from ssd_v2 import SSD300v2 as SSD
input_shape = (640, 480, 3)
# Change this if you run with other classes than VOC
class_names = [
"background", "aeroplane", "bicycle", "bird", "boat", "bottle", "bus",
"car", "cat", "chair", "cow", "diningtable", "dog", "horse", "motorbike",
"person", "pottedplant", "sheep", "sofa", "train", "tvmonitor"
]
NUM_CLASSES = len(class_names)
model = SSD(input_shape, num_classes=NUM_CLASSES)
# Change this path if you want to use your own trained weights
model.load_weights('weights_SSD300.hdf5')
vid_test = VideoTest(class_names, model, input_shape)
# To test on webcam 0, remove the parameter (or change it to another number
# to test on that webcam)
#vid_test.run(0)
for i in range(1):
print(i)
#vid_test.run('1.png') #doga_nogisaka.mp4')
#time.sleep(1.5)
#vid_test.run(0)
#vid_test.run('2.jpg')
#time.sleep(15)
#vid_test.run('3.jpg')
def frames():
video_path = 0
start_frame = 0
conf_thresh = 0.6
input_shape = (480,300,3)
class_names = ["background", "aeroplane", "bicycle", "bird", "boat", "bottle", "bus", "car", "cat", "chair", "cow", "diningtable", "dog", "horse", "motorbike", "person", "pottedplant", "sheep", "sofa", "train", "tvmonitor"]
NUM_CLASSES = len(class_names)
num_classes=NUM_CLASSES
class_colors = []
for i in range(0, num_classes):
hue = 255*i/num_classes
col = np.zeros((1,1,3)).astype("uint8")
col[0][0][0] = hue
col[0][0][1] = 128 # Saturation
col[0][0][2] = 255 # Value
cvcol = cv2.cvtColor(col, cv2.COLOR_HSV2BGR)
col = (int(cvcol[0][0][0]), int(cvcol[0][0][1]), int(cvcol[0][0][2]))
class_colors.append(col)
bbox_util = BBoxUtility(num_classes)
model = SSD(input_shape, num_classes=NUM_CLASSES)
model.load_weights('weights_SSD300.hdf5')
INTERVAL= 33 # 待ち時間
FRAME_RATE = 20 # fps
ORG_WINDOW_NAME = "org"
#GRAY_WINDOW_NAME = "gray"
#OUT_FILE_NAME = "real_SSD_result.mp4"
vid = cv2.VideoCapture(Camera.video_source)
width, height = input_shape[0], input_shape[1] #input_shape
"""
out = cv2.VideoWriter(OUT_FILE_NAME, \
cv_fourcc('M', 'P', '4', 'V'), \
FRAME_RATE, \
(width, height), \
True)
"""
if not vid.isOpened():
raise IOError(("Couldn't open video file or webcam. If you're "
"trying to open a webcam, make sure you video_path is an integer!"))
vidw = vid.get(cv2.CAP_PROP_FRAME_WIDTH)
vidh = vid.get(cv2.CAP_PROP_FRAME_HEIGHT)
vidar = vidw/vidh
"""
if start_frame > 0:
vid.set(cv2.CAP_PROP_POS_MSEC, start_frame)
"""
accum_time = 0
curr_fps = 0
fps = "FPS: ??"
prev_time = timer()
start_time=prev_time
#cv2.namedWindow(ORG_WINDOW_NAME)
while True:
retval, orig_image = vid.read()
if not retval:
print("Done!")
return
im_size = (input_shape[1], input_shape[0])
resized = cv2.resize(orig_image, im_size)
rgb = cv2.cvtColor(resized, cv2.COLOR_BGR2RGB)
to_draw = cv2.resize(resized, (int(input_shape[1]*vidar), input_shape[0]))
inputs = [image.img_to_array(rgb)] #rgb
tmp_inp = np.array(inputs)
x = preprocess_input(tmp_inp)
y = model.predict(x)
results = bbox_util.detection_out(y)
if len(results) > 0 and len(results[0]) > 0:
det_label = results[0][:, 0]
det_conf = results[0][:, 1]
det_xmin = results[0][:, 2]
det_ymin = results[0][:, 3]
det_xmax = results[0][:, 4]
det_ymax = results[0][:, 5]
top_indices = [i for i, conf in enumerate(det_conf) if conf >= conf_thresh]
top_conf = det_conf[top_indices]
top_label_indices = det_label[top_indices].tolist()
top_xmin = det_xmin[top_indices]
top_ymin = det_ymin[top_indices]
top_xmax = det_xmax[top_indices]
top_ymax = det_ymax[top_indices]
for i in range(top_conf.shape[0]):
xmin = int(round(top_xmin[i] * to_draw.shape[1]))
ymin = int(round(top_ymin[i] * to_draw.shape[0]))
xmax = int(round(top_xmax[i] * to_draw.shape[1]))
ymax = int(round(top_ymax[i] * to_draw.shape[0]))
class_num = int(top_label_indices[i])
cv2.rectangle(to_draw, (xmin, ymin), (xmax, ymax),
class_colors[class_num], 2) #to_draw
text = class_names[class_num] + " " + ('%.2f' % top_conf[i])
text_top = (xmin, ymin-10)
text_bot = (xmin + 80, ymin + 5)
text_pos = (xmin + 5, ymin)
cv2.rectangle(to_draw, text_top, text_bot, class_colors[class_num], -1) #to_draw
cv2.putText(to_draw, text, text_pos, cv2.FONT_HERSHEY_SIMPLEX, 0.35, (0,0,0), 1) #to_draw
print(text," ")
curr_time = timer()
exec_time = curr_time - prev_time
prev_time = curr_time
accum_time = accum_time + exec_time
curr_fps = curr_fps + 1
if accum_time > 1:
accum_time = accum_time - 1
fps = "FPS: " + str(curr_fps)
curr_fps = 0
cv2.rectangle(to_draw, (0,0), (50, 17), (255,255,255), -1) #to_draw
cv2.putText(to_draw, fps, (3,10), cv2.FONT_HERSHEY_SIMPLEX, 0.35, (0,0,0), 1) #to_draw
#yield cv2.imencode('.jpg', to_draw)[1].tobytes()
to_draw = cv2.resize(to_draw, (int(input_shape[0]*1), input_shape[1]))
#cv2.imshow(ORG_WINDOW_NAME, to_draw) #to_draw
#out.write(to_draw) #add to_draw
if cv2.waitKey(INTERVAL)>= 0: # & 0xFF == ord('q'):
break
#elif curr_time-start_time>=60:
# break
yield cv2.imencode('.jpg', to_draw)[1].tobytes()
vid.release() #add
#out.release() #add
cv2.destroyAllWindows() #add
from utils.videotest import VideoTest
import sys
sys.path.append("..")
# from ssd import SSD300 as SSD
from ssd_v2 import SSD300v2 as SSD
input_shape = (300, 300, 3)
# Change this if you run with other classes than VOC
class_names = [
"background", "aeroplane", "bicycle", "bird", "boat", "bottle", "bus",
"car", "cat", "chair", "cow", "diningtable", "dog", "horse", "motorbike",
"person", "pottedplant", "sheep", "sofa", "train", "tvmonitor"
]
NUM_CLASSES = len(class_names)
model = SSD(input_shape, num_classes=NUM_CLASSES)
# Change this path if you want to use your own trained weights
model.load_weights('../data/weights_SSD300.hdf5')
vid_test = VideoTest(class_names, model, input_shape)
# To test on webcam 0, remove the parameter (or change it to another number
# to test on that webcam)
vid_test.run('path/to/your/video.mkv')
