def get_all_objs():
a, img_work = utils.take_workspace_img(client)
img_work = utils.standardize_img(img_work)
if not a:
a, img_work = debug_markers(img_work)
return img_work, None
mask = utils.objs_mask(img_work)
objs = utils.extract_objs(img_work, mask)
if len(objs) == 0:
return img_work, []
imgs = []
if model is None:
return img_work, objs
for x in range(len(objs)):
imgs.append(cv2.resize(objs[x].img, (64, 64)))
imgs = np.array(imgs)
predictions = model.predict(imgs)
for x in range(len(predictions)):
obj = objs[x]
obj.type = predictions[x].argmax()
# graphical debug
cv2.drawContours(img_work, [obj.box], 0, (0, 0, 255), 2)
pos = [obj.x + 20, obj.y]
# Text position
img_work = cv2.putText(img_work, objects_names[obj.type], tuple(pos), font, 0.5, (255, 0, 0), 1, cv2.LINE_AA)
pos[0] += img_work.shape[0]
img_work = cv2.putText(img_work, objects_names[obj.type], tuple(pos), font, 0.5, (0, 255, 0), 1, cv2.LINE_AA)
return img_work, objs
def labelling(client, name):
try:
os.mkdir("./data/" + name)
except:
pass
print("label ", name)
a, img_work = utils.take_workspace_img(client, 1.5)
mask = utils.objs_mask(img_work)
debug = concat_imgs([img_work, mask], 1)
if __name__ == '__main__':
show_img("robot view", debug, wait_ms=1)
objs = utils.extract_objs(img_work, mask)
if len(objs) != 0:
print(str(len(objs)) + " object detected")
objs[0].img = resize_img(objs[0].img, width=64, height=64)
if __name__ == '__main__':
show_img("robot view2", img_work, wait_ms=50)
print("saved", name)
cv2.imwrite("./data/" + name + "/" + str(time.time()) + ".jpg",
img_work)
else:
print(str(len(objs)) + " object detected")
return img_work
def load_dataset(data_path):
#list all directories in data_path
objects_names = os.listdir(data_path)
print(objects_names)
objects_list = [] #list containing all objects images
labels_list = [] #list containing all objects Labels
files_names = [] #list containing all objects names
obj_id = 0
#make the data_mask directorie
try:
os.mkdir("./data_mask1")
except:
pass
for name in objects_names:
# list all image for each objects
list_dir = os.listdir(data_path + name)
print(name + " " + str(len(list_dir)))
# make a "data_mask/<obj_name>" subdirectory for each object
try:
os.mkdir("./data_mask1/" + name)
except:
pass
#for each file in in "data/<obj_name>""
for file_name in list_dir:
# read the file
img = cv2.imread(data_path + name + "/" + file_name)
# img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
img = utils.standardize_img(img)
# extract all objects from the image
mask = utils.objs_mask(img)
objs = utils.extract_objs(img, mask)
#for each object in the image
for x in range(0, len(objs)):
img = objs[x].img
#write the image in data_mask/<file_name>"
cv2.imwrite(
"data_mask1/" + name + "/" + str(x) + "_" + file_name, img)
#resize the image to match our model input
img = cv2.resize(img, (64, 64))
#create a numpy array of float with a size of (64, 64, 3)
img_float = np.zeros((64, 64, 3), np.float32)
#scale the image color between 0.0 and 1.0 and copy it in the numpy array
img_float[:][:][:] = img[:][:][:] / 255.0
#create a numpy array full of 0 with a shape of (len(objects_names))
label = np.zeros((len(objects_names)), np.float32)
#set is corresponding id to 1
label[obj_id] = 1
#insert all our numpy array in the data set
objects_list.append(img_float)
labels_list.append(label)
files_names.append(str(x) + "_" + file_name)
print(len(objs), end='')
print("|", end="", flush=True)
print("")
obj_id += 1
return [objects_list, labels_list, files_names, objects_names]
#take a picture of the workspace (infinite loop if the robot can't see the workspace)
while not a:
a, img_work = utils.take_workspace_img(client)
img_work = utils.standardize_img(img_work)
#calculate the mask for img_work (black and white image)
mask = utils.objs_mask(img_work)
#aply the mask to the image
img_masked = cv2.bitwise_and(img_work, img_work, mask=mask)
img_db = concat_imgs([img_work, mask, img_masked], 1)
#get all opbject from the image
objs = utils.extract_objs(img_work, mask)
if len(objs) == 0:
continue
imgs = []
#resize all objects img to 64*64 pixels
for x in range(len(objs)):
imgs.append(resize_img(objs[x].img, width=64, height=64))
imgs = np.array(imgs)
#predict all the images
predictions = model.predict(imgs)
#for all predictions find the corresponding name and print it on img_db
for x in range(len(predictions)):