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I'm a beginner in opencv using python. I have many 16 bit gray scale images and need to detect the same object every time in the different images. Tried template matching in opencv python but needed to take different templates for different images which could be not desirable. Can any one suggest me any algorithm in python to do it efficiently.

Your question is way too general. Feature matching is a very vast field. The type of algorithm to be used totally depends on the object you want to detect, its environment etc.

So if your object won't change its size or angle in the image then use Template Matching.

If the image will change its size and orientation you can use SIFT or SURF.

If your object has unique color features that is different from its background, you can use hsv method.

If you have to classify a group of images as you object,for example all the cricket bats should be detected then you can train a number of positive images to tell the computer how the object looks like and negative image to tell how it doesn't, it can be done using haar training.

In this tutorial, you'll learn how to use the YOLO object detector to detect objects in both images and video streams using Deep Learning, OpenCV, and Python. By applying object detection, you'll not only be able to determine what is in an image, but also where a given object resides! Detect an object with OpenCV-Python OpenCV is the huge open-source library for computer vision, machine learning, and image processing and now it plays a major role in real-time operation which is very important in today’s systems. By using it, one can process images and videos to identify objects, faces, or even the handwriting of a human.

u can try out sliding window method. if ur object is the same in all samples

By the end of this tutorial you'll be able to apply deep learning-based object detection to real Duration: 0:56 Posted: Sep 18, 2017 So this is how object detection takes place in OpenCV, the same programs can also be run in OpenCV installed Raspberry Pi and can be used as a portable device like Smartphones having Google Lens. This article is referred from Master Computer Vision™ OpenCV4 in Python with Deep Learning course on Udemy, created by Rajeev Ratan , subscribe it to learn more about Computer Vision and Python.

One way to do this is to look for known colors, shapes, and sizes.

You could start by performing an HSV threshold on your image, by converting your image to HSV colorspace and then calling

cv2.inRange(source, (minHue, minSat, minVal), (maxHue, maxSat, maxVal))

Next, you could use cv2.findContours to find all the areas in your image that meet your color requirements. Then, you could use methods such as boundingRect and contourArea to find specific attributes of the object that you want.

What you will end up with is essentially a 'pipeline' that can take a frame, and look for a shape that fits the criteria you have set. Depending on the complexity of what you want to do (you didn't say what you're looking for), this may or may not work, but I have used it with reasonable success.

GRIP is an application that allows you to threshold things in a visual way, and it will also generate Python code for you if you want. I don't really recommend using the generated code as-is because I've run into some problems that way. Here's the link to GRIP:

To know about all the objects that can be detected using this library, you can visit the link below. arunponnusamy/object-detection-opencv. You  opencv-python cvlib matplotlib tensorflow Here is the code to import the required python libraries, read an image from storage, perform object detection on the image and display the image with a bounding box and label about the detected objects.

If the object you want to detect has different size in every image and also slightly varies in shape too, then I recommend you use HaarCascade of that object. If the object is very general then you can easily find haar cascade for it online. Otherwise it is not very difficult to make haar cascades(can be a littile time consuming though). You can use this tutorial by sentdex to make HaarCascade here.

Or If you want to know how to use HaarCascades then you can get it on this link here.

Object detection refers to the capability of computer and software systems OpenCV pip3 install opencv-python. iii. Keras pip3 install keras. iv. As you can see from the example image, with very little Python code, I got good OpenCV object detection. The third line of the above Python code reveals how I can pull useful data about the detected object. Furthermore, I can see how this data is being used to draw a bounding box around the detected object.

Object Detection¶. Face Detection using Haar Cascades · objdet_1, Face detection using haar-cascades. Next Previous. © Copyright 2013, Alexander  If we combine both the MobileNet architecture and the Single Shot Detector (SSD) framework, we arrive at a fast, efficient deep learning-based method to object detection. The model we’ll be using in this blog post is a Caffe version of the original TensorFlow implementation by Howard et al. and was trained by chuanqi305 ( see GitHub ).

Tutorial and source code:​using-opencv Duration: 36:56 Posted: Jun 27, 2019 #include <opencv2/imgproc.hpp> Compares a template against overlapped image regions. The function slides through image , compares the overlapped patches of size \(w \times h\) against templ using the specified method and stores the comparison results in result . Here are the formulae

In this video on OpenCV Python Tutorial For Beginners, I am going to show How to do Object Duration: 19:52 Posted: Mar 31, 2019 In this post, we will learn how to use YOLOv3 — a state of the art object detector — with OpenCV. YOLOv3 is the latest variant of a popular object detection algorithm YOLO – You Only Look Once. The published model recognizes 80 different objects in images and videos, but most importantly it is super […]

  • please see: Is there a less restrictive Stack Exchange site specially suited for not too specific questions?
  • are you still looking for an answer?
  • I would suggest Gaussian pyramiding
  • try hsv masking