Bidders have the option of setting a maximum bid amount allowing the auction software system to automatically bid on their behalf to the next highest bid increment.Ĥ.
These notifications will not be sent out during the final two hours of the auction sale.ģ. Bidders may also choose to receive e-mail notifications when Outbid on an item. Throughout the auction process Bidders are encouraged to check their bids often in order to ensure success. YOUR CREDIT CARD WILL NOT BE CHARGED UNLESS YOU ARE THE SUCCESSFUL BIDDER.Ģ. All Bidders are required to provide valid credit card information in order to register for this auction. All Bidders must first complete the New Bidder Information form as provided. Upon registration, the Bidder warrants that they are of legal age and have read and understand the terms and conditions as listed and that such terms shall govern this auction sale.ġ. Any items not picked up in the times allotted will be forfeited with no refund and will become the property of Mariner Auctions & Liquidations Ltd. We cannot store items or offer additional pickup times as we need the time and space to prepare for major upcoming auctions. ***BE WARNED*****PLEASE READ****** Do not bid if you cannot pick up at the times allotted.
#Lookee sleep monitor how to#
If you have ideas about how to improve this project in any way, whether giving suggestions about the graph, or about how to better track the movements and so on, feel free to share it. The actual dataset is trained only on the images of my head (i took images of the 4 different sides left, right, front and back), but in order to work with every person we need a bigger dataset with images of different people. You can contribute to the project in different ways, whether you’re a developer or not, by: The project is opensource and available on Github on this link: Head_distance_hours = Īx2.plot(head_distance_hours, head_distance_movement) This project has many areas for potential future development and if you find it interesting you can find the project on GitHub and are able to contribute! # Plot sleeping information When we plot this distance moved (y-axis) over time (x-axis), we can see the spikes in movement during certain sleeping hours. We also added the distance moved, to an array. Viewing the first graph below we can see, my head predominantly stayed in the right and left position (cheek against pillow).įor the second graph, we determined where the center of the head was in each frame and tracked the movement of that center point throughout the night. We will use the classes (which were used for labeling the rectangles) and head_position. We first plot the information concerning how many frames the head was in each position, into a bar graph.
How well did you sleep? Plot Data on Graphs We can determine the distance by finding the hypotenuse. For example, if the position of the center moves from 50,50 to 40,40 the point moved 10 pixels over and 10 pixels down. This center point will be marked with a circle. To track the actual distance of head movement, we need to determine the center of the head in each frame. Then we can also use the model to see each head position rectangle as a different color during review of the video. The class named class_id, assigns an index number to each side of the head: The model will draw the rectangle around the head.Ĭv2.putText(frame, classes, (x, y - 15), 0, 1.3, hp.colors, 3)Ĭv2.rectangle(frame, (x, y), (x + w, y + h), hp.colors, 3)Ĭv2.circle(frame, center, 5, hp.colors, 3)Īgain this model will be looking to identify which of the 4 sides of the head, right, left, back, or front, is against the pillow. The box is the rectangle around the head, with 4 corners: x, y, w, h. If the head is detected, we want to extract that information. Ret, class_id, box, center = hp.get_head_position(frame) We include ret in this case, so when the video ends and there are no more frames, the loop will break. # Track head informationĪs in previous examples, we will use a while loop to continuously cycle through the video frames. The array for head position will tally the number of frames the head is in each position. We want to keep track of information for the head as the frames progress. import cv2Ĭap = cv2.VideoCapture("sergio_sleeping.avi")Ĭlasses = We will import this model as HeadPosition. YOLO is a real-time object detection system. I have created a Deep Learning model for determining the position of the head using YOLO.ĭeep learning is a form of AI, allowing a program to learn for itself by mimicking a brain (neural network) to detect objects.
0 kommentar(er)
