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
rotate_img = img.rotate(180, Image.BILINEAR)
else:
rotate_img = img
detect_model.load_state_dict(torch.load(configs.detect_model_one))
detect_model.eval()
rotate_img = rotate_img.convert("RGB")
w, h = rotate_img.size
ratio_w = 512 / w
ratio_h = 512 / h
img_tmp = rotate_img.resize((512, 512))
shape, seal_boxes = seal_detect(img_tmp, seal_model, device)
boxes = detect.detect(img_tmp, detect_model, device)
boxes = detect.adjust_ratio(boxes, ratio_w, ratio_h)
plot_img = detect.plot_boxes(rotate_img, boxes)
plot_img = detect.plot_boxes(plot_img, seal_boxes)
plot_img.save('detected.jpg')
print('detection result saved as: detected.jpg')
orig_vertices = []
theta = 0
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)