树莓派AI套件：如何将混乱的数据变成有序的魔法

!pip install boto3

from __future__ import print_functionimport requestsimport sysimport ioimport jsonimport shutilimport sysimport datetimeimport subprocessimport sysimport osimport mathimport base64from time import gmtime, strftimeimport random, stringimport timeimport psutilimport base64import uuidimport socketimport osfrom pymilvus import connectionsfrom pymilvus import utilityfrom pymilvus import FieldSchema, CollectionSchema, DataType, Collectionimport torchfrom torchvision import transformsfrom PIL import Imageimport timmfrom sklearn.preprocessing import normalizefrom timm.data import resolve_data_configfrom timm.data.transforms_factory import create_transformfrom pymilvus import MilvusClientimport osfrom IPython.display import display

from __future__ import print_functionimport requestsimport sysimport ioimport jsonimport shutilimport sysimport datetimeimport subprocessimport sysimport osimport mathimport base64from time import gmtime, strftimeimport random, stringimport timeimport psutilimport base64import uuidimport socketimport osfrom pymilvus import connectionsfrom pymilvus import utilityfrom pymilvus import FieldSchema, CollectionSchema, DataType, Collectionimport torchfrom torchvision import transformsfrom PIL import Imageimport timmfrom sklearn.preprocessing import normalizefrom timm.data import resolve_data_configfrom timm.data.transforms_factory import create_transformfrom pymilvus import MilvusClientimport osfrom IPython.display import display

# -----------------------------------------------------------------------------
class FeatureExtractor:def __init__(self, modelname):# Load the pre-trained modelself.model = timm.create_model(modelname, pretrained=True, num_classes=0, global_pool="avg")self.model.eval()
# Get the input size required by the modelself.input_size = self.model.default_cfg["input_size"]
config = resolve_data_config({}, model=modelname)# Get the preprocessing function provided by TIMM for the modelself.preprocess = create_transform(**config)
def __call__(self, imagepath):# Preprocess the input imageinput_image = Image.open(imagepath).convert("RGB")# Convert to RGB if neededinput_image = self.preprocess(input_image)
# Convert the image to a PyTorch tensor and add a batch dimensioninput_tensor = input_image.unsqueeze(0)
# Perform inferencewith torch.no_grad():output = self.model(input_tensor)
# Extract the feature vectorfeature_vector = output.squeeze().numpy()
return normalize(feature_vector.reshape(1, -1), norm="l2").flatten()

extractor = FeatureExtractor("resnet34")
# -----------------------------------------------------------------------------# Constants - should be environment variables# -----------------------------------------------------------------------------DIMENSION = 512 MILVUS_URL = "http://192.168.1.163:19530" COLLECTION_NAME = "pidetections"BUCKET_NAME = "images"DOWNLOAD_DIR = "/Users/timothyspann/Downloads/code/images/"AWS_RESOURCE = "s3"S3_ENDPOINT_URL = "http://192.168.1.163:9000"AWS_ACCESS_KEY = "minioadmin" AWS_SECRET_ACCESS_KEY = "minioadmin"S3_SIGNATURE_VERSION = "s3v4"AWS_REGION_NAME = "us-east-1"S3_ERROR_MESSAGE = "Download failed"# -----------------------------------------------------------------------------

# -----------------------------------------------------------------------------# Connect to Milvus
# Local Docker Servermilvus_client = MilvusClient( uri=MILVUS_URL)# -----------------------------------------------------------------------------

import osimport boto3from botocore.client import Config
# -----------------------------------------------------------------------------# Access Images on S3 Compatible Store - AWS S3 or Minio or ...# -----------------------------------------------------------------------------s3 = boto3.resource(AWS_RESOURCE,endpoint_url=S3_ENDPOINT_URL,aws_access_key_id=AWS_ACCESS_KEY,aws_secret_access_key=AWS_SECRET_ACCESS_KEY,config=Config(signature_version=S3_SIGNATURE_VERSION),region_name=AWS_REGION_NAME)
bucket = s3.Bucket(BUCKET_NAME)
# -----------------------------------------------------------------------------# Get last modified image# -----------------------------------------------------------------------------files = bucket.objects.filter()files = [obj.key for obj in sorted(files, key=lambda x: x.last_modified, reverse=True)]
for imagename in files:query_image = imagenamebreak
search_image_name = DOWNLOAD_DIR + query_image
try:s3.Bucket(BUCKET_NAME).download_file(query_image, search_image_name)except botocore.exceptions.ClientError as e:print(S3_ERROR_MESSAGE)
# -----------------------------------------------------------------------------# Search Milvus for that vector and filter by a label# -----------------------------------------------------------------------------results = milvus_client.search(COLLECTION_NAME,data=[extractor(search_image_name)],filter='label in ["keyboard"]',output_fields=["label", "confidence", "id", "s3path", "filename"],search_params={"metric_type": "COSINE"},limit=5)
# -----------------------------------------------------------------------------# Iterate through last five results and display metadata and image# -----------------------------------------------------------------------------for result in results:for hit in result[:5]:label = hit["entity"]["label"]confidence = hit["entity"]["confidence"]filename = hit["entity"]["filename"]s3path = hit["entity"]["s3path"]try:s3.Bucket(BUCKET_NAME).download_file(filename, DOWNLOAD_DIR + filename)except botocore.exceptions.ClientError as e:print(S3_ERROR_MESSAGE)print(f"Detection: {label} {confidence:.2f} for {filename} from {s3path}" )img = Image.open(DOWNLOAD_DIR + filename)display(img) # Enhancement:we could also post this to slack or discord

tspann@five:/opt/demo $ hailortcli fw-control identify
Executing on device: 0000:01:00.0Identifying boardControl Protocol Version: 2Firmware Version: 4.17.0 (release,app,extended context switch buffer)Logger Version: 0Board Name: Hailo-8Device Architecture: HAILO8LSerial Number: HLDDLBB241601635Part Number: HM21LB1C2LAEProduct Name: HAILO-8L AI ACC M.2 B+M KEY MODULE EXT TMP
tspann@five:/opt/demo $ dmesg | grep -i hailo
[3.155152] hailo: Init module. driver version 4.17.0[3.155295] hailo 0000:01:00.0: Probing on: 1e60:2864...[3.155301] hailo 0000:01:00.0: Probing: Allocate memory for device extension, 11600[3.155321] hailo 0000:01:00.0: enabling device (0000 -> 0002)[3.155327] hailo 0000:01:00.0: Probing: Device enabled[3.155350] hailo 0000:01:00.0: Probing: mapped bar 0 - 0000000095e362ea 16384[3.155357] hailo 0000:01:00.0: Probing: mapped bar 2 - 000000005e2b2b7e 4096[3.155362] hailo 0000:01:00.0: Probing: mapped bar 4 - 000000008db50d03 16384[3.155365] hailo 0000:01:00.0: Probing: Force setting max_desc_page_size to 4096 (recommended value is 16384)[3.155375] hailo 0000:01:00.0: Probing: Enabled 64 bit dma[3.155378] hailo 0000:01:00.0: Probing: Using userspace allocated vdma buffers[3.155382] hailo 0000:01:00.0: Disabling ASPM L0s[3.155385] hailo 0000:01:00.0: Successfully disabled ASPM L0s[3.417111] hailo 0000:01:00.0: Firmware was loaded successfully[3.427885] hailo 0000:01:00.0: Probing: Added board 1e60-2864, /dev/hailo0

gst-inspect-1.0 hailotoolslspci | grep Hailouname -av4l2-ctl --list-formats-ext -d /dev/video0ls /dev/video*ffplay -f v4l2 /dev/video0