def example_01():
folder_input = 'images/ex_match/'
folder_output = 'images/output/'
if not os.path.exists(folder_output):
os.makedirs(folder_output)
else:
tools_IO.remove_files(folder_output)
#img_base = cv2.imread(folder_input + 'boxes.png')
#img_tmpl = cv2.imread(folder_input + 'pattern2.png')
img_base = cv2.imread(folder_input + 'beach.png', 0)
img_tmpl = cv2.imread(folder_input + 'waldo.png', 0)
hitmap_2d = tools_alg_match.calc_hit_field_basic(img_base, img_tmpl)
cv2.imwrite(folder_output + 'hitmap_basic.png', hitmap_2d)
cv2.imwrite(folder_output + 'hitmap_jet.png',
tools_image.hitmap2d_to_jet(hitmap_2d))
cv2.imwrite(folder_output + 'hitmap_viridis.png',
tools_image.hitmap2d_to_viridis(hitmap_2d))
hitmap_2d = tools_alg_match.calc_hit_field(img_base, img_tmpl)
cv2.imwrite(folder_output + 'hitmap_advanced.png', hitmap_2d)
cv2.imwrite(folder_output + 'hitmap_advanced_jet.png',
tools_image.hitmap2d_to_jet(hitmap_2d))
cv2.imwrite(folder_output + 'hitmap_advanced_viridis.png',
tools_image.hitmap2d_to_viridis(hitmap_2d))
return
def estimate_pattern_position(filename_in, folder_out, pattern_width,
pattern_height):
scale = 4.0
image_base = cv2.cvtColor(cv2.imread(filename_in), cv2.COLOR_RGB2BGR)
image_base_scaled = cv2.resize(
image_base,
(int(image_base.shape[1] / scale), int(image_base.shape[0] / scale)))
tools_IO.remove_files(folder_out)
for shift_row in range(0, pattern_height):
for shift_col in range(0, pattern_width):
row = int(image_base.shape[0] / 2) + shift_row
col = int(image_base.shape[1] / 2) + shift_col
image_pattern = image_base[
int(row - pattern_height / 2):int(row + pattern_height / 2),
int(col - pattern_width / 2):int(col + pattern_width / 2), :]
image_pattern_scaled = cv2.resize(
image_pattern, (int(image_pattern.shape[1] / scale),
int(image_pattern.shape[0] / scale)))
hitmap_2d = tools_alg_match.calc_hit_field(image_base_scaled,
image_pattern_scaled)
hitmap_RGB_jet = tools_image.hitmap2d_to_jet(-hitmap_2d)
e = entropy(hitmap_2d.flatten() / 255.0)
toimage(hitmap_RGB_jet).save(folder_out +
('hit_%1.3f_%03d_%03d.bmp' %
(e, shift_row, shift_col)))
toimage(image_pattern_scaled).save(folder_out +
('pat_%03d_%03d.bmp' %
(shift_row, shift_col)))
return
def build_hitmap(folder_in, filename_in_field, filename_in_pattern,
folder_out):
image_field = cv2.imread(folder_in + filename_in_field)
img_pattern = cv2.imread(folder_in + filename_in_pattern)
hitmap_2d = tools_alg_match.calc_hit_field(image_field, img_pattern)
cv2.imwrite(folder_out + 'hitmap_advanced.png', hitmap_2d)
cv2.imwrite(folder_out + 'hitmap_advanced_jet.png',
tools_image.hitmap2d_to_jet(hitmap_2d))
cv2.imwrite(folder_out + 'hitmap_advanced_viridis.png',
tools_image.hitmap2d_to_viridis(hitmap_2d))
return
def add_box_automated(self):
box_current, classIDs = self.get_box_objects(self.current_frame_ID,
self.current_markup_ID)
if len(box_current) != 1: return
box = numpy.array(box_current[0]).astype(int)
image_current = cv2.imread(self.folder_in +
self.filenames[self.current_frame_ID])
cur_left = min(box[0], box[2])
cur_right = max(box[0], box[2])
cur_top = min(box[1], box[3])
cur_bottom = max(box[1], box[3])
R = 100
next_left = max(0, cur_left - R)
next_right = min(cur_right + R, image_current.shape[1])
next_top = max(0, cur_top - R)
next_bottom = min(cur_bottom + R, image_current.shape[0])
if self.image_small is None:
self.image_small = image_current[cur_top:cur_bottom,
cur_left:cur_right]
image_next = cv2.imread(self.folder_in +
self.filenames[self.current_frame_ID + 1])
image_large = image_next[next_top:next_bottom, next_left:next_right]
hitmap = tools_alg_match.calc_hit_field(image_large, self.image_small)
hit = unravel_index(hitmap.argmax(), hitmap.shape)
box = numpy.array([
next_left + hit[1] - (cur_right - cur_left) // 2,
next_top + hit[0] - (cur_bottom - cur_top) // 2,
next_left + hit[1] + (cur_right - cur_left) // 2,
next_top + hit[0] + (cur_bottom - cur_top) // 2
])
self.Boxes.add(self.current_frame_ID + 1, box, self.current_markup_ID)
#image_small_next = image_next[box[1]:box[3], box[0]:box[2]]
#cv2.imwrite(self.folder_in + 'hit.png',tools_image.hitmap2d_to_jet(hitmap))
#cv2.imwrite(self.folder_in + 'small.png', image_small)
#cv2.imwrite(self.folder_in + 'large.png', image_large)
#cv2.imwrite(self.folder_in + 'image_small_next.png', image_small_next)
return
def get_hits(image_base, image_pattern,grid=0.1,path_debug=''):
hitmap_2d = tools_alg_match.calc_hit_field(image_base, image_pattern)
coordinates, chains_hor, chains_ver = detect_peaks_2d(image_base,image_pattern,hitmap_2d, grid=0.05,path_debug=path_debug)
return coordinates,chains_hor, chains_ver,hitmap_2d, 0