Identify classes and track them frame by frame.
Locate and identify objects of various classes and track them frame by frame.
Locate and identify objects of various classes and track them frame by frame.
EzappSolution delivers powerful Solutions to Optimize and Scale models you can trust while maximizing operational efficiency and reducing the cost of ML projects.Servicing Computer Vision in business applications to engage Customers for Shopping, User Experience and self driving automation.
# Opening image
img = cv2.imread("image.jpg")
# OpenCV opens images as BRG
# but we want it as RGB We'll
# also need a grayscale version
img_gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
img_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
Our DataScience team performs labeling of the Images and outlines the exact shape of the target object to annotate polygons.
EzappSolution team segments multiple types of objects in images belonging to a single class at a pixel level. Classification performed using Methods for upsampling Fully convolutional networks Advanced U-Net variants Dilated convolutions.
Object tracking experts at EzappSolution develop applications of deep learning where the program takes an initial set of object detections and develops a unique identification for each of the initial detections and then tracks the detected objects as they move around frames in a video, medical imaging, traffic flows and audience flows.
Lidar (Light Detection & Ranging) data is an essential sensor for geospatial technology, autonomous technology, and many other Business applications. Lidar annotation can be combined with image annotation to train computer vision and other deep learning models to perform a variety of tasks.
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