def select_note_heads_whole(image_orig, image_bin, groups,dicts,regions):
img, contours, hierarchy = cv2.findContours(image_bin.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)
regions_dict = {}
for contour in contours:
rect = cv2.minAreaRect(contour)
box = cv2.boxPoints(rect)
box = np.int0(box)
(x,y),radius = cv2.minEnclosingCircle(contour)
center = (int(x),int(y))
radius = int(radius)
if radius==9:
print 'rad ',radius
cv2.circle(image_orig,center,radius,(0,255,0),2)
# cv2.drawContours(image_orig,[box],0,(0,0,255),2)
(x,y),(w,h),angle = rect
#if angle < -40 and angle > -50 and w > 6 and w<8 and h > 5:
# if w<8 and w > 4 and h> 9 and angle > -1:
# print '////////////////'
# print w,h,angle
x,y,w,h = cv2.boundingRect(contour)
region = image_bin[y:y+h+1,x:x+w+1]
regions_dict[x] = [img_fun.resize_region(region), (x,y,w,h)]
# cv2.rectangle(image_orig,(x,y),(x+w,y+h),(0,255,0),2)
row = find_row(groups,y)
dicts[row][x] = [img_fun.resize_region(region),(x,y,w,h)]
r = reg.Region(x,y,w,h)
regions.add_region(r)
return image_orig,dicts,regions
def select_note_heads_quarter(image_orig, image_bin, groups, dicts,regions):
img, contours, hierarchy = cv2.findContours(image_bin.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
regions_dict = {}
for contour in contours:
x,y,w,h = cv2.boundingRect(contour)
if w >8 and w<15 and h>7 and h<12:
region = image_bin[y:y+h+1,x:x+w+1];
regions_dict[x] = [img_fun.resize_region(region), (x,y,w,h)]
cv2.rectangle(image_orig,(x,y),(x+w,y+h),(0,255,0),2)
row = find_row(groups,y)
dicts[row][x] = [img_fun.resize_region(region),(x,y,w,h)]
r = reg.Region(x,y,w,h)
regions.add_region(r)
return image_orig,dicts,regions
def select_note_heads(image_orig, image_bin):
img, contours, hierarchy = cv2.findContours(image_bin.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
regions_dict = {}
for contour in contours:
x,y,w,h = cv2.boundingRect(contour)
#if w>10 and w<20:
if w>16 and w < 23 and h<19:
print str(w) + ',' + str(h)
region = image_bin[y:y+h+1,x:x+w+1];
regions_dict[x] = [img_fun.resize_region(region), (x,y,w,h)]
cv2.rectangle(image_orig,(x,y),(x+w,y+h),(0,255,0),2)
sorted_regions_dict = collections.OrderedDict(sorted(regions_dict.items()))
sorted_regions = np.array(sorted_regions_dict.values())
region_positions = []
for x, y, w, h in sorted_regions[0:, 1]:
region_positions.append((x,y))
return image_orig, sorted_regions[:, 0], region_positions
def select_note_heads_half(image_orig, image_bin, groups, dicts,regions):
img, contours, hierarchy = cv2.findContours(image_bin.copy(), cv2.RETR_LIST, cv2.CHAIN_APPROX_NONE)
regions_dict = {}
for contour in contours:
rect = cv2.minAreaRect(contour)
box = cv2.boxPoints(rect)
box = np.int0(box)
(x,y),(w,h),angle = rect
if angle < -20 and angle>-50:
x,y,w,h = cv2.boundingRect(contour)
region = image_bin[y:y+h+1,x:x+w+1];
regions_dict[x] = [img_fun.resize_region(region), (x,y,w,h)]
cv2.rectangle(image_orig,(x,y),(x+w,y+h),(0,255,0),2)
row = find_row(groups,y)
dicts[row][x] = [img_fun.resize_region(region),(x,y,w,h)]
r = reg.Region(x,y,w,h)
regions.add_region(r)
return image_orig,dicts,regions
def select_note_heads(image_orig, image_bin, groups):
img, contours, hierarchy = cv2.findContours(image_bin.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
dicts = [dict() for x in range(len(groups))]
regions_dict = {}
for contour in contours:
x,y,w,h = cv2.boundingRect(contour)
#if w>10 and w<20:
#if w>12 and w < 18 and h<23 and h>18:
region = image_bin[y:y+h+1,x:x+w+1];
row = find_row(groups,y)
#print str(x) + "," + str(y) + ", row: " + str(row)
dicts[row][x] = [img_fun.resize_region(region),(x,y,w,h)]
regions_dict[x] = [img_fun.resize_region(region), (x,y,w,h)]
cv2.rectangle(image_orig,(x,y),(x+w,y+h),(0,255,0),2)
#sorted_regions_all_lines = get_sorted_regions(dicts)
#sorted_regions_dict = collections.OrderedDict(sorted(regions_dict.items()))
#sorted_regions = np.array(sorted_regions_dict.values())
sorted_regions = get_sorted_regions(dicts)
region_positions = []
for x, y, w, h in sorted_regions[0:, 1]:
region_positions.append(y)
return image_orig, sorted_regions[:, 0], region_positions
def select_horizontal_lines(image_orig, image_bin):
img, contours, hierarchy = cv2.findContours(image_bin.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
region_positions = []
regions_dict = {}
for contour in contours:
x,y,w,h = cv2.boundingRect(contour)
region_positions.append(y)
region = image_bin[y:y+h+1,x:x+w+1]
regions_dict[x] = [img_fun.resize_region(region), (y, x, w, h)]
cv2.rectangle(image_orig, (x, y), (x+w, y+h), (0, 255, 0), 2)
sorted_regions_dict = collections.OrderedDict(sorted(regions_dict.items()))
sorted_regions = np.array(sorted_regions_dict.values())
return image_orig, sorted_regions[:, 0], region_positions
