frame = np.array(inRgb.getData()).view(np.float16).reshape((3,640,640)).transpose(1, 2, 0).astype(np.uint8).copy()
        in_nn = qDet.tryGet()
        if in_nn is not None:
            # [print(f"Layer name: {l.name}, Type: {l.dataType}, Dimensions: {l.dims}") for l in inDet.getAllLayers()]
            # Extract the output shape: (batch_size, channels, num_predictions)
            boxes = np.array(in_nn.getLayerFp16('box')).reshape(1, 8400, 64).astype(dtype=np.float32)
           classes = np.array(in_nn.getLayerFp16('class')).reshape(1, 8400, 3).astype(dtype=np.float32)
            detections=[]
            # print(classes)
            result=decode_predictions(boxes, classes, np.expand_dims(np.array(frame),axis=0))
            # print(result)
            result_boxes=result["boxes"]
            num_of_dects=result["num_detections"]
            print("num_of_dects")
            print(num_of_dects)
            if result_boxes[0][0][0] !=-1.0:
                detection = {
                    "label": 1,
                    "confidence": 0.1,
                    "box": result_boxes[0][0]}
                detections.append(detection)
                        # Load and preprocess the image
            image = cv2.imread(image_path)
            image = cv2.resize(image, (640, 640))  # Resize to model's input size
            image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
            image = inRgb.getCvFrame()
            print(image.dtype)
            image = np.expand_dims(image, axis=0)
            image = np.reshape(image, (1,3,640,640))
            res = session.run(
               output_names=[output_name0, output_name1],
                input_feed={input_name: image}
            )
            result=decode_predictions(res[0], res[1], np.expand_dims(np.array(frame),axis=0))
            # print(result)
            result_boxes=result["boxes"]
            num_of_dects=result["num_detections"]
            print("num_of_dects onnx")
            print(num_of_dects)
      
        cv2.imshow("rgb", frame)
        if cv2.waitKey(1) == ord('q'):
            break

- blob model

- onnx model
Converted with this params - --data_type=FP16 --mean_values=[0,0,0] --scale_values=[1,1,1] --layout=NHWC --input_shape=[1,3,640,640] --ip FP16 shaves - 6 version - 2022.1

image

jakaskerl
cap is camera pipeline?

jakaskerl, Sure
here is image and red rectangle is what it thinks where is the cube

And if try to load this image by opencv and then inference on it by onnx, I will get precise results

jakaskerl, sure.
Here it is:
#!/usr/bin/env python3

from pathlib import Path

import sys

import cv2

import depthai as dai

import numpy as np

import time

import tensorflow as tf

import keras_cv

import keras

import onnxruntime

nnPath = str((Path('./models/YOLO KERAS/model_fp16_full_ov.blob')).resolve().absolute())

nnPath_onnx = str((Path('./models/YOLO KERAS/model_fp16_full.onnx')).resolve().absolute())

session= onnxruntime.InferenceSession(nnPath_onnx)

input_name=session.get_inputs()[0].name

output_name0=session.get_outputs()[0].name

output_name1=session.get_outputs()[1].name

image_path = "image.jpg"

BOX_REGRESSION_CHANNELS=64

def decode_regression_to_boxes(preds):

"""Decodes the results of the YOLOV8Detector forward-pass into boxes.

Returns left / top / right / bottom predictions with respect to anchor

points.

Each coordinate is encoded with 16 predicted values. Those predictions are

softmaxed and multiplied by [0..15] to make predictions. The resulting

predictions are relative to the stride of an anchor box (and correspondingly

relative to the scale of the feature map from which the predictions came).

"""

preds_bbox = keras.layers.Reshape((-1, 4, BOX_REGRESSION_CHANNELS // 4))(

preds

)

preds_bbox = tf.nn.softmax(preds_bbox, axis=-1) * tf.range(

BOX_REGRESSION_CHANNELS // 4, dtype="float32"

)

return tf.reduce_sum(preds_bbox, axis=-1)

def dist2bbox(distance, anchor_points):

"""Decodes distance predictions into xyxy boxes.

Input left / top / right / bottom predictions are transformed into xyxy box

predictions based on anchor points.

The resulting xyxy predictions must be scaled by the stride of their

corresponding anchor points to yield an absolute xyxy box.

"""

left_top, right_bottom = tf.split(distance, 2, axis=-1)

x1y1 = anchor_points - left_top

x2y2 = anchor_points + right_bottom

return tf.concat((x1y1, x2y2), axis=-1) # xyxy bbox

def get_anchors(

image_shape,

strides=[8, 16, 32],

base_anchors=[0.5, 0.5],

):

"""Gets anchor points for YOLOV8.

YOLOV8 uses anchor points representing the center of proposed boxes, and

matches ground truth boxes to anchors based on center points.

Args:

image_shape: tuple or list of two integers representing the height and

width of input images, respectively.

strides: tuple of list of integers, the size of the strides across the

image size that should be used to create anchors.

base_anchors: tuple or list of two integers representing the offset from

(0,0) to start creating the center of anchor boxes, relative to the

stride. For example, using the default (0.5, 0.5) creates the first

anchor box for each stride such that its center is half of a stride

from the edge of the image.

Returns:

A tuple of anchor centerpoints and anchor strides. Multiplying the

two together will yield the centerpoints in absolute x,y format.

"""

base_anchors = tf.constant(base_anchors, dtype="float32")

all_anchors = []

all_strides = []

for stride in strides:

hh_centers = tf.range(0, image_shape[0], stride)

ww_centers = tf.range(0, image_shape[1], stride)

ww_grid, hh_grid = tf.meshgrid(ww_centers, hh_centers)

grid = tf.cast(

tf.reshape(tf.stack([hh_grid, ww_grid], 2), [-1, 1, 2]),

"float32",

)

anchors = (

tf.expand_dims(

base_anchors * tf.constant([stride, stride], "float32"), 0

)

+ grid

)

anchors = tf.reshape(anchors, [-1, 2])

all_anchors.append(anchors)

all_strides.append(tf.repeat(stride, anchors.shape[0]))

all_anchors = tf.cast(tf.concat(all_anchors, axis=0), "float32")

all_strides = tf.cast(tf.concat(all_strides, axis=0), "float32")

all_anchors = all_anchors / all_strides[:, None]

# Swap the x and y coordinates of the anchors.

all_anchors = tf.concat(

[all_anchors[:, 1, None], all_anchors[:, 0, None]], axis=-1

)

return all_anchors, all_strides

def decode_predictions(

boxes_,

scores_,

images,

):

boxes = boxes_

scores = scores_

boxes = decode_regression_to_boxes(boxes)

anchor_points, stride_tensor = get_anchors(image_shape=(640,640,3))

stride_tensor = tf.expand_dims(stride_tensor, axis=-1)

box_preds = dist2bbox(boxes, anchor_points) * stride_tensor

prediction_decoder = keras_cv.layers.MultiClassNonMaxSuppression(

bounding_box_format="xyxy",

from_logits=False,

iou_threshold=0.5,

confidence_threshold=0.5

)

return prediction_decoder(box_preds, scores)

# Get argument first

labelMap = [

"green", "pink", "orange"

]

# Create pipeline

pipeline = dai.Pipeline()

camRgb = pipeline.create(dai.node.ColorCamera)

camRgb.setPreviewSize(640, 640)

camRgb.setResolution(dai.ColorCameraProperties.SensorResolution.THE_1080_P)

camRgb.setInterleaved(False)

camRgb.setColorOrder(dai.ColorCameraProperties.ColorOrder.BGR)

camRgb.setFp16(True) # Model requires FP16 input

# NN that detects faces in the image

nn = pipeline.create(dai.node.NeuralNetwork)

nn.setBlobPath(nnPath)

nn.setNumInferenceThreads(2)

camRgb.preview.link(nn.input)

# Send bouding box from the NN to the host via XLink

nn_xout = pipeline.create(dai.node.XLinkOut)

nn_xout.setStreamName("nn")

nn.out.link(nn_xout.input)

# Send rgb frames to the host

rgb_xout = pipeline.create(dai.node.XLinkOut)

rgb_xout.setStreamName("rgb")

nn.passthrough.link(rgb_xout.input)

# Connect to device and start pipeline

with dai.Device(pipeline) as device:

# Output queues will be used to get the rgb frames and nn data from the outputs defined above

qRgb = device.getOutputQueue(name="rgb", maxSize=4, blocking=False)

qDet = device.getOutputQueue(name="nn", maxSize=4, blocking=False)

detections = []

while True:

inRgb = qRgb.get()

frame = np.array(inRgb.getData()).view(np.float16).reshape((3,640,640)).transpose(1, 2, 0).astype(np.uint8).copy()

in_nn = qDet.tryGet()

if in_nn is not None:

# [print(f"Layer name: {l.name}, Type: {l.dataType}, Dimensions: {l.dims}") for l in inDet.getAllLayers()]

# Extract the output shape: (batch_size, channels, num_predictions)

boxes = np.array(in_nn.getLayerFp16('box')).reshape(1, 8400, 64).astype(dtype=np.float32)

classes = np.array(in_nn.getLayerFp16('class')).reshape(1, 8400, 3).astype(dtype=np.float32)

detections=[]

# print(classes)

result=decode_predictions(boxes, classes, frame)

# print(result)

result_boxes=result["boxes"]

num_of_dects=result["num_detections"]

if(num_of_dects[0] >0):

print("num_of_dects")

print(num_of_dects)

for bbox_data in result_boxes[0]:

bbox = [np.int64(bbox_data[0]),np.int64(bbox_data[1]),np.int64(bbox_data[2]),np.int64(bbox_data[3])]

frame = cv2.rectangle(frame, (bbox[0], bbox[1]), (bbox[2], bbox[3]), (0,255,0), 2)

image = inRgb.getCvFrame()

image = np.expand_dims(image, axis=0)

image = np.reshape(image, (1,3,640,640))

res = session.run(

output_names=[output_name0, output_name1],

input_feed={input_name: image}

)

result=decode_predictions(res[0], res[1], np.expand_dims(np.array(frame),axis=0))

# print(result)

result_boxes=result["boxes"]

num_of_dects=result["num_detections"]

if(num_of_dects[0] >0):

print("num_of_dects onnx")

print(num_of_dects)

for bbox_data in result_boxes[0]:

bbox = [np.int64(bbox_data[0]),np.int64(bbox_data[1]),np.int64(bbox_data[2]),np.int64(bbox_data[3])]

frame = cv2.rectangle(frame, (bbox[0], bbox[1]), (bbox[2], bbox[3]), (0,0,255), 2)

cv2.imshow("rgb", frame)

if cv2.waitKey(1) == ord('q'):

break

if cv2.waitKey(1) == ord("s"):

cv2.imwrite("img.png", frame)

print(frame)

reshaped_frame = frame.reshape(-1, frame.shape[2]) # Reshape to (height*width, channels)

np.savetxt("img.txt", reshaped_frame, fmt="%.6f")

break

and google drive with onnx and blob models:
https://drive.google.com/drive/folders/1cXhwfOF7TG81ZSIZ4NJjKl3dlUGRLctz?usp=drive_link

And I set preview frame same size as my model has in input layer - 640x640