The following samples are ways of detecting and counting objects in images that do not require custom models to be trained and can be done after image processing and vectorizing. It is preferable to do agentic search where agents are versed in one of these techniques.

#! /usr/bin/python

import requests

import cv2

import numpy as np

from sklearn.cluster import DBSCAN

from sklearn.preprocessing import normalize

import hdbscan

import matplotlib.pyplot as plt

from io import BytesIO

from azure.core.credentials import AzureKeyCredential

from azure.ai.vision.imageanalysis import ImageAnalysisClient

from azure.ai.vision.imageanalysis.models import VisualFeatures

import os

match_threshold = 0.5

min_number_of_cluster_members = 2

# Azure Vision credentials

vision_endpoint=os.getenv("AZURE_AI_VISION_ENDPOINT")

vision_api_key = os.getenv("AZURE_AI_VISION_API_KEY")

object_uri = os.getenv("AZURE_RED_CAR_2_SAS_URL").strip('"')

scene_uri = os.getenv("AZURE_QUERY_SAS_URI").strip('"')

# Step 1: Download images from SAS URLs

def download_image(url):

    response = requests.get(url)

    image_array = np.frombuffer(response.content, np.uint8)

    return cv2.imdecode(image_array, cv2.IMREAD_COLOR)

# Step 2: Use OpenCV template matching to find object occurrences

def count_object_occurrences(scene, template, threshold=match_threshold):

    scene_gray = cv2.cvtColor(scene, cv2.COLOR_BGR2GRAY)

    template_gray = cv2.cvtColor(template, cv2.COLOR_BGR2GRAY)

    result = cv2.matchTemplate(scene_gray, template_gray, cv2.TM_CCOEFF_NORMED)

    locations = np.where(result >= threshold)

    w, h = template_gray.shape[::-1]

    rects = [[pt[0], pt[1], pt[0] + w, pt[1] + h] for pt in zip(*locations[::-1])]

    rects, _ = cv2.groupRectangles(rects, groupThreshold=1, eps=0.5)

    return len(rects)

# Step 3: Count matches

def count_matches():

    scene_img = download_image(scene_uri)

    object_img = download_image(object_uri)

    count = count_object_occurrences(scene_img, object_img)

    return count

# print(f"Detected {count_matches()} occurrences of the object.")

#Output: Detected 1 occurrences of the object.

# Load image from SAS URL

def load_image_from_sas(url):

    response = requests.get(url)

    image_array = np.frombuffer(response.content, np.uint8)

    return cv2.imdecode(image_array, cv2.IMREAD_COLOR)

def keypoints_and_descriptors(scene_img, object_img):

    orb = cv2.ORB_create(nfeatures=1000)

    kp1, des1 = orb.detectAndCompute(object_img, None)

    kp2, des2 = orb.detectAndCompute(scene_img, None)

    if des1 is None or des2 is None:

        return None, None, None, None

    return kp1, des1, kp2, des2

# Feature detection and matching

def get_matched_keypoints(scene_img, object_img):

    kp1,des1,kp2,des2 = keypoints_and_descriptors(scene_img, object_img)

    if des1 is None or des2 is None:

        return []

    matcher = cv2.BFMatcher(cv2.NORM_HAMMING, crossCheck=True)

    matches = matcher.match(des1, des2)

    matches = sorted(matches, key=lambda x: x.distance)

    matched_pts = np.float32([kp2[m.trainIdx].pt for m in matches])

    print(f"matched_pts={matched_pts}")

    return matched_pts

 # Extract matched descriptors using ORB

def get_matched_descriptors(scene_img, object_img):

    kp1,des1,kp2,des2 = keypoints_and_descriptors(scene_img, object_img)

    if des1 is None or des2 is None:

        return np.array([])

    matcher = cv2.BFMatcher(cv2.NORM_HAMMING, crossCheck=True)

    matches = matcher.match(des1, des2)

    matches = sorted(matches, key=lambda x: x.distance)

    matched_descriptors = np.array([des2[m.trainIdx] for m in matches])

    return matched_descriptors

# Cluster matched keypoints using DBSCAN

def cluster_keypoints(points, eps=30, min_samples=min_number_of_cluster_members):

    if len(points) == 0:

        return []

    clustering = DBSCAN(eps=eps, min_samples=min_samples).fit(points)

    labels = clustering.labels_

    return labels

# Cluster keypoints using HDBSCAN

def cluster_keypoints_hdbscan(points, min_cluster_size=min_number_of_cluster_members):

    if len(points) == 0:

        return np.array([])

    clusterer = hdbscan.HDBSCAN(min_cluster_size=min_cluster_size)

    labels = clusterer.fit_predict(points)

    # if len(labels) > 0:

    # plot_clusters(matched_points, labels)

    return labels

# Cluster descriptors using cosine similarity

def cluster_by_similarity(descriptors, min_cluster_size=min_number_of_cluster_members):

    if len(descriptors) == 0:

        return np.array([])

    # Normalize for cosine similarity

    descriptors = normalize(descriptors, norm='l2')

    clusterer = hdbscan.HDBSCAN(

        min_cluster_size=min_cluster_size,

        metric='euclidean', # Euclidean on normalized vectors ≈ cosine similarity

        cluster_selection_method='eom'

    )

    labels = clusterer.fit_predict(descriptors)

    return labels

# Optional: visualize clusters

def plot_clusters(points, labels):

    plt.figure(figsize=(8, 6))

    for label in set(labels):

        mask = labels == label

        color = 'gray' if label == -1 else None

        plt.scatter(points[mask, 0], points[mask, 1], label=f"Cluster {label}", alpha=0.6, s=30, c=color)

    plt.title("HDBSCAN Clusters of Matched Keypoints")

    plt.xlabel("X")

    plt.ylabel("Y")

    plt.legend()

    plt.show()

def count_multiple_matches():

    scene_img = load_image_from_sas(scene_uri)

    object_img = load_image_from_sas(object_uri)

    # matched_points = get_matched_keypoints(scene_img, object_img)

    # labels = cluster_keypoints_hdbscan(matched_points)

    descriptors = get_matched_descriptors(scene_img, object_img)

    labels = cluster_by_similarity(descriptors)

    print(f"len of labels={len(labels)} and labels={labels}")

    # Count valid clusters (excluding noise label -1)

    count = len(set(labels)) - (1 if -1 in labels else 0)

    return count

print(f"Estimated object instances: {count_multiple_matches()}")

# for dbscan

#Output: Estimated object instances: 3

# for hdbscan based on similarity

# len of labels=24 and labels=[ 1 -1 -1 1 -1 1 0 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 -1 -1 0 -1 1 -1]

# Estimated object instances: 2