def seal_detect(img, model, device):
boxes = detect.detect(img, model, device)
boxes = detect.adjust_ratio(boxes, ratio_w, ratio_h)
orig_vertices = []
theta = 0
if boxes is not None and boxes.size:
for box in boxes:
box = np.array(box[:8])
orig_vertices.append(box)
theta += crop_mode.find_min_rect_angle(box)
orig_vertices = np.array(orig_vertices)
theta /= len(boxes)
tmp_img, vertices = crop_mode.rotate_allimg(img, orig_vertices,
-theta / math.pi * 180)
dict_centers = {}
for i, vertice in enumerate(vertices):
avg_x = int(crop_mode.averagenum(vertice[::2]))
avg_y = int(crop_mode.averagenum(vertice[1::2]))
dict_centers[str(avg_x) + ',' + str(avg_y)] = i
centers = crop_mode.sort_centers(dict_centers, 1)
xcenters = []
for center in centers:
xcenters.append([center])
shape = []
for i, xcenter in enumerate(xcenters):
for center in xcenter:
anno = {}
anno['box'] = orig_vertices[int(center[1])]
anno['class'] = 'seal'
shape.append(anno)
return shape, boxes
orig_vertices.append(box)
theta += crop_mode.find_min_rect_angle(box)
orig_vertices = np.array(orig_vertices)
theta /= len(boxes)
tmp_img, vertices = crop_mode.rotate_allimg(rotate_img, orig_vertices,
-theta / math.pi * 180)
dict_centers = {}
for i, vertice in enumerate(vertices):
avg_x = int(crop_mode.averagenum(vertice[::2]))
avg_y = int(crop_mode.averagenum(vertice[1::2]))
dict_centers[str(avg_x) + ',' + str(avg_y)] = i
centers = crop_mode.sort_centers(dict_centers, 1)
k = 0
xcenters = []
index = [6, 2, 2, 1, 2]
for i, j in enumerate(index):
xcenter = crop_mode.sort_xcenters(centers[k:k + j], 0)
if i == 0:
if int(xcenter[0][0].split(',')[1]) < int(
xcenter[1][0].split(',')[1]):
tmp = xcenter[0]
xcenter[0] = xcenter[1]
xcenter[1] = tmp
if int(xcenter[5][0].split(',')[1]) < int(
xcenter[4][0].split(',')[1]):
tmp = xcenter[5]