Computer vision is transforming industries by enabling machines to understand visual content at scale. Our solutions use state-of-the-art deep learning models (YOLO, ResNet, EfficientNet, Vision Transformers) to extract insights from images and videos. Whether you need to detect defects in manufacturing, recognize faces for security, read text from documents, count people in crowds, or analyze medical images, we build custom computer vision systems that automate visual inspection tasks that previously required human eyes. Our solutions process millions of images with consistent accuracy, work 24/7, and continuously improve through active learning.
Manufacturers automating quality inspection, retailers implementing cashierless checkout, security companies deploying facial recognition, healthcare providers analyzing medical images, agriculture businesses monitoring crops, real estate companies processing property images, autonomous vehicle developers, and any organization processing visual data at scale.
Real-time detection of objects in images and videos using YOLO, Faster R-CNN, or EfficientDet. Multi-object tracking across video frames with DeepSORT. Custom object classes for your domain.
Categorize images into custom classes with high accuracy. ResNet, EfficientNet, Vision Transformers trained on your data. Multi-label classification and hierarchical categories supported.
Face detection, recognition, and verification with 99.8% accuracy. Support for face landmarks, age estimation, emotion detection, and liveness detection for security applications.
Extract text from images, PDFs, and scanned documents. Support for 100+ languages, handwriting recognition, table extraction, and document layout analysis using Tesseract and custom models.
Pixel-level classification for medical imaging, autonomous vehicles, and scene understanding. Instance segmentation using Mask R-CNN for separating individual objects.
Automated defect detection in manufacturing. Identify scratches, dents, missing components, color variations, and dimensional errors with higher accuracy than human inspection.
Extract insights from video streams including activity recognition, people counting, behavior analysis, and anomaly detection. Real-time processing and historical analysis.
Automated document classification, key information extraction, form processing, and invoice parsing. Convert unstructured visual documents into structured data.
Super-resolution, denoising, color correction, and image restoration. Improve low-quality images for better analysis or user experience using GANs and advanced techniques.
Train custom models on your proprietary images using transfer learning, data augmentation, and active learning. Continuous improvement as more data becomes available.
Deploy models on edge devices (NVIDIA Jetson, Raspberry Pi, mobile devices) with TensorRT, ONNX, and TFLite optimization for real-time inference without cloud dependency.
RESTful APIs for image analysis, webhook integrations, batch processing pipelines, and SDKs for popular platforms. Easy integration with your existing systems.
Define visual tasks, success metrics, and accuracy requirements. Assess data availability and identify edge cases that need special handling.
Gather representative image datasets covering all scenarios. Ensure diversity in lighting, angles, backgrounds, and object variations.
Annotate images with bounding boxes, polygons, keypoints, or pixel-level masks. Use active learning to prioritize labeling efforts on hard examples.
Choose appropriate architecture based on accuracy, speed, and deployment constraints. Consider YOLO for speed, Mask R-CNN for accuracy, or EfficientNet for balance.
Train models using transfer learning from ImageNet or COCO. Apply data augmentation, tune hyperparameters, and validate on held-out test sets.
Test on diverse scenarios including edge cases. Measure precision, recall, mAP, IoU, and inference speed. Ensure performance across all conditions.
Deploy to cloud or edge devices with proper optimization. Set up APIs, monitoring, and alerting for production use.
Track prediction confidence, detect model drift, and collect failure cases for retraining. Continuously improve accuracy through active learning.
Let's discuss your project and see how we can help bring your vision to life.
START A PROJECT# Production-Ready Object Detection with YOLO and Tracking
import cv2
import torch
from ultralytics import YOLO
from deep_sort_realtime.deepsort_tracker import DeepSort
import numpy as np
from fastapi import FastAPI, File, UploadFile
from typing import List, Dict
import time
class ObjectDetectionPipeline:
def __init__(self, model_path='yolov8x.pt', confidence_threshold=0.5):
# Load YOLOv8 model
self.model = YOLO(model_path)
self.confidence_threshold = confidence_threshold
# Initialize DeepSORT for object tracking
self.tracker = DeepSort(max_age=30, n_init=3)
# Class names for COCO dataset
self.class_names = self.model.names
# Performance metrics
self.fps_history = []
def detect_objects(self, image: np.ndarray) -> List[Dict]:
"""
Detect objects in an image with bounding boxes and confidence scores
"""
start_time = time.time()
# Run inference
results = self.model(image, conf=self.confidence_threshold)[0]
detections = []
for box in results.boxes:
# Extract box coordinates
x1, y1, x2, y2 = box.xyxy[0].cpu().numpy()
confidence = box.conf[0].cpu().numpy()
class_id = int(box.cls[0].cpu().numpy())
class_name = self.class_names[class_id]
detections.append({
'bbox': [int(x1), int(y1), int(x2), int(y2)],
'confidence': float(confidence),
'class': class_name,
'class_id': class_id
})
# Calculate FPS
inference_time = time.time() - start_time
fps = 1 / inference_time if inference_time > 0 else 0
self.fps_history.append(fps)
return detections
def detect_and_track(self, frame: np.ndarray) -> tuple:
"""
Detect and track objects across video frames
"""
# Detect objects
detections = self.detect_objects(frame)
# Prepare detections for DeepSORT
# Format: ([x, y, w, h], confidence, class_name)
deepsort_input = []
for det in detections:
x1, y1, x2, y2 = det['bbox']
w, h = x2 - x1, y2 - y1
deepsort_input.append((
[x1, y1, w, h],
det['confidence'],
det['class']
))
# Update tracker
tracked_objects = self.tracker.update_tracks(
deepsort_input, frame=frame)
# Add tracking IDs to detections
tracking_info = []
for track in tracked_objects:
if not track.is_confirmed():
continue
track_id = track.track_id
bbox = track.to_ltrb() # left, top, right, bottom
tracking_info.append({
'track_id': track_id,
'bbox': [int(x) for x in bbox],
'class': track.get_det_class()
})
return detections, tracking_info
def annotate_image(self, image: np.ndarray,
detections: List[Dict]) -> np.ndarray:
"""
Draw bounding boxes and labels on image
"""
annotated = image.copy()
for det in detections:
x1, y1, x2, y2 = det['bbox']
confidence = det['confidence']
class_name = det['class']
track_id = det.get('track_id')
# Draw bounding box
color = self._get_color_for_class(det['class_id'])
cv2.rectangle(annotated, (x1, y1), (x2, y2), color, 2)
# Draw label
label = f"{class_name} {confidence:.2f}"
if track_id:
label = f"ID:{track_id} {label}"
label_size, baseline = cv2.getTextSize(
label, cv2.FONT_HERSHEY_SIMPLEX, 0.5, 2)
y1_label = max(y1, label_size[1] + 10)
cv2.rectangle(annotated,
(x1, y1_label - label_size[1] - 10),
(x1 + label_size[0], y1_label + baseline - 10),
color, cv2.FILLED)
cv2.putText(annotated, label, (x1, y1_label - 7),
cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 0), 2)
return annotated
def process_video(self, video_path: str, output_path: str = None):
"""
Process video file with object detection and tracking
"""
cap = cv2.VideoCapture(video_path)
fps = int(cap.get(cv2.CAP_PROP_FPS))
width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
if output_path:
fourcc = cv2.VideoWriter_fourcc(*'mp4v')
out = cv2.VideoWriter(output_path, fourcc, fps, (width, height))
frame_count = 0
object_counts = {}
while cap.isOpened():
ret, frame = cap.read()
if not ret:
break
# Detect and track objects
detections, tracking_info = self.detect_and_track(frame)
# Count objects
for track in tracking_info:
class_name = track['class']
object_counts[class_name] = object_counts.get(
class_name, 0) + 1
# Annotate frame
annotated_frame = self.annotate_image(frame, tracking_info)
# Add statistics overlay
stats = f"Frame: {frame_count} | Objects: {len(tracking_info)}"
cv2.putText(annotated_frame, stats, (10, 30),
cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2)
if output_path:
out.write(annotated_frame)
frame_count += 1
cap.release()
if output_path:
out.release()
return {
'total_frames': frame_count,
'object_counts': object_counts,
'avg_fps': np.mean(self.fps_history)
}
def _get_color_for_class(self, class_id: int) -> tuple:
"""Generate consistent color for each class"""
np.random.seed(class_id)
return tuple(np.random.randint(0, 255, 3).tolist())
# FastAPI endpoint for image detection
app = FastAPI()
detector = ObjectDetectionPipeline(confidence_threshold=0.6)
@app.post("/detect")
async def detect_objects_api(file: UploadFile = File(...)):
"""API endpoint for object detection"""
contents = await file.read()
nparr = np.frombuffer(contents, np.uint8)
image = cv2.imdecode(nparr, cv2.IMREAD_COLOR)
detections = detector.detect_objects(image)
return {
'detections': detections,
'count': len(detections),
'timestamp': time.time()
}
# Example usage
if __name__ == "__main__":
pipeline = ObjectDetectionPipeline()
# Process image
image = cv2.imread('input.jpg')
detections = pipeline.detect_objects(image)
annotated = pipeline.annotate_image(image, detections)
cv2.imwrite('output.jpg', annotated)
# Process video
stats = pipeline.process_video('input.mp4', 'output.mp4')
print(f"Processed {stats['total_frames']} frames at {stats['avg_fps']:.2f} FPS")This production-ready pipeline combines YOLOv8 for detection with DeepSORT for tracking,
includes API endpoints, and processes both images and videos with real-time annotation.