The Python OpenCV Cheat Sheet is your complete guide to mastering computer vision and image processing using Python. It’s designed to be your trusty companion, helping you quickly understand the important ideas, functions, and techniques in the OpenCV library. Whether you’re an experienced developer needing a quick reminder or a newcomer excited to start, this cheat sheet has got you covered.
In this article, we’ve gathered all the vital OpenCV concepts and explained them in simple terms. We’ve also provided practical examples to make things even clearer. You’ll learn everything from how to handle images to using advanced filters, spotting objects, and even exploring facial recognition. It’s all here to help you on your journey of discovering the amazing world of computer vision.
Python OpenCV Cheat Sheet 2023
Presenting the fully updated cheat sheet for mastering OpenCV and its array of commands
Core Operations
| imread(image_path, flag) |
This method is used to read an image from its path |
| imshow(window_name, image) |
It is used to show the image in the window. |
| imwrite(filename, image) |
This method is used to write or save an image using OpenCV. |
Reading Images
You can read an image in Python using OpenCV’s cv.2imread() method. By default, OpenCV stores colored images in BGR(Blue, Green, and Red) format.
img = cv2.imread(image_path, flag)
The types of flags are described below:
| cv2.IMREAD_COLOR |
Read colored images |
| cv2.IMREAD_GRAYSCALE |
Read Grayscale images |
| cv2.IMREAD_UNCHANGED |
Read images with alpha channel |
Showing Images
The cv2.imshow() method is used to display an image in a window.
cv2.imshow(window_name, image)
Image Cropping
Cropping is the removal of unwanted outer areas from an image.
cropped_img = img[100:300, 100:300]
Saving an Image
The cv2.imwrite() method is used to save an image to any storage device. This will save the image according to the specified format in the current working directory.
cv2.imwrite(filename, image)
Drawing Shapes and Text on Images
OpenCV provides the following drawing functions to draw geometric shapes on images. It also provides functions to write text on images.
| line(image, start_coordinates, end_coordinates, color_in_bgr, line_thickness) |
By using this function we can create a line in the image from start coordinates to end coordinates with a certain thickness which can be mentioned specifically as well. |
|
rectangle(image,top_left_vertex_coordinates, lower_right_vertex_coordinates, color_in_bgr, thickness)
|
This function is used to create a box with a certain thickness and color which can be specified as well. |
| circle(image, center_coordinates, radius, color, thickness) |
It is used to draw a circle whose centre and radius length is given with a certain thickness and the colour of the strokes of the circle. |
| polylines(image, [pts], isClosed, color, thickness) |
It is used to draw a polygon on any image whose vertex coordinates are provided. |
|
putText(image, ‘TextContent’, ‘text_starting_point_coordinates’,
‘fontToBeUsed’, ‘font_size’, ‘text_color’, ‘text_thickness’, ‘line_type’)
|
It is used to write some text on the image loaded. |
Draw Line
The cv2.line() method is used to draw the line on an image.
cv2.line(image, (start_coordinates), (end_coordinates), (color_in_bgr),
line_thickness)
Draw Rectangle
The cv2.rectangle() method is used to draw a rectangle on an image.
cv2.rectangle(image, (‘top_left_vertex_coordinates’),
(‘lower_right_vertex_coordinates’),
(‘stroke_color_in_bgr’), ‘stroke_thickness’)
Draw Circle
The cv2.circle() method is used to draw a circle on an image.
cv2.circle(image, (‘center_coordinates’), (‘circle_radius’),
(‘color_in_bgr’), ‘stroke_thickness’)
Draw Polygon
The cv2.polylines() method is used to draw a polygon on any image.
cv2.polylines(image, [pts], isClosed, color, thickness)
Writing Text
The cv2.putText() method is used to write some text on an image.
cv2.putText(image, ‘TextContent’, (‘text_starting_point_coordinates’),
‘fontToBeUsed’, ‘font_size’, (‘text_color’, ‘text_thickness’, ‘line_type’)
Arithmetic Operations on Images
Arithmetic Operations like Addition, Subtraction, and Bitwise Operations(AND, OR, NOT, XOR) can be applied to the input images.
| add(image1, image2) |
This function is used to add two images. |
| subtract(image1, image2) |
This function is used to subtract two images. |
| addWeighted(image1, weight1, image2, weight2, gammaValue) |
This is also known as Alpha Blending. This is nothing but a weighted blending process of two images. |
| bitwise_and(image1, image2, destination, mask) |
This performs bitwise and logical operations between two images. |
| bitwise_or(image1, image2, destination, mask) |
This performs bitwise or logical operations between two images. |
| bitwise_not(image, destination, mask) |
This performs bitwise not logical operations between an image and a mask. |
| bitwise_xor(image1, image2, destination, mask) |
This performs bitwise xor logical operations between two images. |
Image Addition
We can add two images by using the cv2.add() method. This directly adds up image pixels in the two images.
cv2.add(image1, image2)
Image Alpha Blending
Alpha blending is the process of overlaying a foreground image on a background image which can be done using the addWeighted() method.
cv2.addWeighted(image1, weight1, image2, weight2, gammaValue)
Image Subtraction
We can subtract the pixel values in two images and merge them with the help of cv2.subtract(). The images should be of equal size and depth.
cv2.subtract(image1, image2)
Bitwise And
Bit-wise conjunction of input array elements.
cv2.bitwise_and(image1, image2, destination, mask)
Bitwise Or
Bit-wise disjunction of input array elements.
cv2.bitwise_or(image1, image2, destination, mask)
Bitwise Not
Inversion of input array elements.
cv2.bitwise_not(image, destination, mask)
Bitwise Xor
Bit-wise exclusive-OR operation on input array elements.
cv2.bitwise_xor(image1, image2, destination, mask)
Morphological Operations on Images
Python OpenCV Morphological operations are one of the Image processing techniques that process images based on shape. This processing strategy is usually performed on binary images.
| erode(image, kernel, iterations=1) |
Erosion primarily involves eroding the outer surface (the foreground) of the image. As binary images only contain two pixels 0 and 255, it primarily involves eroding the foreground of the image and it is suggested to have the foreground as white. |
| dilate(image, kernel, iterations=1) |
Dilation involves dilating the outer surface (the foreground) of the image. As binary images only contain two pixels 0 and 255, it primarily involves expanding the foreground of the image and it is suggested to have the foreground as white. |
| morphologyEx(image, Morphology_method, kernel, iterations=1) |
Some of the morphology methods are shown below:
- cv2.MORPH_GRADIENT
- cv2.MORPH_CLOSE
- cv2.MORPH_OPEN
- cv2.MORPH_TOPHAT
- cv2.MORPH_BLACKHAT
|
Erosion
Erosion primarily involves eroding the outer surface (the foreground) of the image. As binary images only contain two pixels 0 and 255, it primarily involves eroding the foreground of the image and it is suggested to have the foreground as white.
cv2.erode(image, kernel, iterations=1)
Dilation
Dilation involves dilating the outer surface (the foreground) of the image. As binary images only contain two pixels 0 and 255, it primarily involves expanding the foreground of the image and it is suggested to have the foreground as white.
cv2.dilate(image, kernel, iterations=1)
Opening
The opening involves erosion followed by dilation in the outer surface (the foreground) of the image. It is generally used to remove the noise in the image.
cv2.morphologyEx(image, cv2.MORPH_OPEN, kernel, iterations=1)
Closing
Closing involves dilation followed by erosion in the outer surface (the foreground) of the image. It is generally used to remove the noise in the image.
cv2.morphologyEx(image, cv2.MORPH_CLOSE, kernel, iterations=1)
Morphological Gradient
The morphological gradient first applies erosion and dilation individually on the image and then computes the difference between the eroded and dilated image. The output will be an outline of the given image.
cv2.morphologyEx(image, cv2.MORPH_GRADIENT, kernel)
Top Hat
Top Hat is yet another morphological operation where an Opening is performed on the binary image and the output of this operation is a difference between the input image and the opened image.
cv2.morphologyEx(image, cv2.MORPH_TOPHAT, kernel)
Black Hat
The Black Hat enhances dark objects of interest on a bright background. The output of this operation is the difference between the closing of the input image and the input image.
cv2.morphologyEx(image, cv2.MORPH_BLACKHAT, kernel)
Image Transformation involves the transformation of images, such as scaling, rotating, cropping, etc for further usage.
| resize(image, width, height, interpolation_method) |
By using this method we can resize the image at hand for specific width and height.
Some of the methods that can be used for interpolation are as below:
- cv2.INTER_AREA
- cv2.INTER_CUBIC
- cv2.INTER_LINEAR
|
|
M = cv2.getRotationMatrix2D(center, angle, scale)
warpAffine(image, M, (width, height))
|
Image rotation is a common image processing routine with applications in matching, alignment, and other image-based algorithms, in image rotation the image is rotated by a definite angle. We can also use cv2.rotate() function for image rotation. |
| matrix = cv2.getPerspectiveTransform(src, dst) warpPerspective(image, matrix, dsize) |
In Perspective Transformation, we can change the perspective of a given image or video for getting better insights into the required information. We need to provide the points on the image from which want to gather information by changing the perspective. We also need to provide the points inside which we want to display our image. |
Scaling
Image scaling is a process used to resize a digital image using the cv2.resize() method.
res = cv2.resize(img,(2width, 2height), interpolation)
The resize() method’s Interpolation attribute has the following types: (Scaling types)
| cv2.INTER_AREA |
Used to shrink the image |
| cv2.INTER_CUBIC |
Bicubic interpolation |
| cv2.INTER_LINEAR |
Default interpolation technique used to zoom the image |
Translation
Translation refers to the rectilinear shift of an object i.e. an image from one location to another. If we know the amount of shift in the horizontal and vertical direction, then we can make a transformation matrix. Then, we can use the cv2.wrapAffine() function to implement these translations. This function requires a 2×3 array. The numpy array should be of float type.
T = np.float32([[1, 0, new_width], [0, 1, new_height]])
cv2.warpAffine(image, T, (original_width, original_height))
Rotation
Image rotation is a common image processing routine with applications in matching, alignment, and other image-based algorithms, in image rotation the image is rotated by a definite angle. We can also use cv2.rotate() function for image rotation.
M = cv2.getRotationMatrix2D(center, angle, scale)
cv2.warpAffine(image, M, (width, height))
Affline Transformation
In Affine transformation, all parallel lines in the original image will still be parallel in the output image. This can be done by using the cv2.getAfflineTransform() method.
M = cv2.getAffineTransform(pts1, pts2)
dst = cv2.warpAffine(img, M, (cols, rows))
Perspective Transformation
In Perspective Transformation, we can change the perspective of a given image or video to get better insights into the required information. We need to provide the points on the image from which want to gather information by changing the perspective. We also need to provide the points inside which we want to display our image.
matrix = cv2.getPerspectiveTransform(src, dst)
cv2.warpPerspective(image, matrix, dsize)
Image Thresholding
Thresholding is a technique in OpenCV, which is the assignment of pixel values about the threshold value provided. In thresholding, each pixel value is compared with the threshold value. If the pixel value is smaller than the threshold, it is set to 0, otherwise, it is set to a maximum value (generally 255).
| cv2.threshold(source, thresholdValue, maxVal, thresholdingTechnique) |
For every pixel, the same threshold value is applied. If the pixel value is smaller than the threshold, it is set to 0, otherwise, it is set to a maximum value. This can be achieved using the cv2.threshold() method.
|
| cv2.adaptiveThreshold(source, maxVal, adaptiveMethod, thresholdType, blocksize, constant) |
Adaptive thresholding is the method where the threshold value is calculated for smaller regions. This leads to different threshold values for different regions concerning the change in lighting. |
| cv2.threshold(source, thresholdValue, maxVal, thresholdingTechnique) |
In Otsu Thresholding, a value of the threshold isn’t chosen but is determined automatically. A bimodal image (two distinct image values) is considered. The histogram generated contains two peaks. |
Thresholding Techniques:
| If pixel intensity is greater than the set threshold, the value is set to 255, else set to 0 |
| Inverted or Opposite case of cv2.THRESH_BINARY |
| If the pixel intensity value is greater than the threshold, it is truncated to the threshold. The pixel values are set to be the same as the threshold |
| Pixel intensity is set to 0, for all the pixels’ intensity, less than the threshold value |
| Inverted or Opposite case of cv2.THRESH_TOZERO |
Simple Threshold
For every pixel, the same threshold value is applied. If the pixel value is smaller than the threshold, it is set to 0, otherwise, it is set to a maximum value. This can be achieved using the cv2.threshold() method.
cv2.threshold(source, thresholdValue, maxVal, thresholdingTechnique)
Adaptive Thresholding
Adaptive thresholding is the method where the threshold value is calculated for smaller regions. This leads to different threshold values for different regions with respect to the change in lighting.
cv2.adaptiveThreshold(source, maxVal, adaptiveMethod, thresholdType, blocksize, constant)
Otsu Thresholding
In Otsu Thresholding, a value of the threshold isn’t chosen but is determined automatically. A bimodal image (two distinct image values) is considered. The histogram generated contains two peaks. So, a generic condition would be to choose a threshold value that lies in the middle of both the histogram peak values.
cv2.threshold(source, thresholdValue, maxVal, thresholdingTechnique)
Edge/Line Detection (Features)
| cv2.Canny(image, T_lower, T_upper, aperture_size, L2Gradient) |
The Canny Edge Detection is an algorithm used for edge detection. It reduces noise using Gaussian Smoothing and computes image gradient using The Sobel filter. |
| cv2.HoughLines(edges, 1, np.pi/180, 200) |
The Hough Transform is a method that is used in image processing to detect any shape if that shape can be represented in mathematical form. It can detect the shape even if it is broken or distorted a little bit. |
| cv2.HoughCircles(image, cv2.HOUGH_GRADIENT, 1, 20, param1 = 50, param2 = 30, minRadius = 1, maxRadius = 40) |
Circle detection finds a variety of uses in biomedical applications, ranging from iris detection to white blood cell segmentation. |
| cv2.cornerHarris(src, dest, blockSize, kSize, freeParameter, borderType) |
Harris Corner detection algorithm was developed to identify the internal corners of an image. The corners of an image are basically identified as the regions in which there are variations in the large intensity of the gradient in all possible dimensions and directions. |
| cv2.goodFeaturesToTrack(image, max_corner, quantity_level, min_distance) |
The cv2.goodFeaturesToTrack() function in OpenCV is used for corner detection. |
| cv2.drawKeypoints(input_image, key_points, output_image, colour, flag) |
The distinguishing qualities of an image that make it stand out are referred to as key points in an image. The key points of a particular image let us recognize objects and compare images. This can be done by using the cv2.drawKeypoints() method. |
Canny Edge Detection
The Canny Edge Detection is an algorithm used for edge detection. It reduces noise using Gaussian Smoothing and computes image gradient using The Sobel filter. Finally, apply Hysteresis thresholding which that 2 threshold values T_upper and T_lower which are used in the Canny() function.
cv2.Canny(image, T_lower, T_upper, aperture_size, L2Gradient)
Houghline Method for Line Detection
The Hough Transform is a method that is used in image processing to detect any shape if that shape can be represented in mathematical form. It can detect the shape even if it is broken or distorted a little bit.
cv2.HoughLines(edges, 1, np.pi/180, 200)
Houghline Method for Circle Detection
Circle detection finds a variety of uses in biomedical applications, ranging from iris detection to white blood cell segmentation.
cv2.HoughCircles(image, cv2.HOUGH_GRADIENT, 1, 20, param1 = 50,
param2 = 30, minRadius = 1, maxRadius = 40)
Harris Corner Method for Corner Detection
Harris Corner detection algorithm was developed to identify the internal corners of an image. The corners of an image are basically identified as the regions in which there are variations in the large intensity of the gradient in all possible dimensions and directions.
cv2.cornerHarris(src, dest, blockSize, kSize, freeParameter, borderType)
Shi-Tomasi Method for Corner Detection
In Shi-Tomasi Method, the basic intuition is that corners can be detected by looking for significant changes in all directions. We consider a small window on the image and then scan the whole image, looking for corners. Shifting this small window in any direction would result in a large change in appearance if that particular window happens to be located in a corner.
cv2.goodFeaturesToTrack(imgage, max_corner, quantity_level, min_distance)
Keypoints Detection
The distinguishing qualities of an image that make it stand out are referred to as key points in an image. The key points of a particular image let us recognize objects and compare images. This can be done by using the cv2.drawKeypoints() method.
cv2.drawKeypoints(input_image, key_points, output_image, colour, flag)
Other Detection Techniques
OpenCV module provides functions and classes for the detection of various objects. We can detect a circle or ellipses in an image using the SimpleBlobDetector() function, which is basically used to detect any shape in which the pixels are connected.
Another detection technique used is template matching. It is an image processing technique that is used to find the location of small parts/templates of a large image. This technique is widely used for object detection projects, like product quality, vehicle tracking, robotics, etc. This technique can also be used in matching for detecting document fields in a document image.
We can also use OpenCV to detect emotions. Emotion detectors are used in many industries, one being the media industry where it is important for the companies to determine the public reaction to their products. Smile detectors can also work with a live feed from webcams.
Other than that, OpenCV’s Sobel edge detection method can also be used to detect the edges of an image in a live stream video. Edge detection involves mathematical methods to find points in an image where the brightness of pixel intensities changes distinctly, which requires finding the gradient of the grayscale image. It also makes use of the Laplacian() function.
Image Pyramids
Image Pyramids are used to change the resolution of the images.
Lower Gaussian Pyramid
The pyrDown() function decreases the size of an image to half of the original image.
cv2.pyrDown(layer)
Higher Gaussian Pyramid
The pyrUp() function increases the size of an image to double its original size.
cv2.pyrUp(layer)
Changing the Colorspace of Images
We can change the colorspace of images using OpenCV. Let’s discuss different ways to visualize images where we will represent images in different formats like grayscale, RGB scale, Hot_map, edge map, Spectral map, etc.
| cv2.cvtColor(image, conversion_scale) |
Some of the commonly used ways in which color coding of the images are changed is as shown below:
- cv2.COLOR_BGR2GRAY
- cv2.COLOR_BGR2HSV
- cv2.COLOR_BGR2LAB
- cv2.COLOR_BGR2YCrCb
|
|
hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)
lower_blue = np.array([110,50,50])
upper_blue = np.array([130,255,255])
cv2.inRange(hsv, lower_blue, upper_blue)
|
OpenCV provides functions for the
detection of a specific color
in live-stream video content. A video is composed of infinite frames at different time instants.
|
| green = np.uint8([[[0, 255, 0]]]) hsv_green = cv2.cvtColor(green, cv2.COLOR_BGR2HSV) |
OpenCV lets you find out the HSV color code from the RGB color code. |
Convert to Gray
Grayscale image contains only a single channel.
cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
Convert to HSV
Hue(H) represents the dominant wavelength. Saturation(S) represents shades of color. Value(V) represents Intensity.
cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
Convert to LAB color
L represents Lightness. A represents color components ranging from Green to Magenta. B represents color components ranging from Blue to Yellow.
cv2.cvtColor(img, cv2.COLOR_BGR2LAB)
Convert to YCrCb Color
Y represents Luminance or Luma component, and Cb and Cr are Chroma components. Cb represents the blue difference (difference between the blue component and Luma Component). Cr represents the red difference (difference between the red component and Luma Component).
cv2.cvtColor(img, cv2.COLOR_BGR2YCrCb)
Track Blue (color) Object
OpenCV provides functions for the detection of a specific color in live-stream video content. A video is composed of infinite frames at different time instants.
hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)
lower_blue = np.array([110,50,50])
upper_blue = np.array([130,255,255])
cv2.inRange(hsv, lower_blue, upper_blue)
Find HSV Color
OpenCV lets you find out the HSV color code from the RGB color code.
green = np.uint8([[[0, 255, 0]]])
hsv_green = cv2.cvtColor(green, cv2.COLOR_BGR2HSV)
Smoothing Images
When we are dealing with images at some points the images will be crisper and sharper which we need to smoothen or blur to get a clean image, or sometimes the image will be with a really bad edge which also we need to smooth down to make the image usable. In OpenCV, we got more than one method to smooth or blur an image.
| cv2.filter2D(image, ddepth, kernel) |
Using the cv2.filter2D() method we can smoothen an image with a custom-made kernel with an image to achieve different image filters like sharpening and blurring and more. |
| cv2.blur(image, shapeOfTheKernel) |
The cv2.blur() method is used to blur an image using the normalized box filter. The function smooths an image using the kernel. |
| cv2.getGaussianKernel(ksize, sigma[, ktype]) |
The cv2.getGaussianKernel() method is used to find the Gaussian filter coefficients. The Gaussian kernel is also used in Gaussian Blurring. The ‘ktype’ is the type of filter coefficient. It can be CV_32F or CV_64F. |
| cv2.GaussianBlur(image, shapeOfTheKernel, sigmaX ) |
In a Gaussian blur we are going to use a weighted mean. In this type of kernel, the values near the center pixel will have a higher weight. The sigmaX is the Gaussian kernel standard deviation which is by default set to 0. |
| cv2.medianBlur(image, kernel size) |
In the cv2.medianBlur() method of smoothing, we will simply take the median of all the pixels inside the kernel window and replace the center value with this value. |
| cv2.bilateralFilter(image, diameter, sigmaColor, sigmaSpace) |
The Bilateral Blur method concerns more about the edges and smoothens the image by preserving the images. This is achieved by performing two Gaussian distributions. The SigmaColor is the number of colors to be considered in the given range of pixels and should not be very high. |
Convolve an Image
Using the cv2.filter2D() method we can smoothen an image with a custom-made kernel with an image to achieve different image filters like sharpening and blurring and more.
cv2.filter2D(image, ddepth, kernel)
Averaging Filtering
The cv2.blur() method is used to blur an image using the normalized box filter. The function smooths an image using the kernel
cv2.blur(image, shapeOfTheKernel)
Create Gaussian Kernel
The cv2.getGaussianKernel() method is used to find the Gaussian filter coefficients. The Gaussian kernel is also used in Gaussian Blurring. The ‘ktype’ is the type of filter coefficient. It can be CV_32F or CV_64F.
cv2.getGaussianKernel(ksize, sigma[, ktype])
Gaussian Blur
In a Gaussian blur, we are going to use a weighted mean. In this type of kernel, the values near the center pixel will have a higher weight. The sigmaX is the Gaussian kernel standard deviation which is by default set to 0.
cv2.GaussianBlur(image, shapeOfTheKernel, sigmaX )
Median Blur
In the cv2.medianBlur() method of smoothing, we will simply take the median of all the pixels inside the kernel window and replace the center value with this value.
cv2.medianBlur(image, kernel size)
Bilateral Blur
The Bilateral Blur method concerns more about the edges and smoothens the image by preserving the images. This is achieved by performing two Gaussian distributions. The SigmaColor is the number of colors to be considered in the given range of pixels and should not be very high. SigmaSpace is the space between the biased pixel and the neighbor pixel.
cv2.bilateralFilter(image, diameter, sigmaColor, sigmaSpace)
Working With Videos
OpenCV library can be used to perform multiple operations on videos. We can perform operations like creating a video using multiple images, extracting images, and frames, drawing shapes, putting text, etc on a video. Let us see a few of these operations.
| cv2.VideoCapture(“file_name.mp4”) |
To
capture a video
using OpenCV, we need to create a VideoCapture object. VideoCapture has the device index or the name of a video file. The device index is just the number to specify which camera. If we pass 0 then it is for the first camera, 1 for the second camera so on.
|
|
cv2.VideoCapture(File_path)
cv2.read()
cv2.imwrite(filename, img[, params])
|
Using OpenCV, techniques such as image scanning, and face recognition can be accomplished quite easily. We can
extract images from videos as well using the OpenCV module along with the os module.
|
Playing a Video
To capture a video using OpenCV, we need to create a VideoCapture object. VideoCapture has the device index or the name of a video file. The device index is just the number to specify which camera. If we pass 0 then it is for the first camera, 1 for the second camera so on. We capture the video frame by frame.
cv2.VideoCapture(“file_name.mp4”)
Create a Video from Multiple Images
We can create videos from multiple images using OpenCV. It also requires the PIL library which is used to open and resize images to their mean_height and mean_width because the video which will be created using the cv2 library required the input images of the same height and width.
PIL.Image.open(filename, mode)
Extracting Images from Video
Using OpenCV, techniques such as image scanning, and face recognition can be accomplished quite easily. We can extract images from videos as well using the OpenCV module along with the os module.
cv2.VideoCapture(File_path)
cv2.read()
cv2.imwrite(filename, img[, params])
Camera Calibration and 3D Reconstruction
A Camera Calibration is estimating the parameters of a camera, parameters about the camera are required to determine an accurate relationship between a 3D point in the real world and its corresponding 2D projection (pixel) in the image captured by that calibrated camera. We need to consider both internal parameters like focal length, optical center, and radial distortion coefficients of the lens, etc., and external parameters like rotation and translation of the camera with respect to some real-world coordinate system.
Pose estimation is a computer vision technique that is used to predict the configuration of the body(POSE) from an image. It is used in various applications such as robotics, human-computer interactions, etc.
Often in multiple-view geometry, there are interesting relationships between the multiple cameras, a 3D point, and that point’s projections in each of the camera’s image planes. The geometry that relates the cameras, points in 3D, and the corresponding observations are referred to as the Epipolar geometry of a stereo pair.
A depth map is a picture where every pixel has depth information(rather than RGB) and it is normally represented as a grayscale picture. Depth information means the distance of the surface of scene objects from a viewpoint. The Stereo Images are two images with a slight offset.
Conclusion
In Conclusion, the Python OpenCV Cheat Sheet is like a helpful guide for understanding computer vision and working with images. It’s like having a map to explore this new world! Whether you’re just starting out or already know a lot, the cheat sheet gives you a quick and organized collection of important stuff. You’ll learn from basic image stuff to more advanced things like finding objects in pictures or recognizing faces. This makes learning OpenCV easy and fun. So, as you start using Python and OpenCV for your image work, remember this cheat sheet is your buddy.
Python OpenCV Cheat Sheet – FAQs
Q1.How do people use OpenCV?
OpenCV is a powerful library for computer vision and image processing. It can be used for a wide variety of tasks, including:
- Face detection and recognition
- Object tracking
- Image segmentation
- Video analysis
- Medical imaging
- Industrial automation
- Robotics
Q2.What are the benefits of using a Python OpenCV cheat sheet?
A Python OpenCV cheat sheet can be a valuable resource for anyone who uses OpenCV. It can help you to quickly look up the syntax for different functions, as well as learn about the different features of the library.
Q3.What is a real-life example of Python OpenCV?
One real-life example of Python OpenCV is the Face Recognition API: https://github.com/ageitgey/face_recognition by Adrian Rosebrock. This API allows you to identify faces in images and videos using OpenCV.
Share your thoughts in the comments
Please Login to comment...