def detect_dep(depth_img, gr0, edge_width=8):
# 根据给定抓取从所给边缘图像中裁剪得到五个区域
gr = copy.copy(gr0) # 这得copy一份,否则会影响外面
img = Image(depth_img)
angle = gr.angle
center = gr.center
img = Image(depth_img)
img.rotate(-angle, center)
gr.rotate(-angle, center)
show_grasp(img.img, gr)
points = gr.points
img.crop(points[0], points[2])
img.resize((50, 100))
img_1 = img.img[:, 0:edge_width][2:23, :]
img_2 = img.img[:, 0:edge_width][27:48, :]
img_3 = img.img[:, 100 - edge_width:100][2:23, :]
img_4 = img.img[:, 100 - edge_width:100][27:48, :]
img_middle = img.img[:, 10:90]
flag_m, _, _, _, height_m = check_layers(img_middle, 4200, middle=True)
flag_1, _, _, _, height_1 = check_layers(img_1, 200, base_height=height_m)
flag_2, _, _, _, height_2 = check_layers(img_2, 200, base_height=height_m)
flag_3, _, _, _, height_3 = check_layers(img_3, 200, base_height=height_m)
flag_4, _, _, _, height_4 = check_layers(img_4, 200, base_height=height_m)
if flag_1 and flag_2 and flag_3 and flag_4 and not flag_m:
# print('合理抓取')
return 0
else:
return 1
def visualization(val_data, idx, grasps_pre, grasps_true):
#最开始的几个迭代过程中不会有q_img值足够大的预测出现，所以，此时提出不出有效的抓取，进而是不会有visuaization出现的
img = Image.from_file(val_data.dataset.rgbf[idx])
left = val_data.dataset._get_crop_attrs(idx)[1]
top = val_data.dataset._get_crop_attrs(idx)[2]
img.crop((left, top), (left + 300, top + 300))
a = img.img
a_points = grasps_pre[0].as_gr.points.astype(np.uint8) #预测出的抓取
b_points = grasps_true.points
color1 = (255, 255, 0)
color2 = (255, 0, 0)
for i in range(3):
img = cv2.line(a, tuple(a_points[i]), tuple(a_points[i + 1]),
color1 if i % 2 == 0 else color2, 1)
img = cv2.line(a, tuple(a_points[3]), tuple(a_points[0]), color2, 1)
color1 = (0, 0, 0)
color2 = (0, 255, 0)
for b_point in b_points:
for i in range(3):
img = cv2.line(a, tuple(b_point[i]), tuple(b_point[i + 1]),
color1 if i % 2 == 0 else color2, 1)
img = cv2.line(a, tuple(b_point[3]), tuple(b_point[0]), color2, 1)
#cv2.imshow('img',a)
cv2.imwrite('img.png', a)
def get_rgb(self, idx):
'''
:功能 :读取返回指定id的rgb图像
:参数 idx :int,要读取的数据id
:返回 :ndarray,处理好后的rgb图像
'''
rgb_img = Image.from_file(self.rgbf[idx])
rgb_img.normalize()
center, left, top = self._get_crop_attrs(idx)
rgb_img.crop((left, top),
(left + self.output_size, top + self.output_size))
return rgb_img.img
def get_gr_feature_map(features_map, gr1):
gr = copy.copy(gr1) # 这得copy一份,否则会影响外面
angle = gr.angle
center = gr.center
gr.rotate(-angle, center)
points = gr.points
features = []
for feature_map in features_map:
img = Image(feature_map)
img.rotate(-angle, center)
img.crop(points[0], points[2])
img.resize((50, 100))
features.append(img.img)
return np.array(features)
def get_rgb(self, idx, rot=0, zoom=1.0, normalize=True):
rgb_img = Image.from_file(self.rgbf[idx])
center, left, top = self._get_crop_attrs(idx)
#先旋转后裁剪再缩放最后resize
rgb_img.rotate(rot, center)
rgb_img.crop((top, left), (min(
480, top + self.output_size), min(640, left + self.output_size)))
rgb_img.zoom(zoom)
rgb_img.resize((self.output_size, self.output_size))
if normalize:
rgb_img.normalize()
rgb_img.img = rgb_img.img.transpose((2, 0, 1))
return rgb_img.img
def get_rgb(self,idx,rot = 0,zoom = 1.0,normalize = True):
'''
:功能 :读取返回指定id的rgb图像
:参数 idx :int,要读取的数据id
:返回 :ndarray,处理好后的rgb图像
'''
rgb_img = Image.from_file(self.rgbf[idx])
rgb_img.rotate(rot)
rgb_img.zoom(zoom)
#注意，这里暂且不论是否zoom，图像是直接由（1024X1024）resize成为了(300X300)的，相应的抓取框标注也必须进行同样程度的“resize”或者说缩放（scale）才行
rgb_img.resize((self.output_size, self.output_size))
if normalize:
rgb_img.normalize()
rgb_img.img = rgb_img.img.transpose((2, 0, 1))
#这里还有一句transpose没写，先不管
return rgb_img.img
def get_rgb(self,idx,rot=0, zoom=1.0):
'''
:功能 :读取返回指定id的rgb图像
:参数 idx :int,要读取的数据id
:返回 :ndarray,处理好后的rgb图像
'''
rgb_img = Image.from_file(self.rgbf[idx])
rgb_img.normalize()
center,left,top = self._get_crop_attrs(idx)
#先旋转后裁剪再缩放最后resize
rgb_img.rotate(rot,center)
rgb_img.crop((left,top),(left+self.output_size,top+self.output_size))
rgb_img.zoom(zoom)
rgb_img.resize((self.output_size, self.output_size))
return rgb_img.img
def delete_surplus(edges, gr, gr_origin, idx):
# gr :: type :Grasp
# 根据给定抓取从所给边缘图像中裁剪得到五个区域
width_o = gr_origin.width
width_0 = gr.width
gr_bak = copy.copy(gr)
angle = gr.angle
center = gr.center
img = Image(copy.copy(edges))
img.rotate(-angle, center)
# 将抓取也转正
gr.rotate(-angle, center)
points = gr.points
# 裁剪获得抓取框取域图像
img.crop(points[0], points[2])
img.resize((50, 100))
gr = gr_bak
# 裁剪获得前8% 后8%和中间的图像
# flag_1_l = np.sum(img.img[:,0:8]) > 10
flag_2_l = np.sum(img.img[:, 0:16]) > 10
flag_3_l = np.sum(img.img[:, 0:24]) > 10
flag_4_l = np.sum(img.img[:, 0:32]) > 10
flag_5_l = np.sum(img.img[:, 0:40]) > 10
flag_1_r = np.sum(img.img[:, 60:100]) > 10
flag_2_r = np.sum(img.img[:, 68:100]) > 10
flag_3_r = np.sum(img.img[:, 76:100]) > 10
flag_4_r = np.sum(img.img[:, 84:100]) > 10
# flag_5_r = np.sum(img.img[:,92:100]) > 10
l_list = np.array([flag_2_l, flag_3_l, flag_4_l, flag_5_l], dtype=np.int32)
r_list = np.array([flag_1_r, flag_2_r, flag_3_r, flag_4_r], dtype=np.int32)
zero_l = 4 - np.count_nonzero(l_list)
zero_r = 4 - np.count_nonzero(r_list)
# 需要削去的百分比例
scale = 1 - 0.08 * (zero_l + zero_r)
# 每个0削去部分的实际宽度除以2就是需要平移的距离
step = (gr.width * 0.08 / 2)
# print('移动步幅为',step)
# 削去多余部分
# print(gr.width)
# print(gr.center)
gr = scale_width(gr, scale)
# print(gr.width)
# print(gr.center)
# 执行平移哪边削得少就往哪边平移,坐标0多就是正,往右移,有边0多就是负,往左移
move = int(round((zero_l - zero_r) * step))
# print(move)
gr = move_position(gr, [0, move])
# print(gr.width)
# print(gr.center)
# print(l_list,r_list)
# print(scale)
# print('宽度缩放',gr.width/gr_origin.width)
# print('中心偏移',gr_origin.center-gr.center)
# print('角度差异',gr_origin.angle-gr.angle)
img_a = show_grasp(copy.copy(edges), gr, color=200)
cv2.imwrite('c_images/{}_img3_d.png'.format(idx.cpu().data.numpy()), img_a)
width = gr.width
width_origin = gr_origin.width
angle = gr.angle
origin_angle = gr_origin.angle
center = gr.center
origin_center = gr_origin.center
img_origin = show_grasp(copy.copy(edges), gr_origin, color=200)
cv2.imwrite('c_images/{}_img1_origin.png'.format(idx.cpu().data.numpy()),
img_origin)
return gr, gr.width / gr_origin.width, gr.center - gr_origin.center, gr.angle - gr_origin.angle
def correct_grasp(edges, gr, idx):
# gr :: type :Grasp
# 根据给定抓取从所给边缘图像中裁剪得到五个区域
width = gr.width
gr_bak = copy.copy(gr)
img = Image(edges)
a = img.img
# 可视化的选项
img_b = show_grasp(copy.copy(edges), gr, color=200)
angle = gr.angle
center = gr.center
img.rotate(-angle, center)
# 将抓取也转正
gr.rotate(-angle, center)
points = gr.points
# 裁剪获得抓取框取域图像
img.crop(points[0], points[2])
try:
img.resize((50, 100))
except:
return gr, 0, img_b, 0
# 裁剪获得前10% 后10%和中间的图像
img_1 = img.img[:, 0:8][0:25, :]
img_2 = img.img[:, 0:8][25:50, :]
img_3 = img.img[:, 92:100][0:25, :]
img_4 = img.img[:, 92:100][25:50, :]
# img_middle = img.img[:,10:90]
img_middle_l = img.img[:, 10:50]
img_middle_r = img.img[:, 50:90]
flag_1 = np.sum(img_1) > 10
flag_2 = np.sum(img_2) > 10
flag_3 = np.sum(img_3) > 10
flag_4 = np.sum(img_4) > 10
flag_m_l = np.sum(img_middle_l) > 200
flag_m_r = np.sum(img_middle_r) > 200
# 修正量定义
scale = 1.05
m_angle = 0.05
# 重新获得原始抓取
gr = gr_bak
# 先判断中间有没有
if flag_m_l and flag_m_r:
# print('中间有')
# 再分情况探讨四周
# 如果四个边都有
if flag_1 and flag_2 and flag_3 and flag_4:
# print('四边都有,应当放大一下')
gr = scale_width(gr, scale)
# 三个的时候只有旋转
# 如果1,2,3或1,3,4或
elif (flag_1 and flag_2 and flag_3) or (flag_1 and flag_3 and flag_4):
# print('应当顺时针转一下')
gr = rotate_angle(gr, -m_angle)
elif (flag_2 and flag_3 and flag_4) or (flag_1 and flag_2 and flag_4):
# print('应当逆时针转一下')
gr = rotate_angle(gr, m_angle)
# 两个的时候有可能平移或者旋转
# 如果是2,3或者就顺时针转
elif (flag_2 and flag_3):
# print('应当顺时针转一下')
gr = rotate_angle(gr, -m_angle)
# 如果是1,4或者就逆时针转
elif (flag_1 and flag_4):
# print('应当逆时针转一下')
gr = rotate_angle(gr, m_angle)
# 如果是3,4就往右移
elif (flag_3 and flag_4):
# print('应当往右移动')
gr = move_position(gr, [0, 1])
# 如果是1,2就往左移
elif (flag_1 and flag_2):
# print('应当往左移动')
gr = move_position(gr, [0, -1])
# 如果是2,4就往下移
elif (flag_2 and flag_4):
# print('应当往下移动')
gr = move_position(gr, [-1, 0])
# 如果是1,3就往上移
elif (flag_1 and flag_3):
# print('应当往上移动')
gr = move_position(gr, [1, 0])
# 一个的话就都是旋转
# 如果是2或3就顺时针旋转
elif flag_2 or flag_3:
# print('应当顺时针转一下')
gr = rotate_angle(gr, -m_angle)
# 如果是1或4就逆时针旋转
elif flag_1 or flag_4:
# print('应当逆时针转一下')
gr = rotate_angle(gr, m_angle)
else:
# print('这是个无碰撞的抓取')
img_a = show_grasp(copy.copy(edges), gr, color=200)
cv2.imwrite(
'c_images/{}_img2_a.png'.format(idx.cpu().data.numpy()), img_a)
return gr, 1, img_b, img_a
else:
# print('中间没有,应当放大一下')
gr = scale_width(gr, scale)
if flag_1 and flag_2 and flag_m_l:
# print('应该向左移一下')
gr = move_position(gr, [0, -1])
if flag_3 and flag_4 and flag_m_r:
# print('应该向右移一下')
gr = move_position(gr, [0, 1])
img_a = show_grasp(copy.copy(edges), gr)
return gr, 0, img_b, img_a
img_a = show_grasp(copy.copy(edges), gr, color=200)
return gr, 0, img_b, img_a
