Computer Vision Engineer

import cv2
import numpy as np
import torch
import torchvision.transforms as transforms
from ultralytics import YOLO

class ObjectDetectionPipeline:
    def __init__(self, model_path='yolo11n.pt', confidence_threshold=0.5):
        self.model = YOLO(model_path)
        self.confidence_threshold = confidence_threshold
        
    def detect_objects(self, image_path):
        """
        Comprehensive object detection with post-processing
        """
        # Load and preprocess image
        image = cv2.imread(image_path)
        if image is None:
            raise ValueError(f"Could not load image from {image_path}")
        
        # Run inference
        results = self.model(image)
        
        # Extract detections
        detections = []
        for result in results:
            boxes = result.boxes
            if boxes is not None:
                for box in boxes:
                    confidence = float(box.conf[0])
                    if confidence >= self.confidence_threshold:
                        detection = {
                            'class_id': int(box.cls[0]),
                            'class_name': self.model.names[int(box.cls[0])],
                            'confidence': confidence,
                            'bbox': box.xyxy[0].cpu().numpy().tolist(),
                            'center': self._calculate_center(box.xyxy[0])
                        }
                        detections.append(detection)
        
        return detections, image
    
    def _calculate_center(self, bbox):
        x1, y1, x2, y2 = bbox
        return {'x': float((x1 + x2) / 2), 'y': float((y1 + y2) / 2)}
    
    def draw_detections(self, image, detections):
        """
        Draw bounding boxes and labels on image
        """
        for detection in detections:
            bbox = detection['bbox']
            x1, y1, x2, y2 = map(int, bbox)
            
            # Draw bounding box
            cv2.rectangle(image, (x1, y1), (x2, y2), (0, 255, 0), 2)
            
            # Draw label
            label = f"{detection['class_name']}: {detection['confidence']:.2f}"
            label_size = cv2.getTextSize(label, cv2.FONT_HERSHEY_SIMPLEX, 0.5, 2)[0]
            cv2.rectangle(image, (x1, y1 - label_size[1] - 10), 
                         (x1 + label_size[0], y1), (0, 255, 0), -1)
            cv2.putText(image, label, (x1, y1 - 5), 
                       cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 0), 2)
        
        return image

import face_recognition
import pickle
from sklearn.metrics.pairwise import cosine_similarity

class FaceRecognitionSystem:
    """Lightweight dlib-based fallback. For production accuracy, use InsightFace
    (ArcFace embeddings) or DeepFace instead — see note above."""
    def __init__(self, model='hog', tolerance=0.6):
        self.model = model  # 'hog' or 'cnn'
        self.tolerance = tolerance
        self.known_encodings = []
        self.known_names = []
    
    def encode_faces_from_directory(self, directory_path):
        """
        Build face encoding database from directory structure
        """
        import os
        
        for person_name in os.listdir(directory_path):
            person_dir = os.path.join(directory_path, person_name)
            if not os.path.isdir(person_dir):
                continue
                
            person_encodings = []
            for image_file in os.listdir(person_dir):
                if image_file.lower().endswith(('.jpg', '.jpeg', '.png')):
                    image_path = os.path.join(person_dir, image_file)
                    encodings = self._get_face_encodings(image_path)
                    person_encodings.extend(encodings)
            
            if person_encodings:
                # Use average encoding for better robustness
                avg_encoding = np.mean(person_encodings, axis=0)
                self.known_encodings.append(avg_encoding)
                self.known_names.append(person_name)
    
    def _get_face_encodings(self, image_path):
        """
        Extract face encodings from image
        """
        image = face_recognition.load_image_file(image_path)
        face_locations = face_recognition.face_locations(image, model=self.model)
        face_encodings = face_recognition.face_encodings(image, face_locations)
        return face_encodings
    
    def recognize_faces_in_image(self, image_path):
        """
        Recognize faces in given image
        """
        image = face_recognition.load_image_file(image_path)
        face_locations = face_recognition.face_locations(image, model=self.model)
        face_encodings = face_recognition.face_encodings(image, face_locations)
        
        results = []
        for (top, right, bottom, left), face_encoding in zip(face_locations, face_encodings):
            # Compare with known faces
            matches = face_recognition.compare_faces(
                self.known_encodings, face_encoding, tolerance=self.tolerance
            )
            
            name = "Unknown"
            confidence = 0
            
            if True in matches:
                # Find best match
                face_distances = face_recognition.face_distance(
                    self.known_encodings, face_encoding
                )
                best_match_index = np.argmin(face_distances)
                
                if matches[best_match_index]:
                    name = self.known_names[best_match_index]
                    confidence = 1 - face_distances[best_match_index]
            
            results.append({
                'name': name,
                'confidence': float(confidence),
                'location': {'top': top, 'right': right, 'bottom': bottom, 'left': left}
            })
        
        return results

import easyocr
import cv2
import numpy as np
from PIL import Image
import pytesseract

class DocumentAnalyzer:
    def __init__(self, languages=['en'], use_gpu=False):
        self.reader = easyocr.Reader(languages, gpu=use_gpu)
        
    def extract_text_from_image(self, image_path, method='easyocr'):
        """
        Extract text using multiple OCR methods
        """
        if method == 'easyocr':
            return self._extract_with_easyocr(image_path)
        elif method == 'tesseract':
            return self._extract_with_tesseract(image_path)
        else:
            # Ensemble approach
            easyocr_results = self._extract_with_easyocr(image_path)
            tesseract_results = self._extract_with_tesseract(image_path)
            return self._combine_ocr_results(easyocr_results, tesseract_results)
    
    def _extract_with_easyocr(self, image_path):
        """
        Extract text using EasyOCR
        """
        results = self.reader.readtext(image_path)
        
        extracted_text = []
        for (bbox, text, confidence) in results:
            if confidence > 0.5:  # Filter low-confidence detections
                extracted_text.append({
                    'text': text,
                    'confidence': confidence,
                    'bbox': bbox,
                    'method': 'easyocr'
                })
        
        return extracted_text
    
    def _extract_with_tesseract(self, image_path):
        """
        Extract text using Tesseract OCR with preprocessing
        """
        # Load and preprocess image
        image = cv2.imread(image_path)
        gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
        
        # Apply image processing for better OCR
        denoised = cv2.medianBlur(gray, 5)
        thresh = cv2.threshold(denoised, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)[1]
        
        # Extract text with bounding box information
        data = pytesseract.image_to_data(thresh, output_type=pytesseract.Output.DICT)
        
        extracted_text = []
        for i in range(len(data['text'])):
            if int(data['conf'][i]) > 60:  # Confidence threshold
                text = data['text'][i].strip()
                if text:
                    extracted_text.append({
                        'text': text,
                        'confidence': int(data['conf'][i]) / 100.0,
                        'bbox': [
                            data['left'][i], data['top'][i],
                            data['left'][i] + data['width'][i],
                            data['top'][i] + data['height'][i]
                        ],
                        'method': 'tesseract'
                    })
        
        return extracted_text
    
    def detect_document_structure(self, image_path):
        """
        Analyze document structure and layout.

        Don't hand-roll text/table/figure region detection with raw contour
        heuristics — it's brittle across document types. Use a dedicated
        layout-analysis library or a VLM instead:
        - `layoutparser` (Detectron2-backed models) for classic form/report layouts
        - `unstructured` for mixed-format document partitioning (PDF, image, HTML)
        - A multimodal LLM (Claude vision, GPT-4V, Gemini) for ad-hoc or
          low-volume layouts where training a layout model isn't justified
        """
        import layoutparser as lp

        image = cv2.imread(image_path)
        # PubLayNet was trained on RGB; cv2.imread loads BGR, so convert
        # before detect() or the color-channel mismatch hurts accuracy
        image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
        model = lp.Detectron2LayoutModel(
            'lp://PubLayNet/faster_rcnn_R_50_FPN_3x/config'
        )
        layout = model.detect(image)

        return {
            # PubLayNet's 'Text' label excludes headings/list items, which
            # are still text — include 'Title' and 'List' so they aren't dropped
            'text_regions': [b for b in layout if b.type in ('Text', 'Title', 'List')],
            'tables': [b for b in layout if b.type == 'Table'],
            'figures': [b for b in layout if b.type == 'Figure']
        }

import cv2
import threading
from queue import Queue

class VideoAnalyzer:
    def __init__(self, model_path, buffer_size=10):
        self.model = YOLO(model_path)
        self.frame_queue = Queue(maxsize=buffer_size)
        self.result_queue = Queue()
        self.processing = False
        self._capture_thread = None
        self._process_thread = None
        self._cap = None

    def stop_processing(self, timeout=5.0):
        """
        Stop any running stream and wait for its threads to exit before the
        shared frame/result queues or model are reused.
        """
        self.processing = False

        # cap.read() blocks until a frame arrives (or the source stalls), so
        # a plain join() can hang past the timeout with the capture thread
        # still alive. Releasing the capture here makes read() return
        # immediately, so the thread observes processing == False right away.
        if self._cap is not None:
            self._cap.release()

        for thread in (self._capture_thread, self._process_thread):
            if thread is not None and thread.is_alive():
                thread.join(timeout=timeout)
                if thread.is_alive():
                    raise RuntimeError(
                        f"{thread.name} did not stop within {timeout}s; "
                        "refusing to reuse the shared model/queues while it "
                        "may still be running"
                    )

        # Drain queues so frames from the old stream don't leak into the next one
        for q in (self.frame_queue, self.result_queue):
            while not q.empty():
                try:
                    q.get_nowait()
                except Exception:
                    break

    def start_real_time_analysis(self, video_source=0):
        """
        Start real-time video analysis
        """
        # Stop any previous stream first — otherwise its capture/process
        # threads keep running against the shared frame_queue/result_queue
        # and model, mixing frames and tracker state across streams.
        # stop_processing is idempotent for dead/None threads (it only joins
        # live ones and always drains the queues), so call it unconditionally
        # rather than gating on liveness — gating would skip the queue drain
        # on a retry after a raised RuntimeError, leaking stale frames from
        # the old stream into the new one.
        self.stop_processing()

        # Reset any tracker state left over from a previous stream. With
        # persist=True, Ultralytics caches track state on self.model's
        # predictor across calls, so reusing this instance for a new,
        # independent video/camera would otherwise carry over stale IDs.
        # `trackers` is only registered on the predictor once model.track()
        # has run at least once — if this model was only ever used with
        # model.predict()/model() before, predictor exists but has no
        # trackers attribute yet, so use getattr rather than assuming it.
        trackers = getattr(getattr(self.model, 'predictor', None), 'trackers', None)
        if trackers:
            for tracker in trackers:
                tracker.reset()

        self.processing = True
        
        # Start capture thread
        capture_thread = threading.Thread(
            target=self._capture_frames, 
            args=(video_source,)
        )
        capture_thread.daemon = True
        capture_thread.start()
        
        # Start processing thread
        process_thread = threading.Thread(target=self._process_frames)
        process_thread.daemon = True
        process_thread.start()

        self._capture_thread = capture_thread
        self._process_thread = process_thread

        return capture_thread, process_thread
    
    def _capture_frames(self, video_source):
        """
        Capture frames from video source
        """
        cap = cv2.VideoCapture(video_source)
        self._cap = cap

        while self.processing:
            ret, frame = cap.read()
            if ret:
                if not self.frame_queue.full():
                    self.frame_queue.put(frame)
                else:
                    # Drop oldest frame
                    try:
                        self.frame_queue.get_nowait()
                        self.frame_queue.put(frame)
                    except:
                        pass
        
        cap.release()
    
    def _process_frames(self):
        """
        Process frames for object detection and tracking
        """
        while self.processing:
            if not self.frame_queue.empty():
                frame = self.frame_queue.get()
                
                # Run detection with persistent tracking (ByteTrack) so each
                # object keeps a stable ID across frames
                results = self.model.track(frame, persist=True, tracker="bytetrack.yaml")
                
                # Store results
                if not self.result_queue.full():
                    self.result_queue.put((frame, results))

import cv2
import numpy as np
from skimage.metrics import structural_similarity as ssim

class ImageQualityAssessment:
    def __init__(self):
        pass
    
    def assess_image_quality(self, image_path):
        """
        Comprehensive image quality assessment
        """
        image = cv2.imread(image_path)
        gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
        
        quality_metrics = {
            'brightness': self._assess_brightness(gray),
            'contrast': self._assess_contrast(gray),
            'sharpness': self._assess_sharpness(gray),
            'noise_level': self._assess_noise(gray),
            'blur_detection': self._detect_blur(gray),
            'overall_score': 0
        }
        
        # Calculate overall quality score
        quality_metrics['overall_score'] = self._calculate_overall_score(quality_metrics)
        
        return quality_metrics
    
    def _assess_brightness(self, gray_image):
        """Assess image brightness"""
        mean_brightness = np.mean(gray_image)
        return {
            'score': mean_brightness / 255.0,
            'assessment': 'good' if 50 <= mean_brightness <= 200 else 'poor'
        }
    
    def _assess_contrast(self, gray_image):
        """Assess image contrast"""
        contrast = gray_image.std()
        return {
            'score': min(contrast / 64.0, 1.0),
            'assessment': 'good' if contrast > 32 else 'poor'
        }
    
    def _assess_sharpness(self, gray_image):
        """Assess image sharpness using Laplacian variance"""
        laplacian_var = cv2.Laplacian(gray_image, cv2.CV_64F).var()
        return {
            'score': min(laplacian_var / 1000.0, 1.0),
            'assessment': 'good' if laplacian_var > 100 else 'poor'
        }
    
    def _assess_noise(self, gray_image):
        """Assess noise level"""
        # Simple noise estimation using high-frequency components
        kernel = np.array([[-1,-1,-1], [-1,8,-1], [-1,-1,-1]])
        noise_image = cv2.filter2D(gray_image, -1, kernel)
        noise_level = np.var(noise_image)
        
        return {
            'score': max(1.0 - noise_level / 10000.0, 0.0),
            'assessment': 'good' if noise_level < 1000 else 'poor'
        }
    
    def _detect_blur(self, gray_image):
        """Detect blur using FFT analysis"""
        f_transform = np.fft.fft2(gray_image)
        f_shift = np.fft.fftshift(f_transform)
        magnitude_spectrum = np.log(np.abs(f_shift) + 1)
        
        # Calculate high frequency content
        h, w = magnitude_spectrum.shape
        center_h, center_w = h // 2, w // 2
        high_freq_region = magnitude_spectrum[center_h-h//4:center_h+h//4, 
                                           center_w-w//4:center_w+w//4]
        high_freq_energy = np.mean(high_freq_region)
        
        return {
            'score': min(high_freq_energy / 10.0, 1.0),
            'assessment': 'sharp' if high_freq_energy > 5.0 else 'blurry'
        }
    
    def _calculate_overall_score(self, metrics):
        """Calculate weighted overall quality score"""
        weights = {
            'brightness': 0.2,
            'contrast': 0.3,
            'sharpness': 0.3,
            'noise_level': 0.2
        }
        
        weighted_sum = sum(metrics[key]['score'] * weights[key] 
                          for key in weights.keys())
        return weighted_sum

import torch
import onnx
import tensorrt as trt

class ModelOptimizer:
    def __init__(self):
        pass
    
    def optimize_pytorch_model(self, model, sample_input, optimization_level='O2'):
        """
        Optimize PyTorch model for inference
        """
        # Convert to TorchScript
        traced_model = torch.jit.trace(model, sample_input)
        
        # Optimize for inference
        traced_model.eval()
        traced_model = torch.jit.optimize_for_inference(traced_model)
        
        return traced_model
    
    def convert_to_onnx(self, model, sample_input, onnx_path):
        """
        Convert PyTorch model to ONNX format
        """
        torch.onnx.export(
            model,
            sample_input,
            onnx_path,
            export_params=True,
            opset_version=11,
            do_constant_folding=True,
            input_names=['input'],
            output_names=['output'],
            dynamic_axes={'input': {0: 'batch_size'}, 
                         'output': {0: 'batch_size'}}
        )
    
    def convert_to_tensorrt(self, onnx_path, tensorrt_path):
        """
        Convert ONNX model to TensorRT for NVIDIA GPU optimization.

        NOTE: The TensorRT Python API differs between major versions. This
        targets TensorRT 10.x — `builder.build_engine()` and
        `config.max_workspace_size` were removed/deprecated in TensorRT 10 and
        will return None / raise, silently breaking engine builds on newer
        installs. Use `build_serialized_network()` and
        `set_memory_pool_limit()` instead. If you're pinned to TensorRT 8.x,
        the older `build_engine`/`max_workspace_size` API still applies.
        """
        TRT_LOGGER = trt.Logger(trt.Logger.WARNING)
        builder = trt.Builder(TRT_LOGGER)
        network = builder.create_network(1 << int(trt.NetworkDefinitionCreationFlag.EXPLICIT_BATCH))
        parser = trt.OnnxParser(network, TRT_LOGGER)
        
        # Parse ONNX model
        with open(onnx_path, 'rb') as model:
            parser.parse(model.read())
        
        # Build TensorRT engine (TensorRT 10.x API)
        config = builder.create_builder_config()
        config.set_memory_pool_limit(trt.MemoryPoolType.WORKSPACE, 1 << 30)  # 1GB
        config.set_flag(trt.BuilderFlag.FP16)  # Enable FP16 precision
        
        serialized_engine = builder.build_serialized_network(network, config)
        
        # Save engine
        with open(tensorrt_path, "wb") as f:
            f.write(serialized_engine)

👁️ COMPUTER VISION ANALYSIS REPORT

## Image Analysis Results
- Objects detected: X objects across Y classes
- Confidence scores: Average X.XX (range: X.XX - X.XX)
- Processing time: X.XX seconds per image

## Model Performance
- Model used: [Model name and version]
- Accuracy metrics: [Precision, Recall, F1-score]
- Inference speed: X.XX FPS

## Quality Assessment
- Image quality score: X.XX/1.00
- Issues identified: [List of quality issues]
- Recommendations: [Improvement suggestions]