# Freak Example # # This script shows off keypoint tracking by itself. Put an object in front of # your OpenMV Cam without anything else in the image (i.e. camera should be # facing a smooth wall) and the camera will learn the keypoints for an track # whatever object is in the image. You can save keypoints to disk either via # the OpenMV IDE or from in your script. # # Matching keypoints works by first extracting keypoints from an ROI. Once those # are extracted then the OpenMV Cam compares the extracted keypoints against all # the keypoints in an image. It tries to find the center matching point between # the two sets of keypoints. # # Keep in mind that keypoint matching with just one training example isn't very # robust. If you want professional quality results then stick with getting # professionally generated Haar Cascades like the frontalface or eye cascade. # That said, if you're in a very controlled enviroment then keypoint tracking # allows your OpenMV Cam to learn objects on the fly. # # If... you want really good keypoint matching results we suggest you gather # keypoints from all faces of an object and with multiple rotations and scales. # Comparing against all theses sets of keypoints helps versus just one. # # NOTE: LOTS OF KEYPOINTS MAY CAUSE THE SYSTEM TO RUN OUT OF MEMORY! import sensor, time, image # Normalized keypoints are not rotation invariant... NORMALIZED=False # Keypoint extractor threshold, range from 0 to any number. # This threshold is used when extracting keypoints, the lower # the threshold the higher the number of keypoints extracted. KEYPOINTS_THRESH=30 # Keypoint-level threshold, range from 0 to 100. # This threshold is used when matching two keypoint descriptors, it's the # percentage of the distance between two descriptors to the max distance. # In other words, the minimum matching percentage between 2 keypoints. MATCHING_THRESH=80 # Reset sensor sensor.reset() # Sensor settings sensor.set_contrast(1) sensor.set_gainceiling(16) sensor.set_framesize(sensor.QQVGA) sensor.set_pixformat(sensor.GRAYSCALE) # Skip a few frames to allow the sensor settle down # Note: This takes more time when exec from the IDE. for i in range(0, 30): img = sensor.snapshot() img.draw_string(0, 0, "Please wait...") kpts1 = None # Uncomment to load keypoints from file #kpts1 = image.load_descriptor(image.FREAK, "/desc.freak") clock = time.clock() while (True): clock.tick() img = sensor.snapshot() kpts2 = img.find_keypoints(threshold=KEYPOINTS_THRESH, normalized=NORMALIZED) if (kpts1==None): kpts1 = kpts2 print(kpts1) elif kpts2: c = image.match_descriptor(image.FREAK, kpts1, kpts2, threshold=MATCHING_THRESH) # C[3] contains the percentage of matching keypoints. # If more than 25% of the keypoints match, draw stuff. if (c[2]>25): img.draw_cross(c[0], c[1], size=15) img.draw_string(0, 10, "Match %d%%"%(c[2])) # Draw FPS img.draw_string(0, 0, "FPS:%.2f"%(clock.fps()))