def __init__(self, rec, sym, staff_rec, sharp_notes = [], flat_notes = [],img = [],staff_img=[],best_scale=1):
self.rec = rec
self.sym = sym
self.xpadd = 20
self.ypadd = 50
staff_lower, staff_upper, staff_thresh = int(best_scale*100)-20, int(best_scale*100)+20, 0.60
after = img[ int(staff_rec.y):int(staff_rec.y + staff_rec.h), int(rec.x + rec.w):int(rec.x + 2 * rec.w)]
after = cv2.copyMakeBorder(after,self.ypadd,self.ypadd,self.xpadd,self.xpadd,cv2.BORDER_CONSTANT,value=255) #zero padding
locations, scale = fit(after, staff_img, staff_lower, staff_upper, staff_thresh)
locs = []
for i in range(len(staff_img)):
locs.append([pt for pt in zip(*locations[i][::-1])])
locs = [j for i in locs for j in i]
before = img[int(staff_rec.y):int(staff_rec.y + staff_rec.h), int(rec.x - rec.w):int(rec.x)]
before = cv2.copyMakeBorder(before,self.ypadd,self.ypadd,self.xpadd,self.xpadd, cv2.BORDER_CONSTANT, value=255) # zero padding
locations2, scale2 = fit(before, staff_img, staff_lower, staff_upper, staff_thresh)
locs2 = []
for i in range(len(staff_img)):
locs2.append([pt for pt in zip(*locations2[i][::-1])])
locs2 = [j for i in locs2 for j in i]
if(len(locs)<4):
middle = rec.y + (rec.h / 2.0)
height = (middle - staff_rec.y) / staff_rec.h
note_def = note_defs[int(height / note_step + 0.5)]
print(note_def)
print("is not accurate")
else:
heights = [p[1] for p in locs]
heights2 = [q[1] for q in locs2]
avgheight = sum(heights)/len(heights)
print(avgheight+ staff_rec.y - self.ypadd)
avgheight2 = sum(heights2)/len(heights2)
print(avgheight2+ staff_rec.y - self.ypadd)
avgheight = int((avgheight+avgheight2)/2 + 0.5)
avgheight = avgheight + staff_rec.y - self.ypadd
middle = rec.y + (rec.h / 2.0)
height = (middle - (avgheight + 38*best_scale)) / (9*best_scale) #need to improve
note_def = note_defs[int(height + 0.5) - 3]
print("Middle of note:",(rec.x + rec.w/2),middle)
print("Average Height of Staff:",avgheight)
print("Distance Between Middle Average Height:",middle - avgheight)
print("Note Steps From First Line:",height)
print("Note Found:",note_def[0])
print("Best Scale",best_scale)
print("--------------")
self.note = note_def[0]
self.pitch = note_def[1]
if any(n for n in sharp_notes if n.note[0] == self.note[0]):
self.note += "#"
self.pitch += 1
if any(n for n in flat_notes if n.note[0] == self.note[0]):
self.note += "b"
self.pitch -= 1
def locate_rectangles(img, templates, start, stop, threshold):
locations = fit(img, templates, start, stop, threshold)
img_locations = []
for i in range(len(templates)):
w, h = templates[i].shape[::-1]
for pt in zip(*locations[i][::-1]):
img_locations.append(Rectangle(pt[0], pt[1], w, h))
return img_locations
def locate_images(img, templates, start, stop, threshold):
locations, scale = fit(img, templates, start, stop, threshold)
img_locations = []
for i in range(len(templates)):
w, h = templates[i].shape[::-1]
w *= scale
h *= scale
img_locations.append([Rectangle(pt[0], pt[1], w, h) for pt in zip(*locations[i][::-1])])
return img_locations
def locate_images(img, templates, start, stop, threshold):
locations, scale = fit(img, templates, start, stop, threshold)#return best_location, best_scale
#호출 결과 리스트에 template타입 별 sheet내의 비슷한 좌표가 location으로 return
#scale : template의 확대비
img_locations = []
for i in range(len(templates)):
w, h = templates[i].shape[::-1]
w *= scale
h *= scale
img_locations.append([Rectangle(pt[0], pt[1], w, h) for pt in zip(*locations[i][::-1])])
return img_locations
def locate_images(img, templates, start, stop, threshold):
#最もよくマッチングした際の、テンプレート画像の拡大縮小率(scale)と、
#テンプレート画像がマッチングした場所(locations)を算出
locations, scale = fit(img, templates, start, stop, threshold)
img_locations = []
for i in range(len(templates)):
w, h = templates[i].shape[::-1]
#テンプレート画像を拡大縮小
w *= scale
h *= scale
#マッチングした場所をRectangleクラスのインスタンスにする
img_locations.append(
[Rectangle(pt[0], pt[1], w, h) for pt in zip(*locations[i][::-1])])
return img_locations
def locate_images_elem(img, templates, start, stop, threshold, boxes):
img_locations = []
ypadd = 50
for box in boxes:
one_box_img = img[int(box.y):int(box.y + box.h), :]
one_box_img = cv2.copyMakeBorder(one_box_img,
ypadd,
ypadd,
0,
0,
cv2.BORDER_CONSTANT,
value=255) # zero padding
locations, scale = fit(one_box_img, templates, start, stop, threshold)
for i in range(len(templates)):
w, h = templates[i].shape[::-1]
w *= scale
h *= scale
img_locations.append([
Rectangle(box.x + pt[0], box.y + pt[1] - ypadd, w, h)
for pt in zip(*locations[i][::-1])
])
return img_locations
