HuangMason320
ccd7cdd6b9
- Move test scripts to tests/ directory for better organization - Add improved YOLOv5 postprocessing with reference implementation - Update gitignore to exclude *.mflow files and include main.spec - Add debug capabilities and coordinate scaling improvements - Enhance multi-series support with proper validation - Add AGENTS.md documentation and example utilities 🤖 Generated with [Claude Code](https://claude.ai/code) Co-Authored-By: Claude <noreply@anthropic.com>
186 lines
7.6 KiB
Python
186 lines
7.6 KiB
Python
# ******************************************************************************
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# Copyright (c) 2021-2022. Kneron Inc. All rights reserved. *
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# ******************************************************************************
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import os
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import sys
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import argparse
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PWD = os.path.dirname(os.path.abspath(__file__))
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sys.path.insert(1, os.path.join(PWD, '..'))
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sys.path.insert(1, os.path.join(PWD, '../example/'))
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from example_utils.ExampleHelper import get_device_usb_speed_by_port_id
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from example_utils.ExamplePostProcess import post_process_yolo_v5
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import kp
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import cv2
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SCPU_FW_PATH = os.path.join(PWD, '../../res/firmware/KL520/fw_scpu.bin')
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NCPU_FW_PATH = os.path.join(PWD, '../../res/firmware/KL520/fw_ncpu.bin')
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MODEL_FILE_PATH = os.path.join(PWD,
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'../../res/models/KL520/yolov5-noupsample_w640h640_kn-model-zoo/kl520_20005_yolov5-noupsample_w640h640.nef')
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IMAGE_FILE_PATH = os.path.join(PWD, '../../res/images/people_talk_in_street_1500x1500.bmp')
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LOOP_TIME = 1
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if __name__ == '__main__':
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parser = argparse.ArgumentParser(description='KL520 Kneron Model Zoo Generic Image Inference Example - YoloV5.')
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parser.add_argument('-p',
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'--port_id',
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help='Using specified port ID for connecting device (Default: port ID of first scanned Kneron '
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'device)',
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default=0,
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type=int)
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parser.add_argument('-m',
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'--model',
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help='Model file path (.nef) (Default: {})'.format(MODEL_FILE_PATH),
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default=MODEL_FILE_PATH,
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type=str)
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parser.add_argument('-i',
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'--img',
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help='Image file path (Default: {})'.format(IMAGE_FILE_PATH),
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default=IMAGE_FILE_PATH,
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type=str)
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args = parser.parse_args()
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usb_port_id = args.port_id
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MODEL_FILE_PATH = args.model
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IMAGE_FILE_PATH = args.img
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"""
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check device USB speed (Recommend run KL520 at high speed)
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"""
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try:
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if kp.UsbSpeed.KP_USB_SPEED_HIGH != get_device_usb_speed_by_port_id(usb_port_id=usb_port_id):
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print('\033[91m' + '[Warning] Device is not run at high speed.' + '\033[0m')
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except Exception as exception:
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print('Error: check device USB speed fail, port ID = \'{}\', error msg: [{}]'.format(usb_port_id,
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str(exception)))
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exit(0)
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"""
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connect the device
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"""
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try:
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print('[Connect Device]')
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device_group = kp.core.connect_devices(usb_port_ids=[usb_port_id])
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print(' - Success')
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except kp.ApiKPException as exception:
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print('Error: connect device fail, port ID = \'{}\', error msg: [{}]'.format(usb_port_id,
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str(exception)))
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exit(0)
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"""
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setting timeout of the usb communication with the device
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"""
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print('[Set Device Timeout]')
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kp.core.set_timeout(device_group=device_group, milliseconds=5000)
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print(' - Success')
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"""
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upload firmware to device
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"""
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try:
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print('[Upload Firmware]')
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kp.core.load_firmware_from_file(device_group=device_group,
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scpu_fw_path=SCPU_FW_PATH,
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ncpu_fw_path=NCPU_FW_PATH)
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print(' - Success')
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except kp.ApiKPException as exception:
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print('Error: upload firmware failed, error = \'{}\''.format(str(exception)))
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exit(0)
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"""
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upload model to device
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"""
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try:
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print('[Upload Model]')
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model_nef_descriptor = kp.core.load_model_from_file(device_group=device_group,
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file_path=MODEL_FILE_PATH)
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print(' - Success')
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except kp.ApiKPException as exception:
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print('Error: upload model failed, error = \'{}\''.format(str(exception)))
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exit(0)
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"""
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prepare the image
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"""
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print('[Read Image]')
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img = cv2.imread(filename=IMAGE_FILE_PATH)
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img_bgr565 = cv2.cvtColor(src=img, code=cv2.COLOR_BGR2BGR565)
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print(' - Success')
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"""
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prepare generic image inference input descriptor
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"""
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generic_inference_input_descriptor = kp.GenericImageInferenceDescriptor(
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model_id=model_nef_descriptor.models[0].id,
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inference_number=0,
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input_node_image_list=[
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kp.GenericInputNodeImage(
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image=img_bgr565,
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image_format=kp.ImageFormat.KP_IMAGE_FORMAT_RGB565,
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resize_mode=kp.ResizeMode.KP_RESIZE_ENABLE,
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padding_mode=kp.PaddingMode.KP_PADDING_CORNER,
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normalize_mode=kp.NormalizeMode.KP_NORMALIZE_KNERON
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)
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]
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)
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"""
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starting inference work
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"""
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print('[Starting Inference Work]')
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print(' - Starting inference loop {} times'.format(LOOP_TIME))
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print(' - ', end='')
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for i in range(LOOP_TIME):
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try:
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kp.inference.generic_image_inference_send(device_group=device_group,
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generic_inference_input_descriptor=generic_inference_input_descriptor)
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generic_raw_result = kp.inference.generic_image_inference_receive(device_group=device_group)
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except kp.ApiKPException as exception:
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print(' - Error: inference failed, error = {}'.format(exception))
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exit(0)
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print('.', end='', flush=True)
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print()
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"""
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retrieve inference node output
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"""
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print('[Retrieve Inference Node Output ]')
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inf_node_output_list = []
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for node_idx in range(generic_raw_result.header.num_output_node):
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inference_float_node_output = kp.inference.generic_inference_retrieve_float_node(node_idx=node_idx,
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generic_raw_result=generic_raw_result,
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channels_ordering=kp.ChannelOrdering.KP_CHANNEL_ORDERING_CHW
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)
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inf_node_output_list.append(inference_float_node_output)
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print(' - Success')
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yolo_result = post_process_yolo_v5(inference_float_node_output_list=inf_node_output_list,
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hardware_preproc_info=generic_raw_result.header.hw_pre_proc_info_list[0],
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thresh_value=0.2)
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print('[Result]')
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print(' - Number of boxes detected')
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print(' - ' + str(len(yolo_result.box_list)))
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output_img_name = 'output_{}'.format(os.path.basename(IMAGE_FILE_PATH))
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print(' - Output bounding boxes on \'{}\''.format(output_img_name))
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print(" - Bounding boxes info (xmin,ymin,xmax,ymax):")
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for yolo_box_result in yolo_result.box_list:
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b = 100 + (25 * yolo_box_result.class_num) % 156
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g = 100 + (80 + 40 * yolo_box_result.class_num) % 156
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r = 100 + (120 + 60 * yolo_box_result.class_num) % 156
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color = (b, g, r)
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cv2.rectangle(img=img,
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pt1=(int(yolo_box_result.x1), int(yolo_box_result.y1)),
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pt2=(int(yolo_box_result.x2), int(yolo_box_result.y2)),
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color=color,
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thickness=2)
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print("(" + str(yolo_box_result.x1) + "," + str(yolo_box_result.y1) + ',' + str(yolo_box_result.x2) + ',' + str(
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yolo_box_result.y2) + ")")
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cv2.imwrite(os.path.join(PWD, './{}'.format(output_img_name)), img=img)
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