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Solución:
Pude hacer que esto funcionara usando Python 2.7.13 y opencv-python == 3.1.0.4
Aquí está el código para ello.
import cv2
import numpy as np
import sys
if len(sys.argv) < 3:
print 'Usage: python match.py '
sys.exit()
template_path = sys.argv[1]
template = cv2.imread(template_path, cv2.IMREAD_UNCHANGED)
channels = cv2.split(template)
zero_channel = np.zeros_like(channels[0])
mask = np.array(channels[3])
image_path = sys.argv[2]
image = cv2.imread(image_path, cv2.IMREAD_UNCHANGED)
mask[channels[3] == 0] = 1
mask[channels[3] == 100] = 0
# transparent_mask = None
# According to http://www.devsplanet.com/question/35658323, we can only use
# cv2.TM_SQDIFF or cv2.TM_CCORR_NORMED
# All methods can be seen here:
# http://docs.opencv.org/2.4/doc/tutorials/imgproc/histograms/template_matching/template_matching.html#which-are-the-matching-methods-available-in-opencv
method = cv2.TM_SQDIFF # R(x,y) = sum _x',y' (T(x',y')-I(x+x',y+y'))^2 (essentially, sum of squared differences)
transparent_mask = cv2.merge([zero_channel, zero_channel, zero_channel, mask])
result = cv2.matchTemplate(image, template, method, mask=transparent_mask)
min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(result)
print 'Lowest squared difference WITH mask', min_val
# Now we'll try it without the mask (should give a much larger error)
transparent_mask = None
result = cv2.matchTemplate(image, template, method, mask=transparent_mask)
min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(result)
print 'Lowest squared difference WITHOUT mask', min_val
Aquí está como una esencia.
Esencialmente, debe asegurarse de que está utilizando el método de coincidencia correcto.
Mi entorno usa opencv 3.1.0 y python 2.7.11.
Aquí está el código que busca imágenes en otra imagen donde la plantilla usa transparencia (canal alfa). Espero que esto pueda ayudarte.
def getMultiFullInfo(all_matches,w,h):
#This function will rearrange the data and calculate the tuple
# for the square and the center and the tolerance for each point
result = []
for match in all_matches:
tlx = match[0]
tly = match[1]
top_left = (tlx,tly)
brx = match[0] + w
bry = match[1] + h
bottom_right = (brx,bry)
centerx = match[0] + w/2
centery = match[1] + h/2
center = [centerx,centery]
result.append('top_left':top_left,'bottom_right':bottom_right,'center':center,'tolerance':match[2])
return result
def getMulti(res, tolerance,w,h):
#We get an opencv image in the form of a numpy array and we need to
# find all the occurances in there knowing that 2 squares cannot intersect
#This will give us exactly the matches that are unique
#First we need to get all the points where value is >= tolerance
#This wil get sometimes some squares that vary only from some pixels and that are overlapping
all_matches_full = np.where (res >= tolerance)
logging.debug('*************Start of getMulti function')
logging.debug('All >= tolerance')
logging.debug(all_matches_full)
#Now we need to arrange it in x,y coordinates
all_matches_coords = []
for pt in zip(*all_matches_full[::-1]):
all_matches_coords.append([pt[0],pt[1],res[pt[1]][pt[0]]])
logging.debug('In coords form')
logging.debug(all_matches_coords)
#Let's sort the new array
all_matches_coords = sorted(all_matches_coords)
logging.debug('Sorted')
logging.debug(all_matches_coords)
#This function will be called only when there is at least one match so if matchtemplate returns something
#This means we have found at least one record so we can prepare the analysis and loop through each records
all_matches = [[all_matches_coords[0][0],all_matches_coords[0][1],all_matches_coords[0][2]]]
i=1
for pt in all_matches_coords:
found_in_existing = False
logging.debug('%s)',i)
for match in all_matches:
logging.debug(match)
#This is the test to make sure that the square we analyse doesn't overlap with one of the squares already found
if pt[0] >= (match[0]-w) and pt[0] <= (match[0]+w) and pt[1] >= (match[1]-h) and pt[1] <= (match[1]+h):
found_in_existing = True
if pt[2] > match[2]:
match[0] = pt[0]
match[1] = pt[1]
match[2] = res[pt[1]][pt[0]]
if not found_in_existing:
all_matches.append([pt[0],pt[1],res[pt[1]][pt[0]]])
i += 1
logging.debug('Final')
logging.debug(all_matches)
logging.debug('Final with all info')
#Before returning the result, we will arrange it with data easily accessible
all_matches = getMultiFullInfo(all_matches,w,h)
logging.debug(all_matches)
logging.debug('*************End of getMulti function')
return all_matches
def checkPicture(screenshot,templateFile, tolerance, multiple = False):
#This is an intermediary function so that the actual function doesn't include too much specific arguments
#We open the config file
configFile = 'test.cfg'
config = SafeConfigParser()
config.read(configFile)
basepics_dir = config.get('general', 'basepics_dir')
debug_dir = config.get('general', 'debug_dir')
font = cv2.FONT_HERSHEY_PLAIN
#The value -1 means we keep the file as is meaning with color and alpha channel if any
# btw, 0 means grayscale and 1 is color
template = cv2.imread(basepics_dir+templateFile,-1)
#Now we search in the picture
result = findPicture(screenshot,template, tolerance, multiple)
#If it didn't get any result, we log the best value
if not result['res']:
logging.debug('Best value found for %s is: %f',templateFile,result['best_val'])
elif logging.getLogger().getEffectiveLevel() == 10:
screenshot_with_rectangle = screenshot.copy()
for pt in result['points']:
cv2.rectangle(screenshot_with_rectangle, pt['top_left'], pt['bottom_right'], 255, 2)
fileName_top_left = (pt['top_left'][0],pt['top_left'][1]-10)
cv2.putText(screenshot_with_rectangle,str(pt['tolerance'])[:4],fileName_top_left, font, 1,(255,255,255),2)
#Now we save to the file if needed
filename = time.strftime("%Y%m%d-%H%M%S") + '_' + templateFile[:-4] + '.jpg'
cv2.imwrite(debug_dir + filename, screenshot_with_rectangle)
result['name']=templateFile
return result
def extractAlpha(img, hardedge = True):
if img.shape[2]>3:
logging.debug('Mask detected')
channels = cv2.split(img)
mask = np.array(channels[3])
if hardedge:
for idx in xrange(len(mask[0])):
if mask[0][idx] <=128:
mask[0][idx] = 0
else:
mask[0][idx] = 255
mask = cv2.cvtColor(mask, cv2.COLOR_GRAY2BGR)
img = cv2.cvtColor(img, cv2.COLOR_BGRA2BGR)
return 'res':True,'image':img,'mask':mask
else:
return 'res':False,'image':img
def findPicture(screenshot,template, tolerance, multiple = False):
#This function will work with color images 3 channels minimum
#The template can have an alpha channel and we will extract it to have the mask
logging.debug('Looking for %s' , template)
logging.debug('Tolerance to check is %f' , tolerance)
logging.debug('*************Start of checkPicture')
h = template.shape[0]
w = template.shape[1]
#We will now extract the alpha channel
tmpl = extractAlpha(template)
logging.debug('Image width: %d - Image heigth: %d',w,h)
# the method used for comparison, can be ['cv2.TM_CCOEFF', 'cv2.TM_CCOEFF_NORMED', 'cv2.TM_CCORR','cv2.TM_CCORR_NORMED', 'cv2.TM_SQDIFF', 'cv2.TM_SQDIFF_NORMED']
meth = 'cv2.TM_CCORR_NORMED'
method = eval(meth)
# Apply template Matching
if tmpl['res']:
res = cv2.matchTemplate(screenshot,tmpl['image'],method, mask = tmpl['mask'])
else:
res = cv2.matchTemplate(screenshot,tmpl['image'],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
best_val = 1 - min_val
else:
top_left = max_loc
best_val = max_val
#We need to ensure we found at least one match otherwise we return false
if best_val >= tolerance:
if multiple:
#We need to find all the time the image is found
all_matches = getMulti(res, float(tolerance),int(w),int(h))
else:
bottom_right = (top_left[0] + w, top_left[1] + h)
center = (top_left[0] + (w/2), top_left[1] + (h/2))
all_matches = ['top_left':top_left,'bottom_right':bottom_right,'center':center,'tolerance':best_val]
#point will be in the form: ['tolerance': 0.9889718890190125, 'center': (470, 193), 'bottom_right': (597, 215), 'top_left': (343, 172)]
logging.debug('The points found will be:')
logging.debug(all_matches)
logging.debug('*************End of checkPicture')
return 'res': True,'points':all_matches
else:
logging.debug('Could not find a value above tolerance')
logging.debug('*************End of checkPicture')
return 'res': False,'best_val':best_val
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