A Beginner's Guide to Python OpenCV Morphological Operations (Easy to Understand!)
Morphological operations are shape-based methods in image processing. Their core is to interact with images through a structuring element, altering the shape characteristics of objects. Primarily used for binary images, they implement functions such as denoising, connecting objects, and filling holes. Basic types include: Erosion (shrinking bright regions, expanding dark regions; denoising but edge contraction), Dilation (expanding bright regions, filling dark holes; connecting breaks), Opening (erosion followed by dilation; denoising while preserving shape), and Closing (dilation followed by erosion; hole filling and edge optimization). A structuring element is a small matrix defining the shape and size of operations. OpenCV supports rectangles, ellipses, crosses, etc., created via `cv2.getStructuringElement`. For code implementation, steps include reading the image, binarization, defining the structuring element, performing erosion, dilation, opening/closing operations, and displaying results. Advanced operations like morphological gradient, top hat, and black hat can also extract edges or noise. Summary: Morphology is a fundamental tool for denoising, object connection, and edge extraction. Beginners can start with opening/closing operations, adjusting structuring element size and shape to practice applications in different scenarios.
Read MoreIntroduction to Python OpenCV Filter Effects: Blur and Sharpen Image Processing
This article introduces the basic operations of blurring and sharpening in digital image processing, suitable for beginners to implement using Python+OpenCV. Blurring is used for denoising and smoothing, with common methods including: Mean filtering (simple averaging, fast denoising but blurs details), Gaussian filtering (weighted averaging, natural blurring, removes Gaussian noise), Median filtering (median substitution, anti-salt-and-pepper noise while preserving edges), and Bilateral filtering (edge-preserving blurring, used for portrait beauty). Sharpening enhances edge details, with methods such as: Laplacian operator (second-order derivative, general sharpening), simple pixel superposition (directly highlights edges), and Sobel operator (gradient calculation, enhances edges). The article summarizes the characteristics of these methods in a comparison table and provides exercise suggestions, serving as a foundational introduction to image processing.
Read MoreLearning Python OpenCV from Scratch: Real - time Capture and Display with Camera
This article introduces a method to achieve real - time camera capture and display using Python and OpenCV. The reasons for choosing OpenCV (Open Source Computer Vision Library) and Python (with concise syntax) are their ease of use and functional adaptability. The opencv - python interface for Python is easy to install. Installation steps: First, install Python 3.6 or higher, and then install the library through `pip install opencv - python` (numpy may need to be installed first if necessary). Core process: Open the camera (`cv2.VideoCapture(0)`), loop to read frames (`cap.read()`, which returns ret and frame), display the image (`cv2.imshow()`), press the 'q' key to exit, and release resources (`cap.release()` and `cv2.destroyAllWindows()`). Key code explanation: `cap.read()` checks the reading status, `cv2.waitKey(1)` waits for a key press (the 'q' key to exit), and ensures that resources are correctly released to avoid occupation. The article also mentions common problems (such as the camera not opening) and extended exercises (such as grayscale display, image flipping, etc.), laying a foundation for subsequent complex image processing.
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 MoreEasy Guide: Python OpenCV Edge Detection Fundamentals
This article introduces the concept of image edge detection, its implementation in Python with OpenCV, and core algorithms. Edge detection identifies regions with significant changes in pixel intensity (e.g., object contours), a foundational technique in computer vision with applications in facial recognition, autonomous driving, etc. For environment setup, install Python and OpenCV (`pip install opencv-python`). The core workflow has three steps: image preprocessing (grayscale conversion, noise reduction), edge detection algorithms, and result visualization. The Canny edge detection algorithm (proposed by John Canny in 1986) is emphasized with the following steps: 1) Grayscale conversion (reduces computational complexity); 2) Gaussian blur (noise reduction, 5×5 kernel size is common); 3) Gradient calculation (using Sobel operators); 4) Non-maximum suppression (refines edges); 5) Double thresholding (low threshold 50-150, high threshold 150-200; threshold values affect edge sensitivity). Python code example: read image → grayscale conversion → blur → Canny detection → display results. Other algorithms include Sobel (gradient calculation) and Laplacian (second-order derivative), which require prior blur for noise reduction. Practical tips: prioritize blurring, adjust thresholds; common issues: image read failure (check file path).
Read MoreFrom Beginner to Practical: A Detailed Explanation of Python OpenCV Color Space Conversion
This article introduces the concept of image color spaces and the conversion applications in Python using OpenCV. Common color spaces include RGB (for display, with red/green/blue channels), BGR (OpenCV default, in blue/green/red order), and HSV (hue H, saturation S, value V, suitable for color segmentation). The conversion reasons are that different spaces serve different purposes (RGB for display, HSV for color recognition, BGR as OpenCV's native format). The core tool is `cv2.cvtColor()`, with the syntax `cv2.cvtColor(img, cv2.COLOR_originalSpace2targetSpace)`, e.g., `cv2.COLOR_BGR2HSV`. In practice, taking red object detection as an example: read the image → convert to HSV → define the red HSV range (H values in 0-10 and 160-179 intervals) → extract via mask. It can also be extended to real-time detection with a camera. Key points: master the conversion function, note the difference between BGR and RGB, and adjust HSV ranges according to light conditions.
Read MorePython OpenCV Tutorial: Master Image Binarization in 5 Minutes
Image binarization is a process that classifies pixels into black and white categories based on a threshold, simplifying images for easier analysis, and is commonly used in scenarios such as text recognition. The core implementation relies on the `cv2.threshold()` function, which requires inputting a grayscale image, a threshold value, a maximum value, and a type, returning the actual threshold and the binarized image. Common threshold types include: `THRESH_BINARY` (pixels above the threshold turn white), `THRESH_BINARY_INV` (the opposite), and `THRESH_OTSU` (automatically calculates the optimal threshold). For threshold selection: manual selection is suitable for images with uniform brightness, Otsu's method is ideal for high-contrast scenarios, and adaptive thresholds are used for uneven lighting. The key steps are: reading the image and converting it to grayscale → selecting the threshold type → performing binarization → displaying the result. Mastering binarization supports tasks such as edge detection and object segmentation.
Read MoreLearning Python OpenCV from Scratch: A Step-by-Step Guide to Reading and Displaying Images
This article introduces basic operations of Python OpenCV, including installation, image reading, and displaying. OpenCV is an open-source computer vision library. It can be installed via `pip install opencv-python` (or accelerated by domestic mirror sources). To verify, import the library and print the version number. For reading images, use `cv2.imread()`, specifying the path and parameters (color, grayscale, or original image), and check if the return value is `None` to confirm success. To display images, use `cv2.imshow()`, which should be accompanied by `cv2.waitKey(0)` to wait for a key press and `cv2.destroyAllWindows()` to close windows. Common issues: OpenCV reads images in BGR channels by default; use `cv2.cvtColor()` to convert to RGB to avoid color abnormalities. Path errors may cause reading failure; use absolute paths or confirm the image format. The core steps are installation, reading, and displaying, and hands-on practice can quickly master these operations.
Read MoreImplementing Radix Sort Algorithm in C++
Radix sort is a non-comparison integer sorting algorithm that uses the least significant digit first (LSD) approach, sorting numbers digit by digit (units, tens, etc.) without comparing element sizes. Its core idea is to process each digit using a stable counting sort, ensuring that the result of lower-digit sorting remains ordered during higher-digit sorting. Implementation steps: 1. Identify the maximum number in the array to determine the highest number of digits to process; 2. From the lowest digit to the highest, process each digit using counting sort: count the frequency of the current digit, compute positions, place elements stably from back to front, and finally copy back to the original array. In the C++ code, the `countingSort` helper function implements digit-wise sorting (counting frequencies, calculating positions, and stable placement), while the `radixSort` main function loops through each digit. The time complexity is O(d×(n+k)) (where d is the maximum number of digits, n is the array length, and k=10), making it suitable for scenarios with a large range of integers. The core lies in leveraging the stability of counting sort to ensure that the results of lower-digit sorting are not disrupted during higher-digit sorting. Test results show that the sorted array is ordered, verifying the algorithm's effectiveness.
Read MoreImplementing Bucket Sort Algorithm in C++
Bucket sort is a non-comparison sorting algorithm that sorts elements by distributing them into multiple "buckets", sorting each bucket individually, and then merging the sorted buckets. The core is to reasonably partition the buckets so that each bucket contains a small number of elements, thereby reducing the sorting cost. Taking floating-point numbers in the range [0,1) as an example, the algorithm steps are as follows: 1. Create n empty buckets (where n is the length of the array); 2. Assign each element x to the corresponding bucket using the bucket index calculated as the integer part of x * n; 3. Sort each bucket using std::sort; 4. Merge all elements from the buckets. In the C++ implementation, the `bucketSort` function creates n buckets using a vector of vectors of doubles, distributes elements into the buckets through traversal, sorts each bucket, and then merges the results. Testing verifies the correctness of the algorithm. Complexity analysis: The average time complexity is O(n) (when elements are uniformly distributed), and the worst-case time complexity is O(n log n) (when all elements are placed in the same bucket). The space complexity is O(n). It is suitable for numerical data with uniformly distributed values and a clear range; performance degrades when data distribution is uneven. This algorithm is efficient when the data distribution is reasonable, especially suitable for sorting interval data in statistical analysis.
Read MoreImplementing the Counting Sort Algorithm in C++
**Counting Sort** is a non-comparison sorting algorithm. Its core idea is to construct a sorted array by counting the occurrences of elements, making it suitable for scenarios where the range of integers is not large (e.g., student scores, ages). **Basic Idea**: Taking the array `[4, 2, 2, 8, 3, 3, 1]` as an example, the steps are: 1. Determine the maximum value (8) and create a count array `count` to statistics the occurrences of each element (e.g., `count[2] = 2`); 2. Insert elements into the result array in the order of the count array to obtain the sorted result `[1, 2, 2, 3, 3, 4, 8]`. **Implementation Key Points**: In C++ code, first find the maximum value, count the occurrences, construct the result array, and copy it back to the original array. Key steps include initializing the count array, counting occurrences, and filling the result array according to the counts. **Complexity**: Time complexity is O(n + k) (where n is the array length and k is the data range), and space complexity is O(k). **Applicable Scenarios**: Non-negative integers with a small range, requiring efficient sorting; negative numbers can be handled by offset conversion (e.g., adding the minimum value). Counting Sort achieves linear-time sorting through the "counting-construction" logic and is ideal for processing small-range integers.
Read MoreImplementing the Merge Sort Algorithm in C++
Merge sort is based on the divide-and-conquer principle, with the core being "divide-merge": first recursively split the array into individual elements (where subarrays are ordered), then merge two ordered subarrays into a larger ordered array. **Divide process**: Recursively split the array from the middle until each subarray contains only one element. **Merge process**: Compare elements from two ordered subarrays, take the smaller value and place it in the result array sequentially, then handle the remaining elements. The C++ implementation includes two core functions: `mergeSort` for recursively dividing the array, and `merge` for merging two ordered subarrays. The time complexity is O(n log n), and the space complexity is O(n) (due to the need for a temporary array). Merge sort is stable and efficient, making it suitable for sorting large-scale data. In the example, the array `[5,3,8,6,2,7,1,4]` is sorted into the ordered array `[1,2,3,4,5,6,7,8]` through division and merging, verifying the algorithm's correctness.
Read MoreImplementing the Heap Sort Algorithm in C++
Heap sort is an efficient sorting algorithm based on the heap data structure, with a time complexity of O(n log n) and a space complexity of O(1), making it suitable for large-scale data. A heap is a special complete binary tree, divided into max heaps (parent ≥ children) and min heaps, with max heaps commonly used in sorting. It is stored in an array where the parent of index i is (i-1)/2, and the left and right children are 2i+1 and 2i+2, respectively. The core steps are: 1. Constructing the initial max heap (adjusting from the last non-leaf node upwards); 2. Sorting (swapping the top element with the end of the unsorted part, adjusting the heap, and repeating until completion). The C++ implementation includes swap, max_heapify (iteratively adjusting the subtree to form a max heap), and heap_sort (constructing the heap and performing sorting) functions. The main function tests array sorting, and the output result is correct.
Read MoreImplementing the Selection Sort Algorithm in C++
Selection sort is a simple and intuitive sorting algorithm. Its core idea is to repeatedly select the smallest (or largest) element from the unsorted elements and place it at the end of the sorted sequence until all elements are sorted. The basic steps are as follows: the outer loop controls the current starting position of the unsorted elements; the inner loop finds the minimum value among the remaining elements; the swap operation moves the minimum value to the current starting position; this process repeats until all elements are sorted. Taking the array {64, 25, 12, 22, 11} as an example, the process is demonstrated: when i=0, the minimum value 11 is found and swapped to the first position; when i=1, 12 is found and swapped to the second position; when i=2, 22 is found and swapped to the third position; no swap is needed when i=3, and the array is finally sorted. The C++ code is implemented with two nested loops: the outer loop controls the position i, the inner loop finds the index minIndex of the minimum value, and swaps arr[i] with arr[minIndex]. The time complexity is O(n²) and the space complexity is O(1). Selection sort is easy to implement and requires no additional space. It is suitable for sorting small-scale data and serves as a foundational example for understanding sorting algorithms.
Read MoreImplementing the Shell Sort Algorithm in C++
Shell Sort is an improved version of Insertion Sort, also known as "diminishing increment sort". It efficiently sorts arrays by performing insertion sorts on grouped subsequences and gradually reducing the increment. The basic idea is: select an initial increment `gap` (e.g., half the array length), group elements with intervals of `gap` (forming subsequences), perform insertion sort on each group; repeat by reducing `gap` (usually halving it) until `gap=1` to complete the overall sorting. Core principle: Larger `gap` reduces the number of moves by grouping, while smaller `gap` leaves the array partially sorted, significantly lowering the total number of moves in the final insertion sort. For instance, take the array `[12, 34, 54, 2, 3]` – after initial `gap=2` grouping and sorting, the array becomes more ordered, and then `gap=1` completes the final sort. The code implements Shell Sort with three nested loops: the outer loop controls the `gap`, the middle loop iterates through each group, and the inner loop shifts elements. The average time complexity is `O(n^1.3)` (dependent on the increment), with the worst-case `O(n²)`, and a space complexity of `O(1)`. It is unstable. By optimizing insertion sort through grouping, Shell Sort is suitable for larger arrays. Its core logic lies in "grouping → sorting → reducing increment → final sorting".
Read MoreImplementing the Insertion Sort Algorithm in C++
Insertion sort is a simple and intuitive sorting algorithm whose core idea is to insert elements one by one into their appropriate positions in a sorted subarray (similar to sorting playing cards). The basic approach is: starting from the second element, take the current element, compare it with the previously sorted elements. If a previous element is larger, shift it backward until the insertion position is found. Insert the current element there and continue processing the next element. When implementing, an outer loop iterates through the elements, and an inner loop uses a temporary variable to save the current element. By comparing and shifting the previous elements to make space, the current element is finally inserted. The time complexity is O(n²) in the worst case and O(n) in the best case, with a space complexity of O(1). It is suitable for small-scale data or nearly sorted data. Its advantages include stability and simplicity, making it a foundation for understanding more complex sorting algorithms.
Read MoreImplementing the QuickSort Algorithm in C++
QuickSort is based on the divide-and-conquer method with an average time complexity of O(n log n), widely used in practical applications. Its core idea involves selecting a pivot element, partitioning the array into two parts (elements less than and greater than the pivot), and then recursively sorting the subarrays. The Lomuto partition scheme is adopted, where the rightmost element serves as the pivot. By traversing the array, elements smaller than the pivot are moved to the left, and finally, the pivot is swapped to its correct position (at index i+1). The C++ implementation includes a partition function and a recursive main function (quickSort). The partitioning operation is performed on the original array to achieve in-place sorting. The recursion terminates when the subarray length is ≤1 (i.e., left ≥ right). The average time complexity is O(n log n), while the worst-case complexity is O(n²) (e.g., when sorting an already sorted array with the leftmost/rightmost element as the pivot). This can be optimized by randomly selecting the pivot. Key features: in-place sorting with no additional space required, clear recursion termination conditions, average efficiency, and optimizable worst-case performance. QuickSort is a frequently tested and used algorithm in interviews and development. Mastering its partitioning logic and recursive thinking is crucial for understanding efficient sorting algorithms.
Read MoreImplementing the Bubble Sort Algorithm in C++
Bubble Sort is a classic introductory sorting algorithm. Its core idea is similar to bubbles rising: by repeatedly comparing adjacent elements and swapping out-of-order pairs, smaller elements gradually "bubble" to the top of the array. The basic process is as follows: in each round, start from the first element and compare adjacent elements, swapping them if they are out of order. Each round determines the position of one maximum element, and this continues until the array is sorted. In C++ implementation, the `bubbleSort` function uses an outer loop to control the number of rounds (at most n-1 rounds). The inner loop compares adjacent elements and swaps them, with a `swapped` flag for optimization—if no swaps occur in a round, the algorithm exits early. The time complexity is O(n²) in the worst and average cases, O(n) in the best case (after optimization), with a space complexity of O(1). It is a stable sorting algorithm. Despite its low efficiency, Bubble Sort is intuitive and easy to understand. Mastering the "compare and swap" logic is key to learning the basics of sorting, making it suitable for algorithmic introductory practice.
Read MoreImplementing the Radix Sort Algorithm with Python
Radix sort is a non-comparative integer sorting algorithm. Its core idea is to distribute elements into buckets and collect them by each digit (from the least significant to the most significant). The basic steps are as follows: first, determine the number of digits of the maximum number in the array. Then, from the least significant digit to the most significant digit, perform "bucket distribution" (10 buckets for digits 0-9) and "collection" operations for each digit. Elements with the same current digit are placed into the same bucket, and the array is updated by collecting them in bucket order until all digits are processed. In Python, this is implemented by looping through the digits, calculating the current digit to distribute into buckets, and then collecting. The time complexity is O(d×(n+k)) (where d is the number of digits of the maximum number, n is the array length, and k=10), and the space complexity is O(n+k). It is suitable for integer arrays with few digits. When handling negative numbers, they can first be converted to positive numbers for sorting and then their signs can be restored.
Read MoreImplementing Bucket Sort Algorithm with Python
Bucket sort is a non-comparison-based sorting algorithm based on the divide-and-conquer principle. It achieves overall order by dividing data into buckets, sorting elements within each bucket, and then merging the results. Core steps: First, divide data into buckets based on distribution characteristics; then sort elements in each bucket using simple sorting methods (e.g., built-in sort); finally, merge all bucket results. Applicable scenarios: When data is uniformly distributed and within a limited range, its efficiency approaches linear time complexity (O(n)). However, non-uniform distribution may degrade it to O(n²), making it less performant than quicksort. Python implementation (taking 0-1 interval floating-point numbers as an example): Create n empty buckets (where n is the length of the data), assign data to corresponding buckets using the formula `int(num * n)`, sort elements within each bucket, and merge all bucket elements. The code is concise but requires adjusting the bucket index calculation according to the data range and optimizing bucket size to avoid extreme value concentration. Summary: Bucket sort is suitable for uniformly distributed data. It leverages divide-and-conquer to reduce complexity but requires attention to data distribution characteristics to avoid performance degradation.
Read MoreImplementing the Counting Sort Algorithm in Python
Counting sort is an efficient non-comparison sorting algorithm suitable for integers with a small value range. Its time complexity is O(n + k), where n is the number of elements and k is the data range. Core steps: 1. Determine the data range (find min and max); 2. Construct a counting array to count the occurrences of each element; 3. Output the elements of the counting array in order (outputting the number of times corresponding to the count). It is stable (relative order of duplicate elements remains unchanged), and memory usage depends on the data range. It is suitable for integer data with many duplicates or a small range (e.g., exam scores). The Python implementation completes sorting through boundary handling, counting occurrences, etc. Test cases verify its applicability to arrays with duplicate elements and negative numbers.
Read MoreImplementing the Merge Sort Algorithm with Python
Merge sort is based on the divide and conquer algorithm, with three core steps: divide (split the array into left and right subarrays until single elements), recursively sort (recursively sort each subarray), and merge (merge the ordered subarrays into a single ordered array). Taking the array [3, 1, 4, 2] as an example, after decomposition, each subarray is recursively sorted and then merged into [1, 2, 3, 4]. The Python implementation includes a merge function (to merge two ordered subarrays in sequence) and a recursive sorting function (to decompose and recursively call merge). Its characteristics: time complexity O(n log n), space complexity O(n) (requires additional storage for merge results), and it is a stable sort (the relative order of equal elements remains unchanged).
Read MoreImplementing Heap Sort Algorithm with Python
Heap Sort is an efficient sorting algorithm that leverages the heap data structure, with a stable time complexity of O(n log n) and a space complexity of O(1), making it suitable for sorting large-scale data. A heap is a complete binary tree where parent node values are either greater than or equal to (max heap) or less than or equal to (min heap) their child node values. In an array representation, the indices of a heap follow these relationships: the children of a parent node at index i are located at 2i+1 and 2i+2, while the parent of a child node at index j is at (j-1)//2. The core operations include: 1. **Heapify**: Adjust the subtree rooted at index i to be a max heap by recursively comparing child nodes and swapping values as needed. 2. **Build Max Heap**: Starting from the last non-leaf node (n//2 - 1) and moving upward, adjust all nodes to ensure the entire tree satisfies the max heap property. The sorting process involves: first building the max heap, then repeatedly swapping the root (maximum value) with the last element of the heap, followed by calling Heapify to re-adjust the remaining elements into a max heap. This results in a sorted array from smallest to largest. Heap Sort is an in-place sorting algorithm, making it well-suited for scenarios with large data volumes.
Read MoreImplementing the Selection Sort Algorithm with Python
Selection sort is a simple and intuitive sorting algorithm. Its core idea is to repeatedly select the smallest (or largest) element from the unsorted elements and place it at the end of the sorted portion until the entire array is sorted. The steps are as follows: initially, assume the current element is the smallest, traverse the unsorted portion to find a smaller element, swap it to the end of the sorted portion, and repeat until completion. In Python implementation, the outer loop variable `i` controls the end of the sorted portion (ranging from 0 to n-2). The inner loop variable `j` traverses the unsorted portion (from i+1 to n-1) to find the position `min_index` of the smallest element. Finally, swap `arr[i]` with `arr[min_index]`. For the test array [64, 25, 12, 22, 11], the sorted result is [11, 12, 22, 25, 64]. It has a time complexity of O(n²), a space complexity of O(1), and is an in-place sorting algorithm. Its characteristics are: simple to understand, but unstable (the order of identical elements may be swapped), and suitable for small-scale data.
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