def locate_img(image, template):
img = image.copy()
res = cv2.matchTemplate(img, template, method)
print res
print res.shape
cv2.imwrite('image/shape.png', res)
min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(res)
print cv2.minMaxLoc(res)
if method in [cv2.TM_SQDIFF, cv2.TM_SQDIFF_NORMED]:
top_left = min_loc
else:
top_left = max_loc
h, w = template.shape
bottom_right = (top_left[0] + w, top_left[1]+h)
cv2.rectangle(img, top_left, bottom_right, 255, 2)
cv2.imwrite('image/tt.jpg', img)
python类TM_SQDIFF的实例源码
def locate_img(image, template):
img = image.copy()
res = cv2.matchTemplate(img, template, method)
print res
print res.shape
cv2.imwrite('image/shape.png', res)
min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(res)
print cv2.minMaxLoc(res)
if method in [cv2.TM_SQDIFF, cv2.TM_SQDIFF_NORMED]:
top_left = min_loc
else:
top_left = max_loc
h, w = template.shape
bottom_right = (top_left[0] + w, top_left[1]+h)
cv2.rectangle(img, top_left, bottom_right, 255, 2)
cv2.imwrite('image/tt.jpg', img)
def test_crop_random():
# Given one sample image and the following parameters
image = helpers.get_one_sample_image()
parameters = {"dst_size" : (20, 20),
"n_patches" : 5,
}
# When perform crop_random()
patches = utils.crop_random(image, parameters["dst_size"], parameters["n_patches"])
# Then every patch should be included in an image.
match_cost = []
for patch in patches:
M = cv2.matchTemplate(image, patch, cv2.TM_SQDIFF)
min_cost, _, _, _ = cv2.minMaxLoc(M)
match_cost.append(min_cost)
assert np.array(match_cost).all() == 0, "utils.crop_random() unit test failed!!"
def matchTemplate(img_full, img_template, meth):
w, h = img_template.shape[::-1]
img = img_full.copy()
# Apply template Matching
method = eval(meth)
res = cv2.matchTemplate(img,img_template,method)
min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(res)
# 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)
return [top_left, bottom_right]
def matchTemplate(img_full, img_template, meth):
w, h = img_template.shape[::-1]
img = img_full.copy()
# Apply template Matching
method = eval(meth)
res = cv2.matchTemplate(img,img_template,method)
min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(res)
# 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)
return [top_left, bottom_right]
def getMatchingScore(img,digit):
score = (cv2.matchTemplate(img,cv2.imread('Templates/' + 'T'+str(digit) + '.jpg',0),cv2.TM_SQDIFF)/2000)
return score
# Gets the best prediction of the digit in a cell using template matching
def find_matches(img, template_list):
# Make a copy of the image to draw on
# Define an empty list to take bbox coords
bbox_list = []
# Iterate through template list
# Read in templates one by one
# Use cv2.matchTemplate() to search the image
# using whichever of the OpenCV search methods you prefer
# Use cv2.minMaxLoc() to extract the location of the best match
# Determine bounding box corners for the match
# Return the list of bounding boxes
method = cv2.TM_CCOEFF_NORMED
for temp in templist:
tmp = mpimg.imread(temp)
# Apply template Matching
res = cv2.matchTemplate(img,tmp,method)
min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(res)
w, h = (tmp.shape[1], tmp.shape[0])
# 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)
bbox_list.append((top_left, bottom_right))
return bbox_list
def template_match(img_master, img_slave, method = 'cv2.TM_CCOEFF_NORMED', mlx = 1, mly = 1, show=True):
# Apply image oversampling
img_master = cv2.resize(img_master,None,fx=mlx, fy=mly, interpolation = cv2.INTER_CUBIC)
img_slave = cv2.resize(img_slave,None,fx=mlx, fy=mly, interpolation = cv2.INTER_CUBIC)
res = cv2.matchTemplate(img_slave,img_master,eval(method))
w, h = img_master.shape[::-1]
min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(res)
# Control if the method is TM_SQDIFF or TM_SQDIFF_NORMED, take minimum value
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)
# Retrieve center coordinates
px = (top_left[0]+bottom_right[0])/(2.0*mlx)
py = (top_left[1]+bottom_right[1])/(2.0*mly)
# Scale images for visualization
img_master_scaled = cv2.convertScaleAbs(img_master, alpha=(255.0/500))
img_slave_scaled = cv2.convertScaleAbs(img_slave, alpha=(255.0/500))
cv2.rectangle(img_slave_scaled,top_left, bottom_right, 255, 2*mlx)
if show == True:
plt.figure(figsize=(20,10))
plt.subplot(131),plt.imshow(res,cmap = 'gray')
plt.title('Matching Result'), plt.xticks([]), plt.yticks([])
plt.subplot(132),plt.imshow(img_master_scaled,cmap = 'gray')
plt.title('Detected Point'), plt.xticks([]), plt.yticks([])
plt.subplot(133),plt.imshow(img_slave_scaled, cmap = 'gray')
plt.suptitle(method)
plt.show()
return px, py, max_val
def matchTemplate(self, img_full, img_template, aMeth):
w, h = img_template.shape[::-1]
img = img_full.copy()
# Apply template Matching
method = eval(aMeth)
res = cv2.matchTemplate(img,img_template,method)
min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(res)
# 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)
return [top_left, bottom_right]
def click_image(image, notify=True):
if notify:
_notify("starting to click " + image)
if isinstance(image, str) or isinstance(image, unicode):
template = cv2.imread(image, 0)
elif isinstance(image, PngImageFile):
pass # need to convert to cv2 image type
sleep(2)
#GET SCREENSHOT
call(["gnome-screenshot", "--file=/tmp/beryl.png"])
sleep(1)
#FIND LOCATION OF NAME
source = cv2.imread('/tmp/beryl.png', 0)
points = []
w, h = template.shape[::-1]
methods = [cv2.TM_CCOEFF,cv2.TM_CCOEFF_NORMED,cv2.TM_CCORR,cv2.TM_CCORR_NORMED,cv2.TM_SQDIFF,cv2.TM_SQDIFF_NORMED]
for method in methods:
# Apply Template Matching
result = cv2.matchTemplate(source.copy(), template, method)
min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(result)
#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)
# (x,y)
point = ( top_left[0] + (float(w)/2), top_left[1] + (float(h)/2) )
points.append(point)
best_point = sorted([(point, avg_distance(point, points)) for point in points], key=lambda tup: tup[1])[0][0]
click_location(best_point)
if notify:
_notify("finished clicking image")
def match_template_opencv(template, image, options):
"""
Match template using OpenCV template matching implementation.
Limited by number of channels as maximum of 3.
Suitable for direct RGB or Gray-scale matching
:param options: Other options:
- distance: Distance measure to use. (euclidean | correlation | ccoeff).
Default: 'correlation'
- normalize: Heatmap values will be in the range of 0 to 1. Default: True
- retain_size: Whether to retain the same size as input image. Default: True
:return: Heatmap
"""
# if image has more than 3 channels, use own implementation
if len(image.shape) > 3:
return match_template(template, image, options)
op = _DEF_TM_OPT.copy()
if options is not None:
op.update(options)
method = cv.TM_CCORR_NORMED
if op['normalize'] and op['distance'] == 'euclidean':
method = cv.TM_SQDIFF_NORMED
elif op['distance'] == 'euclidean':
method = cv.TM_SQDIFF
elif op['normalize'] and op['distance'] == 'ccoeff':
method = cv.TM_CCOEFF_NORMED
elif op['distance'] == 'ccoeff':
method = cv.TM_CCOEFF
elif not op['normalize'] and op['distance'] == 'correlation':
method = cv.TM_CCORR
heatmap = cv.matchTemplate(image, template, method)
# make minimum peak heatmap
if method not in [cv.TM_SQDIFF, cv.TM_SQDIFF_NORMED]:
heatmap = heatmap.max() - heatmap
if op['normalize']:
heatmap /= heatmap.max()
# size
if op['retain_size']:
hmap = np.ones(image.shape[:2]) * heatmap.max()
h, w = heatmap.shape
hmap[:h, :w] = heatmap
heatmap = hmap
return heatmap
def find_subimage_in_array(self, sub_image, main_image, threshold=0.40, value=False, debug=False):
"""
http://docs.opencv.org/3.1.0/d4/dc6/tutorial_py_template_matching.html
Args:
sub_image: A numby matrix containing the template we are trying to match
main_image: A numpy array containing the main image we are trying to find the template in
value: If true: Similarity is sent back.
threshold: A treshhold regarding hos sensitive the matching should be.
Returns:
A list containing touples:
If value is true:
The touples got he following elements(left,top,right,down,similarity)
Where similarity is a measure toward one
Else:
The touples got he following elements(left,top,right,down)
"""
# TODO: Check the test_init_wnd test for how to implement this :)
logging.debug("Doing a template match with {} as threshold".format(threshold))
methods = [cv2.TM_CCOEFF, cv2.TM_CCOEFF_NORMED, cv2.TM_CCORR, cv2.TM_CCORR_NORMED, cv2.TM_SQDIFF,
cv2.TM_SQDIFF_NORMED]
method = methods[0]
h, w = sub_image.shape[0:2]
res = cv2.matchTemplate(main_image, sub_image, method)
loc = np.where(res >= threshold)
locations = []
for pt in zip(*loc[::-1]):
if value:
locations.append((pt[0], pt[1], pt[0] + w, pt[1] + h, res[pt[1], pt[0]]))
else:
locations.append((pt[0], pt[1], pt[0] + w, pt[1] + h))
logging.debug("Found {} locations".format(len(locations)))
if debug:
plt.subplot(121), plt.imshow(res, cmap='gray')
plt.title('Matching Result'), plt.xticks([]), plt.yticks([])
plt.subplot(122), plt.imshow(main_image, cmap='gray')
plt.title('Detected Point'), plt.xticks([]), plt.yticks([])
for pt in zip(*loc[::-1]):
cv2.rectangle(main_image, pt, (pt[0] + w, pt[1] + h), (255, 0, 255), 2)
plt.imshow(main_image)
plt.show()
if value:
locations.sort(reverse=True, key=operator.itemgetter(4))
return list(map(operator.itemgetter(0, 1, 2, 3), locations))
