def run_object_detection(config):
# Set variables
camera_width = 320
camera_height = 240
window_name = "Object Detection Demo"
input_width = config.IMAGE_SIZE[1]
input_height = config.IMAGE_SIZE[0]
vc = cv2.VideoCapture(0)
vc.set(cv2.cv.CV_CAP_PROP_FRAME_WIDTH, camera_width)
vc.set(cv2.cv.CV_CAP_PROP_FRAME_HEIGHT, camera_height)
vc.set(cv2.cv.CV_CAP_PROP_FPS, 15)
pool = Pool(processes=1)
result = False
fps = 1.0
q_save = Queue()
q_show = Queue()
grabbed, camera_img = vc.read()
q_show.put(camera_img.copy())
input_img = camera_img.copy()
# ----------- Beginning of Main Loop ---------------
while True:
m1 = MyTime("1 loop of while(1) of main()")
pool_result = pool.apply_async(run_inference, (input_img, ))
is_first = True
while True:
sleep(0.01)
grabbed, camera_img = vc.read()
if is_first:
input_img = camera_img.copy()
is_first = False
q_save.put(camera_img.copy())
if not q_show.empty():
window_img = q_show.get()
if result:
window_img = add_rectangle(config.CLASSES, window_img,
result,
(input_height, input_width))
window_img = add_fps(window_img, fps)
# ---------- END of if result != False -----------------
cv2.imshow(window_name, window_img)
key = cv2.waitKey(2) # Wait for 2ms
if key == 27: # ESC to quit
return
if pool_result.ready():
break
# -------------- END of wait loop ----------------------
q_show = clear_queue(q_show)
q_save, q_show = swap_queue(q_save, q_show)
result, fps = pool_result.get()
m1.show()
def run_classification(config):
global nn
camera_height = 240
camera_width = 320
window_name = "Classification Demo"
window_width = 320
window_height = 240
vc = init_camera(camera_width, camera_height)
pool = Pool(processes=1, initializer=nn.init)
grabbed, camera_img = vc.read()
pool_result = pool.apply_async(_run_inference, (camera_img, ))
result = None
fps = 1.0
loop_count = 0
while 1:
m1 = MyTime("1 loop of while(1) of main()")
key = cv2.waitKey(2) # Wait for 2ms
if key == 27: # ESC to quit
break
m2 = MyTime("vc.read()")
grabbed, camera_img = vc.read()
m2.show()
if pool_result.ready():
result, fps = pool_result.get()
pool_result = pool.apply_async(_run_inference, (camera_img, ))
if (window_width == camera_width) and (window_height == camera_height):
window_img = camera_img
else:
window_img = cv2.resize(camera_img, (window_width, window_height))
if result is not None:
result_class = np.argmax(result, axis=1)
add_class_label(window_img, text=str(result[0, result_class][0]), font_scale=0.52, dl_corner=(230, 230))
add_class_label(window_img, text=config.CLASSES[result_class[0]], font_scale=0.52, dl_corner=(230, 210))
window_img = add_fps(window_img, fps)
loop_count += 1
print("loop_count:", loop_count)
m3 = MyTime("cv2.imshow()")
cv2.imshow(window_name, window_img)
m3.show()
m1.show()
sleep(0.05)
cv2.destroyAllWindows()
def run_keypoint_detection(config):
global nn
camera_width = 320
camera_height = 240
window_name = "Keypoint Detection Demo"
input_width = config.IMAGE_SIZE[1]
input_height = config.IMAGE_SIZE[0]
vc = init_camera(camera_width, camera_height)
pool = Pool(processes=1, initializer=nn.init)
result = None
fps = 1.0
q_save = Queue()
q_show = Queue()
grabbed, camera_img = vc.read()
q_show.put(camera_img.copy())
input_img = camera_img.copy()
while True:
m1 = MyTime("1 loop of while(1) of main()")
pool_result = pool.apply_async(_run_inference, (input_img, ))
is_first = True
while True:
grabbed, camera_img = vc.read()
if is_first:
input_img = camera_img.copy()
is_first = False
q_save.put(camera_img.copy())
if not q_show.empty():
window_img = q_show.get()
drawed_img = window_img
if result is not None:
drawed_img = visualize_keypoint_detection(
window_img, result[0], (input_height, input_width))
drawed_img = add_fps(drawed_img, fps)
cv2.imshow(window_name, drawed_img)
key = cv2.waitKey(2) # Wait for 2ms
# TODO(yang): Consider using another key for abort.
if key == 27: # ESC to quit
return
# TODO(yang): Busy loop is not efficient here. Improve it and change them in other tasks.
if pool_result.ready():
break
q_show = clear_queue(q_show)
q_save, q_show = swap_queue(q_save, q_show)
result, fps = pool_result.get()
m1.show()
def show_keypoint_detection(img, result, fps, window_height, window_width,
config):
window_img = resize(img, size=[window_height, window_width])
window_img = visualize_keypoint_detection(window_img, result[0],
(input_height, input_width))
window_img = add_fps(window_img, fps)
window_name = "Keypoint Detection Demo"
cv2.imshow(window_name, window_img)
def run_sementic_segmentation(config):
global nn
camera_width = 320
camera_height = 240
window_name = "Segmentation Demo"
vc = init_camera(camera_width, camera_height)
pool = Pool(processes=1, initializer=nn.init)
result = None
fps = 1.0
q_save = Queue()
q_show = Queue()
grabbed, camera_img = vc.read()
if not grabbed:
print("Frame is empty")
q_show.put(camera_img.copy())
input_img = camera_img.copy()
colormap = np.array(get_color_map(len(config['CLASSES'])), dtype=np.uint8)
while True:
m1 = MyTime("1 loop of while(1) of main()")
pool_result = pool.apply_async(_run_inference, (input_img, ))
is_first = True
while True:
grabbed, camera_img = vc.read()
if is_first:
input_img = camera_img.copy()
is_first = False
q_save.put(camera_img.copy())
if not q_show.empty():
window_img = q_show.get()
overlay_img = window_img
if result is not None:
seg_img = label_to_color_image(result, colormap)
seg_img = cv2.resize(seg_img,
dsize=(camera_width, camera_height))
overlay_img = cv2.addWeighted(window_img, 1, seg_img, 0.8,
0)
overlay_img = add_fps(overlay_img, fps)
cv2.imshow(window_name, overlay_img)
key = cv2.waitKey(2) # Wait for 2ms
if key == 27: # ESC to quit
return
if pool_result.ready():
break
q_show = clear_queue(q_show)
q_save, q_show = swap_queue(q_save, q_show)
result, fps = pool_result.get()
m1.show()
def show_semantic_segmentation(img, result, fps, window_height, window_width,
config):
orig_img = resize(img, size=[window_height, window_width])
seg_img = label_to_color_image(result, colormap)
seg_img = cv2.resize(seg_img, dsize=(window_width, window_height))
window_img = cv2.addWeighted(orig_img, 1, seg_img, 0.8, 0)
window_img = add_fps(window_img, fps)
window_name = "Semantic Segmentation Demo"
cv2.imshow(window_name, window_img)
def show_object_detection(img, result, fps, window_height, window_width,
config):
window_img = resize(img, size=[window_height, window_width])
input_width = config.IMAGE_SIZE[1]
input_height = config.IMAGE_SIZE[0]
window_img = add_rectangle(config.CLASSES, window_img, result,
(input_height, input_width))
img = add_fps(window_img, fps)
window_name = "Object Detection Demo"
cv2.imshow(window_name, window_img)
def show_semantic_segmentation(img, result, fps, window_height, window_width,
config):
orig_img = resize(img, size=[window_height, window_width])
colormap = np.array(get_color_map(len(config.CLASSES)), dtype=np.uint8)
seg_img = label_to_color_image(result, colormap)
seg_img = cv2.resize(seg_img, dsize=(window_width, window_height))
window_img = cv2.addWeighted(orig_img, 1, seg_img, 0.8, 0)
window_img = add_fps(window_img, fps)
window_name = "Semantic Segmentation Demo"
cv2.imshow(window_name, window_img)
def show_classification(img, result, fps, window_height, window_width, config):
window_img = resize(img, size=[window_height, window_width])
result_class = np.argmax(result, axis=1)
add_class_label(window_img,
text=str(result[0, result_class][0]),
font_scale=0.52,
dl_corner=(230, 230))
add_class_label(window_img,
text=config.CLASSES[result_class[0]],
font_scale=0.52,
dl_corner=(230, 210))
window_img = add_fps(window_img, fps)
window_name = "Classification Demo"
cv2.imshow(window_name, window_img)
