--- name: opencv-7fd1aaddcf00 description: Expert OpenCV assistance covering computer vision, image filtering, edge detection, contour analysis, and real-time video processing. Use when processing visual data, building camera pipelines, or object detection. --- # OpenCV OpenCV (Open Source Computer Vision Library) is the standard library for real-time computer vision, image processing, camera calibration, and deep learning model inference. ## When to Use - **Real-Time Computer Vision & Video Processing**: Frame-by-frame analysis of camera feeds, robotics, and industrial automation. - **Image Preprocessing for Deep Learning**: Resizing, normalizing, thresholding, and color-space conversions. - **Edge, Contour & Shape Detection**: Canny edge detection, Hough transforms, and bounding box polygon calculations. - **Feature Matching & Homography**: Aligning perspectives and matching keypoints with ORB and SIFT. ## Quick Start ```python import cv2 # Load image, convert to grayscale, apply Gaussian blur and Canny edge detection img = cv2.imread("input.jpg") gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) blurred = cv2.GaussianBlur(gray, (5, 5), 0) edges = cv2.Canny(blurred, threshold1=50, threshold2=150) cv2.imwrite("edges.jpg", edges) ``` ## Core Concepts ### Image Preprocessing, Thresholding & Contours Detecting object shapes and drawing bounding boxes: ```python import cv2 import numpy as np # Load image in grayscale image = cv2.imread('circuit_board.jpg') gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) # Apply Gaussian blur to reduce high-frequency noise blurred = cv2.GaussianBlur(gray, (5, 5), 0) # Canny edge detection edges = cv2.Canny(blurred, threshold1=50, threshold2=150) # Find contours in the binary edge image contours, _ = cv2.findContours(edges, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE) output = image.copy() for cnt in contours: area = cv2.contourArea(cnt) if area > 100: # Filter small noise specks x, y, w, h = cv2.boundingRect(cnt) cv2.rectangle(output, (x, y), (x + w, y + h), (0, 255, 0), 2) cv2.putText(output, f"Area: {int(area)}", (x, y - 5), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 1) cv2.imwrite('detected_components.jpg', output) ``` ### Color Space Filtering with HSV Masking Isolating colored objects independent of scene brightness: ```python # Convert to Hue-Saturation-Value (HSV) hsv = cv2.cvtColor(image, cv2.COLOR_BGR2HSV) # Define range for blue color in HSV lower_blue = np.array([100, 150, 50]) upper_blue = np.array([140, 255, 255]) # Threshold image to obtain binary mask mask = cv2.inRange(hsv, lower_blue, upper_blue) # Bitwise-AND mask and original image blue_segmented = cv2.bitwise_and(image, image, mask=mask) cv2.imwrite('blue_segmented.jpg', blue_segmented) ``` ### Video Stream Processing Loop Reading frames continuously from a camera feed or video file: ```python cap = cv2.VideoCapture(0) # 0 = default webcam while cap.isOpened(): ret, frame = cap.read() if not ret: break # Process frame gray_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) cv2.imshow('Live Monitor', gray_frame) if cv2.waitKey(1) & 0xFF == ord('q'): break cap.release() cv2.destroyAllWindows() ``` ## Common Patterns ### Real-Time Video Stream Processing with FPS Throttling **Problem**: Processing high-resolution webcam frames in Python causes frame buffering lag. **Solution**: Process frames in a tight loop with resolution resizing: ```python cap = cv2.VideoCapture(0) # Set capture resolution cap.set(cv2.CAP_PROP_FRAME_WIDTH, 640) cap.set(cv2.CAP_PROP_FRAME_HEIGHT, 480) while cap.isOpened(): ret, frame = cap.read() if not ret: break # Process frame gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) cv2.imshow('Live Stream', gray) if cv2.waitKey(1) & 0xFF == ord('q'): break cap.release() cv2.destroyAllWindows() ``` ## Best Practices **Do**: - Remember OpenCV stores images in BGR format by default; convert with `cv2.COLOR_BGR2RGB` before Matplotlib/PyTorch. - Always release hardware resources (`cap.release()`) and destroy windows (`cv2.destroyAllWindows()`). - Use HSV or LAB color spaces rather than RGB when performing color-based segmentation under varying lighting. - Apply Gaussian or bilateral blurring prior to edge detection to suppress noise. **Don't**: - Perform heavy deep learning inferences sequentially inside the video capture loop; use background threads. - Hardcode kernel sizes with even numbers; blurring and morphological kernels should be odd (e.g. 3x3, 5x5). - Assume `cv2.imread()` succeeded without checking if the returned array is not `None`. ## Troubleshooting | Error | Cause | Solution | | :------------------------------------------------------ | :---------------------------------------------------------------------- | :--------------------------------------------------------------- | | `cv2.error: (-215:Assertion failed) !ssize.empty()` | `cv2.imread()` returned None because file path was wrong. | Verify image file exists at specified path and is non-empty. | | `OpenCV: FFMPEG: tag 0x... is not supported with codec` | Incompatible video codec FourCC code during VideoWriter initialization. | Use `cv2.VideoWriter_fourcc(*'mp4v')` for MP4 video output. | | `Color distortion in exported image` | Displaying or saving BGR image as RGB (OpenCV default is BGR). | Convert color space with `cv2.cvtColor(img, cv2.COLOR_BGR2RGB)`. | ## References - [OpenCV Documentation](https://opencv.org/) --- Source attribution: G1Joshi Original project: https://github.com/G1Joshi/Agent-Skills/blob/6a74cf03a8a86dc7d993ead3091b41123dbd0181/skills/ai-ml/opencv/SKILL.md License: MIT License information: https://opensource.org/license/mit Original upstream instructions; 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THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. # Mega AI Hub resource metadata Creator / publisher: G1Joshi Original project: https://github.com/G1Joshi/Agent-Skills/blob/6a74cf03a8a86dc7d993ead3091b41123dbd0181/skills/ai-ml/opencv/SKILL.md Attribution basis: Source repository record Fields of work: Data & Analytics, Development & Engineering Roles: Backend Developer, Data Scientist, ML / AI Engineer Specialties: API, Cross Platform Tools: Opencv Country applicability: Country not specified Classification basis: Source catalog labels Instruction SHA-256: 5a254d53f59dddec4e4e7bcf4f51de983e2bca4ec6221cf025286b54179cbca8