import cv2
import numpy as np
from matplotlib import pyplot as plt
import time

img = cv2.imread('ml2.jpg',0)
img2 = img.copy()
template = cv2.imread('ml.png',0)
w, h = template.shape[::-1]

# All the 6 methods for comparison in a list
methods = [ 'cv2.TM_CCOEFF_NORMED']
            #'cv2.TM_CCORR_NORMED', 'cv2.TM_SQDIFF', 'cv2.TM_SQDIFF_NORMED']

tb = time.clock()
print tb


for meth in methods:
    img = img2.copy()
    method = eval(meth)

    # Apply template Matching
    res = cv2.matchTemplate(img,template,method)
    min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(res)
    print min_loc
    print min_val
    print max_loc
    print max_val

    # If the method is TM_SQDIFF or TM_SQDIFF_NORMED, take minimum
    if method in [cv2.TM_SQDIFF, cv2.TM_SQDIFF_NORMED]:
        top_left = min_loc
    else:
        top_left = max_loc
    bottom_right = (top_left[0] + w, top_left[1] + h)

    cv2.rectangle(img,top_left, bottom_right, 255, 2)

    plt.subplot(121),plt.imshow(res,cmap = 'gray')
    plt.title('Matching Result'), plt.xticks([]), plt.yticks([])
    plt.subplot(122),plt.imshow(img)
    plt.title('Detected Point'), plt.xticks([]), plt.yticks([])
    plt.suptitle(meth)
    
    te = time.clock()
    print te


    plt.show()

<<<<<<< HEAD
=======
    plt.show()
>>>>>>> origin/master

728df1e901d879277530310a379ffc5f0cbd6d1c
import cv2
import numpy as np
from matplotlib import pyplot as plt
import time

img = cv2.imread('ml2.jpg',0)
img2 = img.copy()
template = cv2.imread('ml.png',0)
w, h = template.shape[::-1]

# All the 6 methods for comparison in a list
methods = [ 'cv2.TM_CCOEFF_NORMED']
            #'cv2.TM_CCORR_NORMED', 'cv2.TM_SQDIFF', 'cv2.TM_SQDIFF_NORMED']

tb = time.clock()
print tb


for meth in methods:
    img = img2.copy()
    method = eval(meth)

    # Apply template Matching
    res = cv2.matchTemplate(img,template,method)
    min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(res)
    print min_loc
    print min_val
    print max_loc
    print max_val

    # If the method is TM_SQDIFF or TM_SQDIFF_NORMED, take minimum
    if method in [cv2.TM_SQDIFF, cv2.TM_SQDIFF_NORMED]:
        top_left = min_loc
    else:
        top_left = max_loc
    bottom_right = (top_left[0] + w, top_left[1] + h)

    cv2.rectangle(img,top_left, bottom_right, 255, 2)

    plt.subplot(121),plt.imshow(res,cmap = 'gray')
    plt.title('Matching Result'), plt.xticks([]), plt.yticks([])
    plt.subplot(122),plt.imshow(img)
    plt.title('Detected Point'), plt.xticks([]), plt.yticks([])
    plt.suptitle(meth)
    
    te = time.clock()
    print te


    plt.show()

<<<<<<< HEAD
=======
    plt.show()
>>>>>>> origin/master
