![]() ![]() ![]() There’s no time to go down a rabbit hole. Second, we have to become masters of timeboxing. There are people out there who have dedicated years to specific technologies, so we can leverage their learnings and examples to give us a jump start on solving the problem. To accomplish this, we’ve become very good at a few key skills.įirst, we have to be able to find quality resources and examples quickly. This is often the case with prototyping we have to become proficient enough to get something working by jumping into new, cutting edge technologies. With the short time frame, we didn’t have time to deep dive into the world of computer vision. So, of course, we said, “No problem!” and got to work. The project had a quick turn around with only four weeks to design and build the entire experience, and it had to work on an iPhone in real-time. ![]() Our client loved this, so all we needed to do was figure out how to make it happen. Instead of faking it and just using a timer, we tossed out the idea of actually detecting the user before snapping the photos in an effort to provide users with the appropriate prompts and make it feel more realistic. The challenge was knowing when they were fully in-frame and the right distance away from the device. One interaction required a user to put their phone on the ground, step away, and have full-body photos taken. The user flows required the participants to use smartphones in non-traditional ways, and we needed to make sure these unfamiliar behaviors were as easy and friction-free as possible. In early 2019, we set out to build a functional high-fidelity prototype for user experience testing, and we wanted it to feel as real as possible. Hog.setSVMDetector( cv2.HOGDescriptor_getDefaultPeopleDetector() )įound,w=hog.detectMultiScale(frame, winStride=(8,8), padding=(32,32), scale=1.Article written by Matthew Ward, Senior Developer at Instrument # so we slightly shrink the rectangles to get a nicer output.Ĭv2.rectangle(img, (x+pad_w, y+pad_h), (x+w-pad_w, y+h-pad_h), (0, 255, 0), thickness) # the HOG detector returns slightly larger rectangles than the real objects. Return rx > qx and ry > qy and rx + rw < qx + qw and ry + rh < qy + qhĭef draw_detections(img, rects, thickness = 1): This one is using the hog descriptor you can find the sample in samples/python/peopledetect.py I used the sample video provided by the opencv installation. ![]()
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