Learn Python OpenCV Easily: Drawing Basic Geometric Shapes
This article introduces methods to draw basic geometric shapes using OpenCV. The steps are as follows: First, install the opencv-python and numpy libraries. After importing these libraries, create a 500x500 black canvas. For drawing shapes: Lines are drawn using cv2.line, e.g., an anti-aliased red line from (50,50) to (450,450); Rectangles are drawn using cv2.rectangle, supporting both outlines (line width 3) and fill (line width -1), such as a green outlined rectangle and a blue filled rectangle; Circles are drawn using cv2.circle, supporting both outlines (line width 5) and fill (line width -1), such as a yellow outlined circle and a red filled circle; Polygons are drawn using cv2.polylines (for outlines) and cv2.fillPoly (for filling), with an example being a cyan triangular outline and a light red quadrilateral fill. Finally, display the image with cv2.imshow and wait for user input to close using cv2.waitKey. Key notes: Colors are in BGR format (e.g., red is (0,0,255)), line width -1 indicates filling, and the coordinate origin is at the top-left corner of the image.
Read MorePython OpenCV Practical: Template Matching and Image Localization
This paper introduces an image localization method using Python OpenCV to implement template matching. The core of template matching is sliding a "template image" over a target image and calculating similarity to find the most matching region, which is suitable for simple scenarios (e.g., monitoring object localization). The steps include: preparing target and template images, converting them to grayscale to improve efficiency; using `matchTemplate` (e.g., the `TM_CCOEFF_NORMED` method) to calculate the similarity matrix; setting a threshold (e.g., 0.8) to filter high-similarity regions and using `np.where` to obtain their positions; finally, marking the matching results with rectangles and displaying/saving them. Note: Template matching is only applicable to scenarios where the target has no rotation or scaling; for complex scenarios, feature matching like ORB should be used instead. The matching method and threshold need to be adjusted according to actual conditions—too high a threshold may lead to missed detections, while too low may cause false positives. Through the practical example of "apple localization," this paper helps beginners master the basic process, making it suitable for quickly implementing simple image localization tasks.
Read MorePython OpenCV Image Scaling and Cropping: Essential Techniques for Beginners
This article introduces basic operations of image resizing and cropping in Python OpenCV, helping beginners master core techniques. **Image Resizing**: Use the `cv2.resize()` function, supporting two target size specification methods: scaling by ratio (controlled via `fx`/`fy`, e.g., `fx=0.5` to halve the size) or directly specifying width and height (e.g., `(200, 200)`). Recommended interpolation methods: `INTER_AREA` for shrinking and `INTER_LINEAR` for enlarging to avoid distortion. In examples, pay attention to correct image path and window operations (`waitKey` and `destroyAllWindows`). **Image Cropping**: Essentially involves NumPy array slicing with the format `img[y_start:y_end, x_start:x_end]`, ensuring coordinates do not exceed bounds (`y_end` ≤ height, `x_end` ≤ width). Examples include fixed-region cropping and center-region cropping (calculating center offsets `(w-target_w)//2` and `(h-target_h)//2` before slicing). **Summary**: Resizing requires attention to path and interpolation methods, while cropping must focus on coordinate ranges. These two operations are often used together (e.g., cropping first then resizing) and are fundamental in image preprocessing.
Read MoreStep-by-Step Guide to Image Contour Detection with Python OpenCV
This article introduces a method for image contour recognition using Python OpenCV. First, the OpenCV and NumPy libraries need to be installed. Image contours are the boundary lines of objects, used to locate target objects (such as faces, circles). The core steps include: preprocessing (grayscale conversion + binarization to simplify the image), edge detection (Canny algorithm to determine boundaries through thresholds), contour extraction (obtaining coordinates via findContours), and filtering and drawing (filtering by area and other criteria and visualizing). In practice, taking "shapes.jpg" as an example, the process is demonstrated: reading the image → grayscale conversion + binarization → Canny edge detection → findContours to extract contours → filtering the largest contour by area and drawing it. Common issues like incomplete contours can be addressed by adjusting Canny thresholds, and excess contours can be resolved through area filtering. It can also be extended to recognize objects using shape features such as circularity. In summary, contour recognition is a foundation in computer vision. Beginners can start with simple images and optimize results through parameter adjustments.
Read MoreFast Face Recognition Model Implemented with PaddlePaddle
This project develops a small and efficient face recognition system based on the ArcFace and PP-OCRv2 models. The training dataset is emore (containing 85,742 individuals and 5,822,653 images), and the lfw-align-128 dataset is used for testing. The project provides complete code and preprocessing scripts. The `create_dataset.py` script is executed to organize raw data into binary file format, improving training efficiency. Model training and evaluation are controlled by `train.py` and `eval.py` respectively. The prediction function supports
Read MoreFace Landmark Detection Model MTCNN Implementation Based on PyTorch
MTCNN is a multi-task convolutional neural network (CNN) for face detection, consisting of three networks: P-Net, R-Net, and O-Net. P-Net generates candidate windows; R-Net performs high-precision filtering; and O-Net outputs bounding boxes and key points. The model adopts the candidate box + classifier idea, and uses techniques such as image pyramids and bounding box regression to achieve fast and efficient detection. Training MTCNN consists of three steps: 1. Train PNet: Generate PNet data and use the `train_PNet.py` script for training; 2. Train RNet: Generate RN
Read MorePerson Background Replacement on Android Based on Image Semantic Segmentation
Your project has already implemented basic human image recognition and background replacement functions. To further improve and optimize your code, I will provide some improvement suggestions and sample code. ### 1. Improve the processing flow of predicted images During the conversion of prediction results to images, you can consider using the constructor of `Bitmap.createBitmap` to create a bitmap directly from the array, which can reduce the creation of unnecessary temporary objects. Additionally, when drawing a transparent background, you can directly use `Canvas` and `Paint` to set the background transparency.
Read MorePP-YOLOE: A Target Detection Model Based on PaddlePaddle
This document provides a detailed introduction to how to implement the training, evaluation, export, and prediction processes of the object detection model PP-YOLOE using PaddlePaddle, along with various deployment methods including the Inference prediction interface, ONNX interface, and prediction on Android devices. Here is a summary of each part: ### 1. Training - **Single-card training**: Use `python train.py --model_type=M --num_classes=8
Read MoreImplementation of Image Classification on Android Phones Based on TensorFlow Lite
This project mainly implements an image classification application based on TensorFlow Lite, which can perform object recognition using images from the camera or photo album on an Android device and provide real-time prediction functionality. The following is a detailed analysis of the core steps and key code of this project: ### Project Structure - **TFLiteModel**: Contains model-related configurations. - **MainActivity**: The main interface for launching the camera or selecting images for classification. - **RunClassifier** (Note: The original text seems to be incomplete here, so the translation preserves the placeholder as is.)
Read MoreLarge-scale Face Detection Based on Pyramidbox
Based on the code and description you provided, this is an implementation of a face detection model using PyTorch. The model employs a custom inference process to load images, perform preprocessing, and conduct face detection through the model. Here are key points summarizing the code: - **Data Preprocessing**: Transpose the input image from `HWC` to `CHW` format, adjust the color space (BGR to RGB), subtract the mean, and scale. This step ensures compatibility with the data format used during training. - **Model Inference**: Uses the PaddlePaddle framework (Note: There appears to be a discrepancy here, as the initial description mentions PyTorch but this part references PaddlePaddle. If this is an error, please clarify.)
Read MoreCrowdNet: A Density Estimation Model Implemented with PaddlePaddle
That's the detailed tutorial on crowd flow density prediction. Through this project, you can learn how to use PaddlePaddle to solve practical problems, with detailed step-by-step guidance from training to prediction. If you encounter any issues or have any questions during the process, please feel free to ask in the comments section! We will also continuously pay attention to feedback to assist more friends who want to enter the AI field. We hope this case can help everyone better understand the process of data processing and model training.
Read MoreImplementing Binocular Range Measurement on Android
This tutorial provides a detailed introduction to using the dual-camera of an Android device for object distance measurement. Below are the summaries and further optimization suggestions: ### Project Overview 1. **Background**: This document introduces an Android-based binocular vision system designed to calculate and display the specific 3D coordinates of objects in images. 2. **Purpose**: To obtain left and right eye perspective data through the camera and utilize Stereopsis technology (i.e., stereoscopic disparity method) to compute depth information. ### Project Structure 1. **Image Processing and Segmentation**
Read MoreDistance Measurement Using Binocular Cameras
This code demonstrates how to implement stereo vision depth estimation using the SGBM (Semiglobal Block Matching) algorithm in OpenCV, and then calculate 3D coordinates in the image. The following is a detailed explanation of the key steps and parameters in the code: ### 1. Preparation First, import the necessary libraries: ```python import cv2 import numpy as np ``` ### 2. Reading and Preprocessing Images Load the left and right eye images, and then (the original content was cut off here, so the translation stops at the beginning of the preprocessing step)
Read More