import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
import cv2
from holdDetector import findHolds, findColors, plotColors, buildDetector
import os.path
import time
#cap = cv2.VideoCapture(0)
#path = "C:/Users/SeanC/Documents/GitHub/NeuralClimb/Hold Detection/2D Space/results.avi"
#abspath = os.path.abspath(path)
#cap = cv2.VideoCapture(abspath)
i = 0
detector = buildDetector()
# Define the codec and create VideoWriter object
out = cv2.VideoWriter('results.avi', -1, 15.0, (568, 640), True)
while (True):
i += 1
file_path = "frames_" + "%04d" % i + ".png"
print file_path
frame = cv2.imread(file_path, 1)
if frame == None:
break
#Find keypoints
keypoints, _ = findHolds(frame, detector)
import cv2
import numpy as np
import matplotlib.pyplot as plt
import math
from util import *
from holdDetector import buildDetector
ret, imgOrig = grab_from_file()
gray = cv2.cvtColor(imgOrig,cv2.COLOR_BGR2GRAY)
gray = resize(imgOrig,750)
detector = buildDetector()
th = 150
def draw(img, keypoints):
# Draw detected blobs as red circles.
# cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS ensures the
# size of the circle corresponds to the size of blob
for i, key in enumerate(keypoints):
x = int(key.pt[0])
y = int(key.pt[1])
size = int(math.ceil(key.size))
#Finds a rectangular window in which the keypoint fits
br = (x + size, y + size)
tl = (x - size, y - size)
cv2.rectangle(img,tl,br,(0,0,255),2)
#OpenCV uses BGR format, so that'll need to be reversed for display
"""Example""" import holdDetector as hd #Open dialog to select image img = hd.openImage() # Set initial detector parameters hd.buildDetector(minArea = 500) # Finds each hold. Returns keypoints for each hold # and the points that define the contours of each hold holds, contours = hd.findHolds(img) #Finds a color associated with each keypoint colors = hd.findColors(img,holds) #Draws keypoints onto image and plots colors in 3D space hd.draw(img,holds) hd.plotColors(colors)