Python opencv cv2 matchTemplate con transparencia

OpenCV 3.0.0 agregó la capacidad de especificar una máscara al realizar templateMatch. Cuando especifico una máscara, obtengo este error: error: (-215) (depth == CV_8U || depth == CV_32F) && type == _templ.type() && _img.dims() <= 2 in function matchTemplateMask

Imagen de plantilla (PNG con transparencia):

introduzca la descripción de la imagen aquí

Imagen de origen:

introduzca la descripción de la imagen aquí


 # read the template emoji with the alpha channel template = cv2.imread(imagePath, cv2.IMREAD_UNCHANGED) channels = cv2.split(template) zero_channel = np.zeros_like(channels[0]) mask = np.array(channels[3]) # all elements in alpha_channel that have value 0 are set to 1 in the mask matrix mask[channels[3] == 0] = 1 # all elements in alpha_channel that have value 100 are set to 0 in the mask matrix mask[channels[3] == 100] = 0 transparent_mask = cv2.merge([zero_channel, zero_channel, zero_channel, mask]) print image.shape, image.dtype # (72, 232, 3) uint8 print template.shape, template.dtype # (40, 40, 4) uint8 print transparent_mask.shape, transparent_mask.dtype # (40, 40, 4) uint8 # find the matches res = cv2.matchTemplate(image, template, cv2.TM_CCOEFF_NORMED, mask=transparent_mask) 

¿Hay algún problema con el tipo de imagen? No puedo encontrar ningún ejemplo (en Python) usando el nuevo parámetro de máscara del método matchTemplate. ¿Alguien sabe cómo crear la máscara?

Pude hacer que esto funcionara usando Python 2.7.13 y opencv-python ==

Aquí está el código para ello.

 import cv2 import numpy as np import sys if len(sys.argv) < 3: print 'Usage: python  ' 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, we can only use # cv2.TM_SQDIFF or cv2.TM_CCORR_NORMED # All methods can be seen here: # 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 estar usando el método de concordancia correcto.

Mi entorno usa opencv 3.1.0 y python 2.7.11.

Aquí está el código que está buscando imágenes en otra imagen donde la plantilla está utilizando la 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() 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}