def objects_detector(imgs_bgr8):
global img_clean_gray_class
global img_clean_bgr_learn
global clf
global n_bin
global b_size
global c_size
global saving_learn
global saved
global saving_test
global live
global show
global got_speech
global speech
global loaded_clf
detected_objects_list = []
objects_detector_time = time.time()
for index, img_bgr8 in enumerate(imgs_bgr8):
width, height, d = np.shape(img_bgr8)
w, l, d = np.shape(img_bgr8)
img_clean_bgr_learn = img_bgr8.copy()
img_bgr8 = img_bgr8[13:w - 5, 13:l - 8]
img_clean_bgr_class = img_bgr8.copy()
img_clean_bgr_class = cv2.resize(img_clean_bgr_class, (128, 128), interpolation=cv2.INTER_AREA) # resize image
img_clr = cv2.resize(img_clean_bgr_learn, (128, 128))
img_gray = cv2.resize(cv2.cvtColor(img_clean_bgr_learn, cv2.COLOR_BGR2GRAY), (128, 128))
edges = cv2.Canny(img_gray, 40, 100)
cv2.imshow('Edges', edges)
cv2.imshow('img_gray', img_gray)
cv2.imshow('Clean' + str(index), img_clean_bgr_class)
if loaded_clf:
final, confiance = get_img_to_be_sent(img_clean_bgr_class)
print("storing " + str(confiance))
if confiance < 0.85:
str_img(img_clean_bgr_learn)
if saving_learn == 1:
save_imgs_learn(img_clean_bgr_learn)
if saving_test == 1:
save_imgs_test(img_clean_bgr_class)
if live == 1:
live_learn(img_bgr8)
if show:
if loaded_clf == 0:
print("Classifier not fitted, trying to load one")
if load_classifier(1) == -1:
print("Could not load it, quitting")
else:
cv2.imshow('Sent' + str(index), final)
if got_speech == 0:
pass
else:
if confiance < 0.7:
learn_from_str(1)
rows, cols, d = img_clean_bgr_class.shape
detected_object = Detected_Object()
detected_object.id = 1
detected_object.image = CvBridge().cv2_to_imgmsg(img_clean_bgr_class, encoding="passthrough")
detected_object.center_x = rows / 2
detected_object.center_y = cols / 2
detected_object.features.hog_histogram = get_img_hog(img_clean_bgr_class)[0]
colors_histo, object_shape = getpixelfeatures(img_clean_bgr_class)
detected_object.features.colors_histogram = colors_histo.tolist()
detected_object.features.shape_histogram = object_shape.tolist()
detected_objects_list.append(detected_object)
if got_speech == 0:
return
detected_objects_list_msg = Detected_Objects_List()
detected_objects_list_msg.detected_objects_list = detected_objects_list
detected_objects_list_publisher.publish(detected_objects_list_msg)
got_speech = 0
def objects_detector(uprightrects_tuples):
global last_imgs
global img_clean_gray_class
global img_clean_bgr_class
global img_clean_bgr_learn
global clf
global n_bin
global b_size
global c_size
global saving_learn
global saved
global saving_test
global live
global show
global got_speech
global speech
global loaded_clf
global using_VGA
global iterations
global recording
global obj_history
iterations += 1
detected_objects_list = []
objects_detector_time = time.time()
if len(obj_history) < 7:
obj_history.append(uprightrects_tuples)
return
else:
index_last_obj = iterations % 7
obj_history[index_last_obj] = uprightrects_tuples
if check_stability(obj_history) == 0:
return
# for indexx, snapshots in enumerate(obj_history):
# # print (snapshots[0][0])
# cv2.imshow('Good? ' + str(indexx), snapshots[0][0])
# cv2.waitKey(1)
uprightrects_tuples = obj_history[2]
for index, curr_tuple in enumerate(uprightrects_tuples):
img_bgr8, center = curr_tuple
width, height, d = np.shape(img_bgr8)
w, l, d = np.shape(img_bgr8)
img_clean_bgr_learn = img_bgr8.copy()
if not using_VGA:
img_bgr8 = img_bgr8[13:w - 5, 13:l - 8]
else:
img_bgr8 = img_bgr8[6:w - 2, 6:l - 4]
img_clean_bgr_class = img_bgr8.copy()
img_clean_bgr_class = cv2.resize(img_clean_bgr_class, (128, 128), interpolation=cv2.INTER_AREA) # resize image
img_clr = cv2.resize(img_clean_bgr_learn, (128, 128))
# img_gray = cv2.resize(cv2.cvtColor(img_clean_bgr_learn, cv2.COLOR_BGR2GRAY), (128, 128))
# edges = cv2.Canny(img_gray, 40, 100)
# cv2.imshow('Edges', edges)
# cv2.imshow('img_gray', img_gray)
cv2.imshow('Clean' + str(index), img_clean_bgr_class)
if loaded_clf:
final, confiance = get_img_to_be_sent(img_clean_bgr_class)
if iterations % 50 == 0:
print (confiance)
# print("storing " + str(confiance))
if confiance < 0.85 and recording:
learn_tuple(curr_tuple, obj_history)
learn_from_str(1)
final = cv2.resize(img_clean_bgr_class, (256, 256))
# print("storing " + str(confiance))
if saving_learn == 1:
cv2.imshow('LEARN', img_clean_bgr_learn)
cv2.waitKey(1000)
save_imgs_learn(img_clean_bgr_learn)
if saving_test == 1:
cv2.imshow('TEST', img_clean_bgr_class)
cv2.waitKey(1000)
save_imgs_test(img_clean_bgr_class)
if show:
if loaded_clf == 0:
print("Classifier not fitted, trying to load one")
if load_classifier(1) == -1:
print("Could not load it, quitting")
else:
cv2.imshow('Sent' + str(index), final)
cv2.waitKey(100)
# if got_speech == 0:
# pass
# else:
if loaded_clf:
rows, cols, d = final.shape
detected_object = Detected_Object()
detected_object.id = 1
detected_object.image = CvBridge().cv2_to_imgmsg(final, encoding="passthrough")
detected_object.center_x = rows / 2
detected_object.center_y = cols / 2
detected_object.features.hog_histogram = get_img_hog(final)[0]
colors_histo, object_shape = getpixelfeatures(final)
detected_object.features.colors_histogram = colors_histo.tolist()
detected_object.features.shape_histogram = object_shape.tolist()
detected_objects_list.append(detected_object)
if recording == 1:
cv2.imshow('Just Sent' + str(index), final)
# if got_speech == 0:
# return
if loaded_clf:
detected_objects_list_msg = Detected_Objects_List()
detected_objects_list_msg.detected_objects_list = detected_objects_list
detected_objects_list_publisher.publish(detected_objects_list_msg)
got_speech = 0
recording = 0