opencv

name opencv description You are an expert in OpenCV (Open Source Computer Vision Library), the most popular library for real-time computer vision. You help developers build image processing pipelines, object detection systems, video analysis tools, augmented reality, and document processing using OpenCV's 2,500+ algorithms for image manipulation, feature detection, camera calibration, 3D reconstruction, and DNN inference — in Python, C++, or JavaScript. license Apache-2.0 compatibility metadata {"author":"terminal-skills","version":"1.0.0","category":"AI & Machine Learning","tags":["computer-vision","image-processing","video","python","real-time","detection"]} OpenCV — Computer Vision Library You are an expert in OpenCV (Open Source Computer Vision Library), the most popular library for real-time computer vision. You help developers build image processing pipelines, object detection systems, video analysis tools, augmented reality, and document processing using OpenCV's 2,500+ algorithms for image manipulation, feature detection, camera calibration, 3D reconstruction, and DNN inference — in Python, C++, or JavaScript. Core Capabilities Image Processing import cv2 import numpy as np # Read and display img = cv2.imread( "photo.jpg" ) gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) # Resize resized = cv2.resize(img, ( 800 , 600 )) # Or maintain aspect ratio scale = 800 / img.shape[ 1 ] resized = cv2.resize(img, None , fx=scale, fy=scale) # Blur (noise reduction) blurred = cv2.GaussianBlur(img, ( 5 , 5 ), 0 ) median = cv2.medianBlur(img, 5 ) # Better for salt-and-pepper noise # Edge detection edges = cv2.Canny(gray, 50 , 150 ) # Thresholding _, binary = cv2.threshold(gray, 127 , 255 , cv2.THRESH_BINARY) adaptive = cv2.adaptiveThreshold(gray, 255 , cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY, 11 , 2 ) # Morphological operations kernel = np.ones(( 5 , 5 ), np.uint8) dilated = cv2.dilate(binary, kernel, iterations= 1 ) eroded = cv2.erode(binary, kernel, iterations= 1 ) opened = cv2.morphologyEx(binary, cv2.MORPH_OPEN, kernel) # Remove noise closed = cv2.morphologyEx(binary, cv2.MORPH_CLOSE, kernel) # Fill gaps Object Detection (DNN Module) # YOLO inference with OpenCV DNN net = cv2.dnn.readNetFromONNX( "yolov8n.onnx" ) def detect_objects ( image, conf_threshold= 0.5 ): """Detect objects using YOLOv8 with OpenCV DNN backend. Args: image: BGR image (numpy array) conf_threshold: Minimum confidence to keep detection Returns: List of (class_id, confidence, x, y, w, h) tuples """ blob = cv2.dnn.blobFromImage(image, 1 / 255.0 , ( 640 , 640 ), swapRB= True , crop= False ) net.setInput(blob) outputs = net.forward(net.getUnconnectedOutLayersNames()) detections = [] h, w = image.shape[: 2 ] for output in outputs: for detection in output[ 0 ]: scores = detection[ 4 :] class_id = np.argmax(scores) confidence = scores[class_id] if confidence > conf_threshold: cx, cy, bw, bh = detection[: 4 ] x = int ((cx - bw/ 2 ) * w / 640 ) y = int ((cy - bh/ 2 ) * h / 640 ) detections.append((class_id, float (confidence), x, y, int (bw*w/ 640 ), int (bh*h/ 640 ))) return detections Video Processing # Real-time video processing cap = cv2.VideoCapture( 0 ) # Webcam # cap = cv2.VideoCapture("video.mp4") # File # Output video fourcc = cv2.VideoWriter_fourcc(* "mp4v" ) out = cv2.VideoWriter( "output.mp4" , fourcc, 30.0 , ( 640 , 480 )) while cap.isOpened(): ret, frame = cap.read() if not ret: break # Process frame processed = cv2.GaussianBlur(frame, ( 15 , 15 ), 0 ) edges = cv2.Canny(frame, 50 , 150 ) # Draw detections for cls, conf, x, y, w, h in detect_objects(frame): cv2.rectangle(frame, (x, y), (x+w, y+h), ( 0 , 255 , 0 ), 2 ) cv2.putText(frame, f" {CLASSES[cls]} {conf: .2 f} " , (x, y- 10 ), cv2.FONT_HERSHEY_SIMPLEX, 0.5 , ( 0 , 255 , 0 ), 2 ) out.write(frame) cv2.imshow( "Detection" , frame) if cv2.waitKey( 1 ) & 0xFF == ord ( "q" ): break cap.release() out.release() Contours and Shape Detection # Find and analyze contours gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) _, thresh = cv2.threshold(gray, 127 , 255 , cv2.THRESH_BINARY) contours, hierarchy = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE) for contour in contours: area = cv2.contourArea(contour) if area < 100 : # Skip small noise continue # Bounding box x, y, w, h = cv2.boundingRect(contour) # Shape approximation peri = cv2.arcLength(contour, True ) approx = cv2.approxPolyDP(contour, 0.04 * peri, True ) sides = len (approx) shape = "circle" if sides > 8 else { 3 : "triangle" , 4 : "rectangle" }.get(sides, "polygon" ) cv2.drawContours(img, [contour], - 1 , ( 0 , 255 , 0 ), 2 ) cv2.putText(img, shape, (x, y - 10 ), cv2.FONT_HERSHEY_SIMPLEX, 0.5 , ( 255 , 0 , 0 ), 2 ) Installation pip install opencv-python # Core + main modules pip install opencv-contrib-python # + extra modules (SIFT, face detection, tracking) pip install opencv-python-headless # Without GUI (for servers) Best Practices BGR not RGB — OpenCV loads images in BGR; convert with cv2.cvtColor when using with matplotlib or PIL DNN for inference — Use cv2.dnn for running YOLO, SSD, face detection; no PyTorch/TF dependency needed Preprocessing pipeline — Resize → blur → convert → threshold → morphology → contours; order matters Contour hierarchy — Use RETR_EXTERNAL for outermost contours only; RETR_TREE for nested relationships Video with codec — Use mp4v for MP4, XVID for AVI; check codec availability on your platform NumPy integration — OpenCV images are NumPy arrays; use NumPy for fast pixel-level operations Headless for servers — Install opencv-python-headless ; no X11/GUI dependencies needed for processing pipelines GPU acceleration — Build from source with CUDA support for 10-50x speedup on GPU; or use cv2.cuda module