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TehseenHasan / repository
This is the code for object detection and pick and place operation using a 4DoF DIY Robotic Arm.
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These are the files/code of my pick and place robotic arm using OpenCV-Python. These code files are not so much organized as I did not find spare time to clean the code or write a good documentation/tutorial. I will try my best to wrate a complete guide and documentation about that complete system.
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apockill /
2015 - This is the code for my checkers playing robot arm! It uses openCV, python, and a uArm Metal with a camera attached to the end of the arm, pointing at the game board.
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54/100 healthcelerometis /
this poject is the code of 2nd place naj_uav team in drone emergency service- indoor leage fira roboworldcup 2019 tehran- iran
daniel-s-ingram /
Using just a webcam, this program allows the uArm Metal to mirror the movement of your hand. The state of the pump is also controlled by the closing and opening of your hand.
31/100 healthunderthehooditsme /
This repository contains the codes and all necessary files to make a robot that is wirelessly controlled by Iris movement
31/100 healthBhavana1480 /
for a robot to visualize the environment, along with the object detection, detection of its color in real-time is also very important. Why this is important? : Some Real-world Applications In self-driving car, to detect the traffic signals. Multiple color detection is used in some industrial robots, to performing pick-and-place task in separating different colored objects. This is an implementation of detecting multiple colors (here, only red, green and blue colors have been considered) in real-time using Python programming language. Python Libraries Used: NumPy OpenCV-Python Work Flow Description: Step 1: Input: Capture video through webcam. Step 2: Read the video stream in image frames. Step 3: Convert the imageFrame in BGR(RGB color space represented as three matrices of red, green and blue with integer values from 0 to 255) to HSV(hue-saturation-value) color space. Hue describes a color in terms of saturation , represents the amount of gray color in that color and value describes the brightness or intensity of the color. This can be represented as three matrices in the range of 0-179, 0-255 and 0-255 respectively. Step 4: Define the range of each color and create the corresponding mask. Step 5: Morphological Transform: Dilation, to remove noises from the images. Step 6: bitwise_and between the image frame and mask is performed to specificaly detect that particular color and discrad others. Step 7: Create contour for the individual colors to display the detected colored region distinguishly. Step 8: Output: Detection of the colors in real-time.
39/100 health