def main(_argv):
if FLAGS.tiny:
STRIDES = np.array(cfg.YOLO.STRIDES_TINY)
ANCHORS = utils.get_anchors(cfg.YOLO.ANCHORS_TINY, FLAGS.tiny)
else:
STRIDES = np.array(cfg.YOLO.STRIDES)
if FLAGS.model == 'yolov4':
ANCHORS = utils.get_anchors(cfg.YOLO.ANCHORS, FLAGS.tiny)
else:
ANCHORS = utils.get_anchors(cfg.YOLO.ANCHORS_V3, FLAGS.tiny)
NUM_CLASS = len(utils.read_class_names(cfg.YOLO.CLASSES))
XYSCALE = cfg.YOLO.XYSCALE
input_size = FLAGS.size
video_path = FLAGS.video
print("Video from: ", video_path)
vid = cv2.VideoCapture(video_path)
if FLAGS.framework == 'tf':
input_layer = tf.keras.layers.Input([input_size, input_size, 3])
if FLAGS.tiny:
feature_maps = YOLOv3_tiny(input_layer, NUM_CLASS)
bbox_tensors = []
for i, fm in enumerate(feature_maps):
bbox_tensor = decode(fm, NUM_CLASS, i)
bbox_tensors.append(bbox_tensor)
model = tf.keras.Model(input_layer, bbox_tensors)
utils.load_weights_tiny(model, FLAGS.weights)
else:
if FLAGS.model == 'yolov3':
feature_maps = YOLOv3(input_layer, NUM_CLASS)
bbox_tensors = []
for i, fm in enumerate(feature_maps):
bbox_tensor = decode(fm, NUM_CLASS, i)
bbox_tensors.append(bbox_tensor)
model = tf.keras.Model(input_layer, bbox_tensors)
utils.load_weights_v3(model, FLAGS.weights)
elif FLAGS.model == 'yolov4':
feature_maps = YOLOv4(input_layer, NUM_CLASS)
bbox_tensors = []
for i, fm in enumerate(feature_maps):
bbox_tensor = decode(fm, NUM_CLASS, i)
bbox_tensors.append(bbox_tensor)
model = tf.keras.Model(input_layer, bbox_tensors)
if FLAGS.weights.split(".")[len(FLAGS.weights.split(".")) -
1] == "weights":
utils.load_weights(model, FLAGS.weights)
else:
model.load_weights(FLAGS.weights).expect_partial()
model.summary()
else:
# Load TFLite model and allocate tensors.
interpreter = tf.lite.Interpreter(model_path=FLAGS.weights)
interpreter.allocate_tensors()
# Get input and output tensors.
input_details = interpreter.get_input_details()
output_details = interpreter.get_output_details()
print(input_details)
print(output_details)
while True:
return_value, frame = vid.read()
if return_value:
frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
image = Image.fromarray(frame)
else:
raise ValueError("No image! Try with another video format")
frame_size = frame.shape[:2]
image_data = utils.image_preprocess(np.copy(frame),
[input_size, input_size])
image_data = image_data[np.newaxis, ...].astype(np.float32)
prev_time = time.time()
if FLAGS.framework == 'tf':
pred_bbox = model.predict(image_data)
else:
interpreter.set_tensor(input_details[0]['index'], image_data)
interpreter.invoke()
pred_bbox = [
interpreter.get_tensor(output_details[i]['index'])
for i in range(len(output_details))
]
if FLAGS.model == 'yolov4':
pred_bbox = utils.postprocess_bbbox(pred_bbox, ANCHORS, STRIDES,
XYSCALE)
else:
pred_bbox = utils.postprocess_bbbox(pred_bbox, ANCHORS, STRIDES)
bboxes = utils.postprocess_boxes(pred_bbox, frame_size, input_size,
0.25)
bboxes = utils.nms(bboxes, 0.213, method='nms')
image = utils.draw_bbox(frame, bboxes)
curr_time = time.time()
exec_time = curr_time - prev_time
result = np.asarray(image)
info = "time: %.2f ms" % (1000 * exec_time)
print(info)
cv2.namedWindow("result", cv2.WINDOW_AUTOSIZE)
result = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
cv2.imshow("result", result)
if cv2.waitKey(1) & 0xFF == ord('q'): break
def main(_argv):
if FLAGS.tiny:
STRIDES = np.array(cfg.YOLO.STRIDES_TINY)
ANCHORS = utils.get_anchors(cfg.YOLO.ANCHORS_TINY, FLAGS.tiny)
else:
STRIDES = np.array(cfg.YOLO.STRIDES)
if FLAGS.model == 'yolov4':
ANCHORS = utils.get_anchors(cfg.YOLO.ANCHORS, FLAGS.tiny)
else:
ANCHORS = utils.get_anchors(cfg.YOLO.ANCHORS_V3, FLAGS.tiny)
NUM_CLASS = len(utils.read_class_names(cfg.YOLO.CLASSES))
XYSCALE = cfg.YOLO.XYSCALE
config = ConfigProto()
config.gpu_options.allow_growth = True
# session = InteractiveSession(config=config)
input_size = FLAGS.size
physical_devices = tf.config.experimental.list_physical_devices('GPU')
if len(physical_devices) > 0:
tf.config.experimental.set_memory_growth(physical_devices[0], True)
if FLAGS.framework == 'tf':
input_layer = tf.keras.layers.Input([input_size, input_size, 3])
if FLAGS.tiny:
feature_maps = YOLOv3_tiny(input_layer, NUM_CLASS)
bbox_tensors = []
for i, fm in enumerate(feature_maps):
bbox_tensor = decode(fm, NUM_CLASS, i)
bbox_tensors.append(bbox_tensor)
model = tf.keras.Model(input_layer, bbox_tensors)
utils.load_weights_tiny(model, FLAGS.weights)
else:
if FLAGS.model == 'yolov3':
feature_maps = YOLOv3(input_layer, NUM_CLASS)
bbox_tensors = []
for i, fm in enumerate(feature_maps):
bbox_tensor = decode(fm, NUM_CLASS, i)
bbox_tensors.append(bbox_tensor)
model = tf.keras.Model(input_layer, bbox_tensors)
utils.load_weights_v3(model, FLAGS.weights)
elif FLAGS.model == 'yolov4':
feature_maps = YOLOv4(input_layer, NUM_CLASS)
bbox_tensors = []
for i, fm in enumerate(feature_maps):
bbox_tensor = decode(fm, NUM_CLASS, i)
bbox_tensors.append(bbox_tensor)
model = tf.keras.Model(input_layer, bbox_tensors)
utils.load_weights(model, FLAGS.weights)
elif FLAGS.framework == 'trt':
saved_model_loaded = tf.saved_model.load(FLAGS.weights,
tags=[tag_constants.SERVING])
signature_keys = list(saved_model_loaded.signatures.keys())
print(signature_keys)
infer = saved_model_loaded.signatures['serving_default']
logging.info('weights loaded')
@tf.function
def run_model(x):
return model(x)
# Test the TensorFlow Lite model on random input data.
sum = 0
original_image = cv2.imread(FLAGS.image)
original_image = cv2.cvtColor(original_image, cv2.COLOR_BGR2RGB)
original_image_size = original_image.shape[:2]
image_data = utils.image_preprocess(np.copy(original_image),
[FLAGS.size, FLAGS.size])
image_data = image_data[np.newaxis, ...].astype(np.float32)
img_raw = tf.image.decode_image(open(FLAGS.image, 'rb').read(), channels=3)
img_raw = tf.expand_dims(img_raw, 0)
img_raw = tf.image.resize(img_raw, (FLAGS.size, FLAGS.size))
batched_input = tf.constant(image_data)
for i in range(1000):
prev_time = time.time()
# pred_bbox = model.predict(image_data)
if FLAGS.framework == 'tf':
pred_bbox = []
result = run_model(image_data)
for value in result:
value = value.numpy()
pred_bbox.append(value)
if FLAGS.model == 'yolov4':
pred_bbox = utils.postprocess_bbbox(pred_bbox, ANCHORS,
STRIDES, XYSCALE)
else:
pred_bbox = utils.postprocess_bbbox(pred_bbox, ANCHORS,
STRIDES)
bboxes = utils.postprocess_boxes(pred_bbox, original_image_size,
input_size, 0.25)
bboxes = utils.nms(bboxes, 0.213, method='nms')
elif FLAGS.framework == 'trt':
pred_bbox = []
result = infer(batched_input)
for key, value in result.items():
value = value.numpy()
pred_bbox.append(value)
if FLAGS.model == 'yolov4':
pred_bbox = utils.postprocess_bbbox(pred_bbox, ANCHORS,
STRIDES, XYSCALE)
else:
pred_bbox = utils.postprocess_bbbox(pred_bbox, ANCHORS,
STRIDES)
bboxes = utils.postprocess_boxes(pred_bbox, original_image_size,
input_size, 0.25)
bboxes = utils.nms(bboxes, 0.213, method='nms')
# pred_bbox = pred_bbox.numpy()
curr_time = time.time()
exec_time = curr_time - prev_time
if i == 0: continue
sum += (1 / exec_time)
info = str(i) + " time:" + str(round(
exec_time, 3)) + " average FPS:" + str(round(
sum / i, 2)) + ", FPS: " + str(round((1 / exec_time), 1))
print(info)
predict_result_path = os.path.join(predicted_dir_path,
str(num) + '.txt')
# Predict Process
image_size = image.shape[:2]
image_data = utils.image_preporcess(np.copy(image),
[INPUT_SIZE, INPUT_SIZE])
image_data = image_data[np.newaxis, ...].astype(np.float32)
pred_bbox = model.predict(image_data)
pred_bbox = [p for i, p in enumerate(pred_bbox) if i % 2 == 1]
pred_bbox = [tf.reshape(x, (-1, tf.shape(x)[-1])) for x in pred_bbox]
pred_bbox = tf.concat(pred_bbox, axis=0)
bboxes = utils.postprocess_boxes(pred_bbox, image_size, INPUT_SIZE,
cfg.TEST.SCORE_THRESHOLD)
bboxes = utils.nms(bboxes, cfg.TEST.IOU_THRESHOLD, method='nms')
if cfg.TEST.DECTECTED_IMAGE_PATH is not None:
image = utils.draw_bbox(image, bboxes)
cv2.imwrite(cfg.TEST.DECTECTED_IMAGE_PATH + image_name, image)
with open(predict_result_path, 'w') as f:
for bbox in bboxes:
coor = np.array(bbox[:4], dtype=np.int32)
score = bbox[4]
class_ind = int(bbox[5])
class_name = CLASSES[class_ind]
score = '%.4f' % score
xmin, ymin, xmax, ymax = list(map(str, coor))
bbox_mess = ' '.join(
[class_name, score, xmin, ymin, xmax, ymax]) + '\n'
prev_time = time.time()
if FLAGS.framework == 'tf':
pred_bbox = model.predict(image_data)
else:
interpreter.set_tensor(input_details[0]['index'], image_data)
interpreter.invoke()
pred_bbox = [interpreter.get_tensor(output_details[i]['index']) for i in range(len(output_details))]
if FLAGS.model == 'yolov4':
pred_bbox = utils.postprocess_bbbox(pred_bbox, ANCHORS, STRIDES, XYSCALE)
else:
pred_bbox = utils.postprocess_bbbox(pred_bbox, ANCHORS, STRIDES)
bboxes = utils.postprocess_boxes(pred_bbox, frame_size, input_size, 0.25)
bboxes = utils.nms(bboxes, 0.213, method='nms')
image = utils.draw_bbox(frame, bboxes)
curr_time = time.time()
exec_time = curr_time - prev_time
result = np.asarray(image)
info = "time: %.2f ms" %(1000*exec_time)
print(info)
cv2.namedWindow("result", cv2.WINDOW_AUTOSIZE)
result = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
cv2.imshow("result", result)
if cv2.waitKey(1) & 0xFF == ord('q'): break
if __name__ == '__main__':
try:
app.run(main)
def main(_argv):
if FLAGS.tiny:
STRIDES = np.array(cfg.YOLO.STRIDES_TINY)
ANCHORS = utils.get_anchors(cfg.YOLO.ANCHORS_TINY, FLAGS.tiny)
XYSCALE = cfg.YOLO.XYSCALE_TINY
else:
STRIDES = np.array(cfg.YOLO.STRIDES)
if FLAGS.model == 'yolov4':
ANCHORS = utils.get_anchors(cfg.YOLO.ANCHORS, FLAGS.tiny)
else:
ANCHORS = utils.get_anchors(cfg.YOLO.ANCHORS_V3, FLAGS.tiny)
XYSCALE = cfg.YOLO.XYSCALE
NUM_CLASS = len(utils.read_class_names(cfg.YOLO.CLASSES))
input_size = FLAGS.size
image_path = FLAGS.image
original_image = cv2.imread(image_path)
original_image = cv2.cvtColor(original_image, cv2.COLOR_BGR2RGB)
original_image_size = original_image.shape[:2]
image_data = utils.image_preprocess(np.copy(original_image), [input_size, input_size])
image_data = image_data[np.newaxis, ...].astype(np.float32)
if FLAGS.framework == 'tf':
input_layer = tf.keras.layers.Input([input_size, input_size, 3])
if FLAGS.tiny:
if FLAGS.model == 'yolov3':
feature_maps = YOLOv3_tiny(input_layer, NUM_CLASS)
else:
feature_maps = YOLOv4_tiny(input_layer, NUM_CLASS)
bbox_tensors = []
for i, fm in enumerate(feature_maps):
bbox_tensor = decode(fm, NUM_CLASS, i)
bbox_tensors.append(bbox_tensor)
model = tf.keras.Model(input_layer, bbox_tensors)
model.summary()
utils.load_weights_tiny(model, FLAGS.weights, FLAGS.model)
else:
if FLAGS.model == 'yolov3':
feature_maps = YOLOv3(input_layer, NUM_CLASS)
bbox_tensors = []
for i, fm in enumerate(feature_maps):
bbox_tensor = decode(fm, NUM_CLASS, i)
bbox_tensors.append(bbox_tensor)
model = tf.keras.Model(input_layer, bbox_tensors)
utils.load_weights_v3(model, FLAGS.weights)
elif FLAGS.model == 'yolov4':
feature_maps = YOLOv4(input_layer, NUM_CLASS)
bbox_tensors = []
for i, fm in enumerate(feature_maps):
bbox_tensor = decode(fm, NUM_CLASS, i)
bbox_tensors.append(bbox_tensor)
model = tf.keras.Model(input_layer, bbox_tensors)
if FLAGS.weights.split(".")[len(FLAGS.weights.split(".")) - 1] == "weights":
utils.load_weights(model, FLAGS.weights)
else:
model.load_weights(FLAGS.weights).expect_partial()
model.summary()
pred_bbox = model.predict(image_data)
else:
# Load TFLite model and allocate tensors.
interpreter = tf.lite.Interpreter(model_path=FLAGS.weights)
interpreter.allocate_tensors()
# Get input and output tensors.
input_details = interpreter.get_input_details()
output_details = interpreter.get_output_details()
print(input_details)
print(output_details)
interpreter.set_tensor(input_details[0]['index'], image_data)
interpreter.invoke()
pred_bbox = [interpreter.get_tensor(output_details[i]['index']) for i in range(len(output_details))]
if FLAGS.model == 'yolov4':
if FLAGS.tiny:
pred_bbox = utils.postprocess_bbbox(pred_bbox, ANCHORS, STRIDES, XYSCALE)
else:
pred_bbox = utils.postprocess_bbbox(pred_bbox, ANCHORS, STRIDES, XYSCALE)
else:
pred_bbox = utils.postprocess_bbbox(pred_bbox, ANCHORS, STRIDES)
bboxes = utils.postprocess_boxes(pred_bbox, original_image_size, input_size, 0.25)
bboxes = utils.nms(bboxes, 0.213, method='nms')
image = utils.draw_bbox(original_image, bboxes)
image = Image.fromarray(image)
image.show()
image = cv2.cvtColor(np.array(image), cv2.COLOR_BGR2RGB)
cv2.imwrite(FLAGS.output, image)
model = tf.keras.models.load_model('SavedModel/YOLOv3_model')
image = cv2.imread(path_to_image)
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
image_size = image.shape[:2]
image_data = utils.image_preporcess(np.copy(image),
[INPUT_SIZE, INPUT_SIZE])
image_data = image_data[np.newaxis,
...].astype(np.float32) # (1, width, height, 3)
pred_bbox = model.predict(image_data)
pred_bbox = [tf.reshape(x, (-1, tf.shape(x)[-1])) for x in pred_bbox]
pred_bbox = tf.concat(pred_bbox, axis=0)
bboxes = utils.postprocess_boxes(pred_bbox, image_size, INPUT_SIZE,
SCORE_THRESHOLD)
bboxes = utils.nms(bboxes, IOU_THRESHOLD, method='nms')
image_with_detections = utils.draw_bbox(image, bboxes)
image_with_detections = cv2.cvtColor(image_with_detections,
cv2.COLOR_RGB2BGR)
print('image_size = ', image_size)
# If image size is too big then resize the image for display purposes
display_shape = (image_size[1], image_size[0])
if image_size[0] > 1500 or image_size[1] > 1500:
display_shape = (int(image_size[1] / 4), int(image_size[0] / 4))
image_with_detections = cv2.resize(image_with_detections, display_shape)
cv2.imshow('detections', image_with_detections)
cv2.waitKey(0)
def predict(self,
image_path,
result_dir='.',
save_img=True,
image_name=None):
try:
if not (os.path.exists(image_path)):
print('No such file or directory', image_path)
#return None
else:
original_image = cv2.imread(image_path)
print('Shape1', original_image.shape)
except:
original_image = image_path
print('Shape2', original_image.shape)
if self.tiny:
STRIDES = np.array(cfg.YOLO.STRIDES_TINY)
ANCHORS = utils.get_anchors(cfg.YOLO.ANCHORS_TINY, self.tiny)
XYSCALE = cfg.YOLO.XYSCALE_TINY
else:
STRIDES = np.array(cfg.YOLO.STRIDES)
if self.model == 'yolov4':
ANCHORS = utils.get_anchors(cfg.YOLO.ANCHORS, self.tiny)
else:
ANCHORS = utils.get_anchors(cfg.YOLO.ANCHORS_V3, self.tiny)
XYSCALE = cfg.YOLO.XYSCALE
input_size = self.size
try:
#print('image:',original_image)
original_image = cv2.cvtColor(original_image, cv2.COLOR_BGR2RGB)
original_image_size = original_image.shape[:2]
except:
return pd.DataFrame()
image_data = utils.image_preprocess(
np.copy(original_image),
[self.size, self.size]) #[input_size, input_size])
image_data = image_data[np.newaxis, ...].astype(np.float32)
if self.framework == 'tf':
model = self.instanciated_model
#model.summary()
pred_bbox = model.predict(image_data)
else:
interpreter = self.instanciated_model
# Get input and output tensors.
input_details = interpreter.get_input_details()
output_details = interpreter.get_output_details()
print(input_details)
print(output_details)
interpreter.set_tensor(input_details[0]['index'], image_data)
interpreter.invoke()
pred_bbox = [
interpreter.get_tensor(output_details[i]['index'])
for i in range(len(output_details))
]
if self.model == 'yolov4':
if self.tiny:
pred_bbox = utils.postprocess_bbbox(pred_bbox,
ANCHORS,
STRIDES,
XYSCALE,
RESIZE=1.5)
else:
pred_bbox = utils.postprocess_bbbox(pred_bbox, ANCHORS,
STRIDES, XYSCALE)
else:
pred_bbox = utils.postprocess_bbbox(pred_bbox, ANCHORS, STRIDES)
bboxes = utils.postprocess_boxes(pred_bbox, original_image_size,
input_size, 0.25)
bboxes = utils.nms(bboxes, 0.213, method='nms')
image = utils.draw_bbox(original_image, bboxes)
image = Image.fromarray(image)
#image.show()
classes = utils.read_class_names(cfg.YOLO.CLASSES)
list_bboxes = []
for i, bbox in enumerate(bboxes):
coor = np.array(bbox[:4], dtype=np.int32)
score = bbox[4]
class_ind = int(bbox[5])
#print('type bbox',type(bbox))
#print('bbox',bbox[:4])
#print('coor',list(coor))
bbox_info = {
'coor': list(coor),
'probability': score,
'class': classes[class_ind]
}
list_bboxes.append(bbox_info)
output_name = os.path.join(result_dir + '/out_' + str(image_name) +
'.jpg')
if save_img:
image.save(output_name)
#cv2.imwrite(output_name,img)
print('Img saved to', output_name)
try:
output_name = os.path.join(result_dir + '/out_' +
os.path.basename(image_path))
if save_img:
image.save(output_name)
#cv2.imwrite(output_name,img)
print('Img saved to', output_name)
output = pd.DataFrame(list_bboxes)
#print('image_path',image_path )
output_name = '.'.join(output_name.split('.')[:2]) + '.xlsx'
#output_name = 'results/out_'+image_path.split('\\')[-1].split('.')[0]+'.xlsx'
print('Result file saved to', output_name)
output.to_excel(output_name)
return output
except Exception as e:
print(e)
return pd.DataFrame()
#yolo = YoloV4()
#yolo.predict('1fc35a5149379fff131e939f18257341.7.jpeg')
# Working Class
# =============================================================================
# class YoloV4:
#
# def __init__(self,framework = 'tf', weights=os.path.join(Path(os.path.realpath(__file__)).parent,'data/yolov4.weights'),size=608,tiny=False,model='yolov4'):
# self.framework = framework
# self.weights = weights
# self.size = size
# self.tiny = tiny
# self.model = model
# self.instanciated_model = None
#
# # Instanciate model
#
# print('Tiny ',self.tiny)
#
# #image_path = self.image
# NUM_CLASS = len(utils.read_class_names(cfg.YOLO.CLASSES))
# input_size = self.size
# if self.framework == 'tf':
# input_layer = tf.keras.layers.Input([input_size, input_size, 3])
# if self.tiny:
# if self.model == 'yolov3':
# feature_maps = YOLOv3_tiny(input_layer, NUM_CLASS)
# else:
# feature_maps = YOLOv4_tiny(input_layer, NUM_CLASS)
# bbox_tensors = []
# for i, fm in enumerate(feature_maps):
# bbox_tensor = decode(fm, NUM_CLASS, i)
# bbox_tensors.append(bbox_tensor)
# model = tf.keras.Model(input_layer, bbox_tensors)
# model.summary()
# utils.load_weights_tiny(model, self.weights, self.model)
# else:
# if self.model == 'yolov3':
# feature_maps = YOLOv3(input_layer, NUM_CLASS)
# bbox_tensors = []
# for i, fm in enumerate(feature_maps):
# bbox_tensor = decode(fm, NUM_CLASS, i)
# bbox_tensors.append(bbox_tensor)
# model = tf.keras.Model(input_layer, bbox_tensors)
# utils.load_weights_v3(model, self.weights)
# elif self.model == 'yolov4':
# feature_maps = YOLOv4(input_layer, NUM_CLASS)
# bbox_tensors = []
# for i, fm in enumerate(feature_maps):
# bbox_tensor = decode(fm, NUM_CLASS, i)
# bbox_tensors.append(bbox_tensor)
# model = tf.keras.Model(input_layer, bbox_tensors)
#
# if self.weights.split(".")[len(self.weights.split(".")) - 1] == "weights":
# utils.load_weights(model, self.weights)
# else:
# model.load_weights(self.weights).expect_partial()
#
# self.instanciated_model = model
#
# else:
# # Load TFLite model and allocate tensors.
# interpreter = tf.lite.Interpreter(model_path=self.weights)
# interpreter.allocate_tensors()
#
# self.instanciated_model = interpreter
#
#
# def predict(self,image_path,result_dir='results',save_img=True):
#
# if self.tiny:
# STRIDES = np.array(cfg.YOLO.STRIDES_TINY)
# ANCHORS = utils.get_anchors(cfg.YOLO.ANCHORS_TINY, self.tiny)
# XYSCALE = cfg.YOLO.XYSCALE_TINY
# else:
# STRIDES = np.array(cfg.YOLO.STRIDES)
# if self.model == 'yolov4':
# ANCHORS = utils.get_anchors(cfg.YOLO.ANCHORS, self.tiny)
# else:
# ANCHORS = utils.get_anchors(cfg.YOLO.ANCHORS_V3, self.tiny)
# XYSCALE = cfg.YOLO.XYSCALE
#
# input_size = self.size
#
# original_image = cv2.imread(image_path)
# print('image:',original_image)
# original_image = cv2.cvtColor(original_image, cv2.COLOR_BGR2RGB)
# original_image_size = original_image.shape[:2]
#
# image_data = utils.image_preprocess(np.copy(original_image), [self.size,self.size])#[input_size, input_size])
# image_data = image_data[np.newaxis, ...].astype(np.float32)
#
# if self.framework == 'tf':
# model = self.instanciated_model
# model.summary()
# pred_bbox = model.predict(image_data)
#
# else:
# interpreter = self.instanciated_model
#
# # Get input and output tensors.
# input_details = interpreter.get_input_details()
# output_details = interpreter.get_output_details()
# print(input_details)
# print(output_details)
#
# interpreter.set_tensor(input_details[0]['index'], image_data)
# interpreter.invoke()
# pred_bbox = [interpreter.get_tensor(output_details[i]['index']) for i in range(len(output_details))]
#
# if self.model == 'yolov4':
# if self.tiny:
# pred_bbox = utils.postprocess_bbbox(pred_bbox, ANCHORS, STRIDES, XYSCALE, RESIZE=1.5)
# else:
# pred_bbox = utils.postprocess_bbbox(pred_bbox, ANCHORS, STRIDES, XYSCALE)
# else:
# pred_bbox = utils.postprocess_bbbox(pred_bbox, ANCHORS, STRIDES)
# bboxes = utils.postprocess_boxes(pred_bbox, original_image_size, input_size, 0.25)
# bboxes = utils.nms(bboxes, 0.213, method='nms')
#
# image = utils.draw_bbox(original_image, bboxes)
# image = Image.fromarray(image)
# #image.show()
#
# print('Image path',image_path)
# print('Type Image path',type(image_path))
# print('Bboxes type',type(bboxes))
#
# classes = utils.read_class_names(cfg.YOLO.CLASSES)
# list_bboxes = []
#
# for i, bbox in enumerate(bboxes):
# coor = np.array(bbox[:4], dtype=np.int32)
# score = bbox[4]
# class_ind = int(bbox[5])
# #print('type bbox',type(bbox))
# #print('bbox',bbox[:4])
# #print('coor',list(coor))
# bbox_info = {'coor':list(coor),'probability':score,'class':classes[class_ind]}
# list_bboxes.append(bbox_info)
#
# try:
# output_name = os.path.join(result_dir+'/out_' + os.path.basename(image_path))
#
# if save_img:
# image.save(output_name)
# #cv2.imwrite(output_name,img)
# print('Img saved to',output_name)
#
# output = pd.DataFrame(list_bboxes)
# print('image_path',image_path )
# output_name = '.'.join(output_name.split('.')[:2])+'.xlsx'
# #output_name = 'results/out_'+image_path.split('\\')[-1].split('.')[0]+'.xlsx'
# print('output_name',output_name)
# output.to_excel(output_name)
#
# except Exception as e:
# print(e)
# =============================================================================
#yolo = YoloV4()
#yolo.predict('1fc35a5149379fff131e939f18257341.7.jpeg')
pb_file = "./saved_model.pb"
image_path = './test_images/' + files[100]
num_classes = 1
input_size = 416
graph = tf.Graph()
original_image = cv2.imread(image_path)
original_image = cv2.cvtColor(original_image, cv2.COLOR_BGR2RGB)
original_image_size = original_image.shape[:2]
image_data = utils.image_preporcess(np.copy(original_image), [input_size, input_size])
image_data = image_data[np.newaxis, ...]
return_tensors = utils.read_pb_return_tensors(graph, pb_file, return_elements)
with tf.Session(graph=graph) as sess:
pred_sbbox, pred_mbbox, pred_lbbox = sess.run(
[return_tensors[1], return_tensors[2], return_tensors[3]],
feed_dict={ return_tensors[0]: image_data})
pred_bbox = np.concatenate([np.reshape(pred_sbbox, (-1, 5 + num_classes)),
np.reshape(pred_mbbox, (-1, 5 + num_classes)),
np.reshape(pred_lbbox, (-1, 5 + num_classes))], axis=0)
bboxes = utils.postprocess_boxes(pred_bbox, original_image_size, input_size, 0.3) #score_threshold = 0.3
bboxes = utils.nms(bboxes, 0.45, method='nms') #iou_threshold = 0.45
image = utils.draw_bbox(original_image, bboxes)
image = Image.fromarray(image)
image
def main(video_path):
global end,init
return_elements = ["input/input_data:0", "pred_sbbox/concat_2:0", "pred_mbbox/concat_2:0", "pred_lbbox/concat_2:0"]
pb_file = "./yolov3_coco.pb"
num_classes = 80 # 使用原有训练权重，有80个
input_size = 416
graph = tf.Graph() # 计算图，表示实例化一个用于tensorflow计算和表示用的数据流图，不负责运行计算
return_tensors = utils.read_pb_return_tensors(graph, pb_file, return_elements)
framenumber = 1
junzhi=10
with tf.Session(graph=graph) as sess:
vid = cv2.VideoCapture(video_path) # 获取视频
init = 0
fourcc = cv2.VideoWriter_fourcc('X','V','I','D')
size = (int(vid.get(cv2.CAP_PROP_FRAME_WIDTH)), int(vid.get(cv2.CAP_PROP_FRAME_HEIGHT)))
out = cv2.VideoWriter(os.path.join(path_,'result2.avi'), fourcc, 30.0, size)
while (1):
return_value, frame = vid.read()
#frame = cv2.flip(frame, 1) #水平颠倒
framenumber = framenumber + 1
currentFrame = framenumber
if currentFrame % 6 != 0:
out.write(frame)
continue
if return_value:
J = dehaze.DeHaze(frame)
# frame = cv2.cvtColor(J, cv2.COLOR_BGR2RGB)
#image = Image.fromarray(J)
frame = J
else:
raise ValueError("No image!")
frame1 = Pedestrian_Detection.detect(frame) #识别行人
frame_size = frame.shape[:2]
image_data = utils.image_preporcess(np.copy(frame), [input_size, input_size])
image_data = image_data[np.newaxis, ...]
#prev_time = time.time()
pred_sbbox, pred_mbbox, pred_lbbox = sess.run(
[return_tensors[1], return_tensors[2], return_tensors[3]],
feed_dict={return_tensors[0]: image_data})
pred_bbox = np.concatenate([np.reshape(pred_sbbox, (-1, 5 + num_classes)),
np.reshape(pred_mbbox, (-1, 5 + num_classes)),
np.reshape(pred_lbbox, (-1, 5 + num_classes))], axis=0)
# np.concatenate numpy.concatenate((a1,a2,...), axis=0)函数。能够一次完成多个数组的拼接。其中a1,a2,...是拼接数组的名字
# np.reshape(a,newshape,order='C') a:数组——需要处理的数据 newshape:新的格式——整数或整数数组，如(2,3)表示2行3列。新的形状应该与原来的形状兼容，即行数和列数相乘后等于a中元素的数量。
bboxes = utils.postprocess_boxes(pred_bbox, frame_size, input_size,
0.3) # pred_bbox 预测的框架 frame_size 框架尺寸
bboxes = utils.nms(bboxes, 0.45, method='nms')
image,junzhi = utils.draw_bbox(frame1, bboxes, junzhi) # 绘制框
for i, bbox in enumerate(bboxes):
coor = np.array(bbox[:4], dtype=np.int32)
if coor[2]-coor[0] > junzhi*1.5:
winsound.Beep(600, 100)
#pygame.mixer.music.play(1)
out.write(image)
#result = np.asarray(image)
#cv2.namedWindow("result", cv2.WINDOW_AUTOSIZE)
#result = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
pilImage = Image.fromarray(image)
pilImage = pilImage.resize((700, 380), Image.ANTIALIAS)
tkImage = ImageTk.PhotoImage(image=pilImage)
bj.create_image(0, 0, anchor='nw', image=tkImage)
window.update_idletasks()
window.update()
str1.set('进行中')
if end == 1:
str1.set('欢迎使用')
end = 0
init = 1
window.update()
sess.close()
if cv2.waitKey(1) & 0xFF == ord('q'):
str1.set('欢迎使用')
window.update()
init = 1
break
str1.set('欢迎使用')
window.update()
init = 1
end = 0
return
def main(_argv):
#Yolo-tiny버전이 아닌지 if문을 통해 구분한다.
if FLAGS.tiny:
STRIDES = np.array(cfg.YOLO.STRIDES_TINY)
ANCHORS = utils.get_anchors(cfg.YOLO.ANCHORS_TINY, FLAGS.tiny)
else:
STRIDES = np.array(cfg.YOLO.STRIDES)
#tiny버전이 아닐 경우 Yolo-v4모델을 가져오고 anchor박스의 정보도 함께 가져온다.
if FLAGS.model == 'yolov4':
ANCHORS = utils.get_anchors(cfg.YOLO.ANCHORS, FLAGS.tiny)
else:
ANCHORS = utils.get_anchors(cfg.YOLO.ANCHORS_V3, FLAGS.tiny)
#클래스개수, 박스의 XYSCALE을 Yolo-v4의 cfg파일에서 불러오고 input_size와 image_path를 미리 정의한 flags객체의 size와 image값으로 정의한다.
NUM_CLASS = len(utils.read_class_names(cfg.YOLO.CLASSES))
XYSCALE = cfg.YOLO.XYSCALE
input_size = FLAGS.size
image_path = FLAGS.image
#cv2모듈을 통해 이미지를 불러오고 불러온 이미지를 BGR이미지를 RGB로 바꿔준다.
#이는 컬러 사진을 opencv에서는 BGR순서로 저장하는데 matplotlib에서는 RGB로 저장하기 때문이다.
original_image = cv2.imread(image_path)
original_image = cv2.cvtColor(original_image, cv2.COLOR_BGR2RGB)
original_image_size = original_image.shape[:2]
#이미지 데이터들을 배열로 바꿔주고 데이터타입을 float32로 변환해준다.
image_data = utils.image_preporcess(np.copy(original_image), [input_size, input_size])
image_data = image_data[np.newaxis, ...].astype(np.float32)
#framework가 tf로 정의된 경우 FLAGS.model이 어떻게 정의되었는지에 따라 불러오는 모델이 다르다.
#지금의 경우는 Yolo-v4를 다루고 있으므로 FLAGS.model이 yolov4로 정의된 경우만 보겠다.
if FLAGS.framework == 'tf':
input_layer = tf.keras.layers.Input([input_size, input_size, 3])
if FLAGS.tiny:
feature_maps = YOLOv3_tiny(input_layer, NUM_CLASS)
bbox_tensors = []
for i, fm in enumerate(feature_maps):
bbox_tensor = decode(fm, NUM_CLASS, i)
bbox_tensors.append(bbox_tensor)
model = tf.keras.Model(input_layer, bbox_tensors)
utils.load_weights_tiny(model, FLAGS.weights)
else:
if FLAGS.model == 'yolov3':
feature_maps = YOLOv3(input_layer, NUM_CLASS)
bbox_tensors = []
for i, fm in enumerate(feature_maps):
bbox_tensor = decode(fm, NUM_CLASS, i)
bbox_tensors.append(bbox_tensor)
model = tf.keras.Model(input_layer, bbox_tensors)
utils.load_weights_v3(model, FLAGS.weights)
#YOLOv4에 input 레이어와 클래스를 넣어주어 feature map을 생성하고 바운딩 박스를 예측하기 위한 리스트를 선언해준다.
#이후 반복문을 통해 예측된 바운딩박스의 좌표를 리스트에 넣어준 뒤 이것을 model에 input레이어와 함께 넣어 model을 생성해준다.
#그 다음 미리 학습된 weights값들을 load해온다.
elif FLAGS.model == 'yolov4':
feature_maps = YOLOv4(input_layer, NUM_CLASS)
bbox_tensors = []
for i, fm in enumerate(feature_maps):
bbox_tensor = decode(fm, NUM_CLASS, i)
bbox_tensors.append(bbox_tensor)
model = tf.keras.Model(input_layer, bbox_tensors)
utils.load_weights(model, FLAGS.weights)
model.summary()
#이후 원래 이미지 데이터에서 예측된 바운딩 박스를 표시해준다.
pred_bbox = model.predict(image_data)
else:.
interpreter = tf.lite.Interpreter(model_path=FLAGS.weights)
interpreter.allocate_tensors()
input_details = interpreter.get_input_details()
output_details = interpreter.get_output_details()
print(input_details)
print(output_details)
interpreter.set_tensor(input_details[0]['index'], image_data)
interpreter.invoke()
pred_bbox = [interpreter.get_tensor(output_details[i]['index']) for i in range(len(output_details))]
#이후 표시된 바운딩 박스 중 유효한 바운딩 박스들만 남기는 작업을 한 후 최종적으로 pred_bbox에 저장한다.
if FLAGS.model == 'yolov4':
pred_bbox = utils.postprocess_bbbox(pred_bbox, ANCHORS, STRIDES, XYSCALE)
else:
pred_bbox = utils.postprocess_bbbox(pred_bbox, ANCHORS, STRIDES)
bboxes = utils.postprocess_boxes(pred_bbox, original_image_size, input_size, 0.25)
bboxes = utils.nms(bboxes, 0.213, method='nms')
#cv2모듈을 사용하여 예측한 바운딩박스가 표시된 이미지를 출력한다.
image = utils.draw_bbox(original_image, bboxes)
image = Image.fromarray(image)
image.show()
def deal_data(conn, addr):
print('Accept new connection from {0}'.format(addr))
# conn.settimeout(500)
conn.send('Hi, Welcome to the server!'.encode("utf-8"))
while 1:
fileinfo_size = struct.calcsize('128sq')
buf = conn.recv(fileinfo_size)
if buf:
# filename, filesize = struct.unpack('128sq', buf)
filesize = buf
fn = 'rgb.jpg' #filename.strip(b"\x00").decode("utf-8")
new_filename = os.path.join('./', 'new_' + fn)
print(new_filename, filesize)
print('file new name is {0}, filesize if {1}'.format(
new_filename, filesize))
recvd_size = 0 # 定义已接收文件的大小
fp = open(new_filename, 'wb')
print('start receiving...')
while not recvd_size == filesize:
if filesize - recvd_size > 1024:
data = conn.recv(1024)
recvd_size += len(data)
else:
data = conn.recv(filesize - recvd_size)
recvd_size = filesize
fp.write(data)
fp.close()
print('end receive...')
tic = time.time()
original_image = cv2.imread(new_filename)
# rotate the image
height, width = original_image.shape[:2]
matRotate = cv2.getRotationMatrix2D((height * 0.5, width * 0.5),
-90, 1)
dst = cv2.warpAffine(original_image, matRotate,
(width, height * 2))
rows, cols = dst.shape[:2]
for col in range(0, cols):
if dst[:, col].any():
left = col
break
for col in range(cols - 1, 0, -1):
if dst[:, col].any():
right = col
break
for row in range(0, rows):
if dst[row, :].any():
up = row
break
for row in range(rows - 1, 0, -1):
if dst[row, :].any():
down = row
break
res_widths = abs(right - left)
res_heights = abs(down - up)
res = np.zeros([res_heights, res_widths, 3], np.uint8)
for res_width in range(res_widths):
for res_height in range(res_heights):
res[res_height, res_width] = dst[up + res_height,
left + res_width]
original_image = res
original_image = cv2.cvtColor(original_image, cv2.COLOR_BGR2RGB)
original_image_size = original_image.shape[:2]
image_data = utils.image_preporcess(np.copy(original_image),
[input_size, input_size])
image_data = image_data[np.newaxis, ...].astype(np.float32)
pred_bbox = model.predict(image_data)
pred_bbox = [
tf.reshape(x, (-1, tf.shape(x)[-1])) for x in pred_bbox
]
pred_bbox = tf.concat(pred_bbox, axis=0)
bboxes = utils.postprocess_boxes(pred_bbox, original_image_size,
input_size, 0.3)
bboxes = utils.nms(bboxes, 0.45, method='nms')
strbox = str(len(bboxes)) + ','
for i in range(0, len(bboxes)):
for j in range(0, 5):
strbox = strbox + str(bboxes[i][j]) + ','
strbox = strbox + classes[int(bboxes[i][5])] + ','
image = utils.draw_bbox(original_image, bboxes)
image = Image.fromarray(image)
image.show()
toc = time.time()
print(toc - tic)
conn.send(strbox.encode("utf-8"))
print(conn.recv(1024).decode('utf-8'))
conn.close()
break
def evaluate(model_path):
INPUT_SIZE = 416
NUM_CLASS = len(utils.read_class_names(cfg.YOLO.CLASSES))
CLASSES = utils.read_class_names(cfg.YOLO.CLASSES)
predicted_dir_path = './data/mAP/predicted'
ground_truth_dir_path = './data/mAP/ground-truth'
if os.path.exists(predicted_dir_path): shutil.rmtree(predicted_dir_path)
if os.path.exists(ground_truth_dir_path):
shutil.rmtree(ground_truth_dir_path)
if os.path.exists(cfg.TEST.DECTECTED_IMAGE_PATH):
shutil.rmtree(cfg.TEST.DECTECTED_IMAGE_PATH)
os.mkdir(predicted_dir_path)
os.mkdir(ground_truth_dir_path)
os.mkdir(cfg.TEST.DECTECTED_IMAGE_PATH)
# Build Model
model = yolov3.build_for_test()
model.load_weights(model_path)
# utils.load_weights(model, "./weight/yolov3-voc_10000.weights") # 加载利用darknet训练的权重文件，需要用utils.load_weights
print(model.summary())
with open(cfg.TEST.ANNOT_PATH, 'r') as annotation_file:
for num, line in enumerate(annotation_file):
annotation = line.strip().split()
image_path = annotation[0]
image_name = image_path.split('/')[-1]
image = cv2.imread(image_path)
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
bbox_data_gt = np.array(
[list(map(int, box.split(','))) for box in annotation[1:]])
if len(bbox_data_gt) == 0:
bboxes_gt = []
classes_gt = []
else:
bboxes_gt, classes_gt = bbox_data_gt[:, :4], bbox_data_gt[:, 4]
ground_truth_path = os.path.join(ground_truth_dir_path,
str(num) + '.txt')
print('=> ground truth of %s:' % image_name)
num_bbox_gt = len(bboxes_gt)
with open(ground_truth_path, 'w') as f:
for i in range(num_bbox_gt):
class_name = CLASSES[classes_gt[i]]
xmin, ymin, xmax, ymax = list(map(str, bboxes_gt[i]))
bbox_mess = ' '.join([class_name, xmin, ymin, xmax, ymax
]) + '\n'
f.write(bbox_mess)
print('\t' + str(bbox_mess).strip())
print('=> predict result of %s:' % image_name)
predict_result_path = os.path.join(predicted_dir_path,
str(num) + '.txt')
# Predict Process
image_size = image.shape[:2]
image_data = utils.image_preporcess(np.copy(image),
[INPUT_SIZE, INPUT_SIZE])
image_data = image_data[np.newaxis, ...].astype(np.float32)
pred_bbox = model.predict(image_data)
pred_bbox = [
tf.reshape(x, (-1, tf.shape(x)[-1])) for x in pred_bbox
]
pred_bbox = tf.concat(pred_bbox, axis=0)
bboxes = utils.postprocess_boxes(pred_bbox, image_size, INPUT_SIZE,
cfg.TEST.SCORE_THRESHOLD)
bboxes = utils.nms(bboxes, cfg.TEST.IOU_THRESHOLD, method='nms')
if cfg.TEST.DECTECTED_IMAGE_PATH is not None:
image = utils.draw_bbox(image, bboxes)
cv2.imwrite(cfg.TEST.DECTECTED_IMAGE_PATH + image_name, image)
with open(predict_result_path, 'w') as f:
for bbox in bboxes:
coor = np.array(bbox[:4], dtype=np.int32)
score = bbox[4]
class_ind = int(bbox[5])
class_name = CLASSES[class_ind]
score = '%.4f' % score
xmin, ymin, xmax, ymax = list(map(str, coor))
bbox_mess = ' '.join(
[class_name, score, xmin, ymin, xmax, ymax]) + '\n'
f.write(bbox_mess)
print('\t' + str(bbox_mess).strip())
def detection(vid):
with tf.Session(graph=graph) as sess:
return_value, frame = vid.read()
if return_value:
frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
image = Image.fromarray(frame)
else:
raise ValueError("No image!")
frame_size = frame.shape[:2]
image_data = utils.image_preporcess(np.copy(frame),
[input_size, input_size])
image_data = image_data[np.newaxis, ...]
prev_time = time.time()
pred_sbbox, pred_mbbox, pred_lbbox = sess.run(
[return_tensors[1], return_tensors[2], return_tensors[3]],
feed_dict={return_tensors[0]: image_data})
pred_bbox = np.concatenate([
np.reshape(pred_sbbox, (-1, 5 + num_classes)),
np.reshape(pred_mbbox, (-1, 5 + num_classes)),
np.reshape(pred_lbbox, (-1, 5 + num_classes))
],
axis=0)
bboxes = utils.postprocess_boxes(pred_bbox, frame_size, input_size,
0.3)
bboxes = utils.nms(bboxes, 0.45, method='nms')
image, detected = utils.draw_bbox(frame, bboxes)
detected = np.asarray(detected)
print("------- frame i ---------")
class_count = []
for i in range(len(obj_classes)): # 80
obj_count = 0
for j in range(len(detected)):
if int(detected[j][5]) == i: obj_count += 1
class_count = np.append(class_count, obj_count)
curr_time = time.time()
exec_time = curr_time - prev_time
result = np.asarray(image)
info = "time: %.2f ms" % (1000 * exec_time)
# cv2.namedWindow("result", cv2.WINDOW_AUTOSIZE)
result = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
return result, class_count
# if __name__ == "__main__":
# while True:
# result = detection(vid)
# cv2.imshow("result", result)
# if cv2.waitKey(1) & 0xFF == ord('q'): break
def main(_argv):
import os
os.environ["CUDA_VISIBLE_DEVICES"] = "0"
if FLAGS.tiny:
STRIDES = np.array(cfg.YOLO.STRIDES_TINY)
ANCHORS = utils.get_anchors(cfg.YOLO.ANCHORS_TINY, FLAGS.tiny)
else:
STRIDES = np.array(cfg.YOLO.STRIDES)
if FLAGS.model == 'yolov4':
ANCHORS = utils.get_anchors(cfg.YOLO.ANCHORS, FLAGS.tiny)
else:
ANCHORS = utils.get_anchors(cfg.YOLO.ANCHORS_V3, FLAGS.tiny)
NUM_CLASS = len(utils.read_class_names(cfg.YOLO.CLASSES))
XYSCALE = cfg.YOLO.XYSCALE
input_size = FLAGS.size
if FLAGS.framework == 'tf':
input_layer = tf.keras.layers.Input([input_size, input_size, 3])
if FLAGS.tiny:
feature_maps = YOLOv3_tiny(input_layer, NUM_CLASS)
bbox_tensors = []
for i, fm in enumerate(feature_maps):
bbox_tensor = decode(fm, NUM_CLASS, i)
bbox_tensors.append(bbox_tensor)
model = tf.keras.Model(input_layer, bbox_tensors)
utils.load_weights_tiny(model, FLAGS.weights)
else:
if FLAGS.model == 'yolov3':
feature_maps = YOLOv3(input_layer, NUM_CLASS)
bbox_tensors = []
for i, fm in enumerate(feature_maps):
bbox_tensor = decode(fm, NUM_CLASS, i)
bbox_tensors.append(bbox_tensor)
model = tf.keras.Model(input_layer, bbox_tensors)
utils.load_weights_v3(model, FLAGS.weights)
elif FLAGS.model == 'yolov4':
feature_maps = YOLOv4(input_layer, NUM_CLASS)
bbox_tensors = []
for i, fm in enumerate(feature_maps):
bbox_tensor = decode(fm, NUM_CLASS, i)
bbox_tensors.append(bbox_tensor)
model = tf.keras.Model(input_layer, bbox_tensors)
utils.load_weights(model, FLAGS.weights)
model.summary()
else:
# Load TFLite model and allocate tensors.
interpreter = tf.lite.Interpreter(model_path=FLAGS.weights)
interpreter.allocate_tensors()
# Get input and output tensors.
input_details = interpreter.get_input_details()
output_details = interpreter.get_output_details()
print(input_details)
print(output_details)
while True:
frames = pipeline.wait_for_frames()
depth_frame = frames.get_depth_frame()
# Align the depth frame to color frame
aligned_frames = align.process(frames)
# Get aligned frames
depth_frame = aligned_frames.get_depth_frame()
color_frame = aligned_frames.get_color_frame()
if not depth_frame or not color_frame:
continue
depth_intrin = depth_frame.profile.as_video_stream_profile().intrinsics
color_intrin = color_frame.profile.as_video_stream_profile().intrinsics
depth_to_color_extrin = depth_frame.profile.get_extrinsics_to(
color_frame.profile)
depth_image = np.asanyarray(depth_frame.get_data())
color_image = np.asanyarray(color_frame.get_data())
frame = cv2.cvtColor(color_image, cv2.COLOR_BGR2RGB)
image = Image.fromarray(frame)
frame_size = frame.shape[:2]
image_data = utils.image_preprocess(np.copy(frame),
[input_size, input_size])
image_data = image_data[np.newaxis, ...].astype(np.float32)
prev_time = time.time()
scaled_depth = cv2.convertScaleAbs(depth_image, alpha=0.08)
depth_colormap = cv2.applyColorMap(scaled_depth, cv2.COLORMAP_JET)
if FLAGS.framework == 'tf':
pred_bbox = model.predict(image_data)
else:
interpreter.set_tensor(input_details[0]['index'], image_data)
interpreter.invoke()
pred_bbox = [
interpreter.get_tensor(output_details[i]['index'])
for i in range(len(output_details))
]
if FLAGS.model == 'yolov4':
pred_bbox = utils.postprocess_bbbox(pred_bbox, ANCHORS, STRIDES,
XYSCALE)
else:
pred_bbox = utils.postprocess_bbbox(pred_bbox, ANCHORS, STRIDES)
bboxes = utils.postprocess_boxes(pred_bbox, frame_size, input_size,
0.25)
bboxes = utils.nms(bboxes, 0.213, method='nms')
view2d = np.zeros((480, 640, 3), np.uint8)
for box in bboxes:
x_mid = int((box[0] + box[2]) / 2)
y_mid = int((box[1] + box[3]) / 2)
pixel_depths = []
for i in range(3):
for j in range(3):
pixel_depths.append(
depth_frame.get_distance(int(x_mid + i - 1),
int(y_mid + j - 1)))
object_depth = statistics.median(pixel_depths)
object_point = rs.rs2_deproject_pixel_to_point(
depth_intrin, [x_mid, y_mid], object_depth)
if box[5] == 67.0:
print('found phone')
if object_depth == 0.0:
print('depth not found')
depth_colormap[max(0, min(y_mid, 479)),
max(0, min(x_mid, 639))] = [0, 255, 0]
view2d[max(0, min(480 - int(object_point[2] * 350), 479)),
max(0, min(int(object_point[0] * 350) +
320, 639))] = [0, 255, 0]
#print('x_min', box[0])
#print('y_min', box[1])
#print('x_max', box[2])
#print('y_max', box[3])
#print('probability', box[4])
#print('object_id', box[5])
#print('point', object_point)
#print('-----')
#curr_time = time.time()
#exec_time = curr_time - prev_time
#info = "time: %.2f ms" %(1000*exec_time)
#print(info)
cv2.namedWindow("result", cv2.WINDOW_AUTOSIZE)
image_color = utils.draw_bbox(frame, bboxes)
result = cv2.cvtColor(image_color, cv2.COLOR_RGB2BGR)
image_depth = utils.draw_bbox(depth_colormap, bboxes)
images = np.hstack((view2d, image_depth))
cv2.imshow("result", images)
print('-----')
if cv2.waitKey(1) & 0xFF == ord('q'):
pipeline.stop()
break
def detect_inference(self):
self.update_process_message.emit('Detection Process')
graph = tf.Graph()
detections = []
return_tensors = utils.read_pb_return_tensors(
graph, self.window.pb_file, self.window.return_elements)
# 创建进度条
pbar = tqdm(total=self.window.total_frame_counter)
with tf.Session(graph=graph) as sess:
if self.window.writeVideo_flag:
isOutput = True if self.window.output_path != "" else False
if isOutput:
video_FourCC = cv2.VideoWriter_fourcc(*'MPEG')
out = cv2.VideoWriter(
self.window.output_path, video_FourCC, self.window.media_fps, self.window.media_size)
list_file = open('detection.txt', 'w')
frame_index = -1
while True:
while not self.window.is_on:
pass
return_value, frame = self.window.vid.read()
if return_value != True:
break
if return_value:
image = Image.fromarray(frame)
self.window.frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
else:
raise ValueError("No image!")
frame_size = frame.shape[:2]
image_data = utils.image_preporcess(
np.copy(frame), [self.window.input_size, self.window.input_size])
image_data = image_data[np.newaxis, ...]
pred_sbbox, pred_mbbox, pred_lbbox = sess.run(
[return_tensors[1], return_tensors[2], return_tensors[3]],
feed_dict={return_tensors[0]: image_data})
pred_bbox = np.concatenate([np.reshape(pred_sbbox, (-1, 5 + self.window.num_classes)),
np.reshape(pred_mbbox, (-1, 5 + self.window.num_classes)),
np.reshape(pred_lbbox, (-1, 5 + self.window.num_classes))], axis=0)
bboxes = utils.postprocess_boxes(pred_bbox, frame_size, self.window.input_size, 0.45)
bboxes = utils.nms(bboxes, 0.45, method='nms')
# save images
# save_image.save_image(annotation_file, frame, vid.get(1), bboxes)
image = utils.draw_bbox(frame, bboxes)
# image = utils.draw_bbox(frame, bboxes, vid.get(1))
# 保存为 iou_tracker 格式
detections = save_mod(bboxes, 0.6)
result = np.asarray(image)
if self.window.writeVideo_flag:
# save a frame
out.write(result)
if self.window.showVideo_flag:
self.window.update_graphic_viewer(result)
pbar.update(1)
self.window.ui.processrate.setText(str(pbar))
else:
pbar.update(1)
self.window.ui.processrate.setText(str(pbar))
# Release everything if job is finished
out.release()
pbar.close()
# 多目标追踪
trackers = track_viou_video(self.window.media_path ,detections , 0.5, 0.6, 0.1, 23, 16, 'MEDIANFLOW', 1.0)
# 保存 trackers
with open(self.window.pickle_file_path, 'wb') as pk_f:
pickle.dump(trackers, pk_f)
self.window.ui.processrate.setText('=> saved trackers to pk file.')
def detect_pnet(self, im):
"""Get face candidates through pnet
Parameters:
----------
im: numpy array
input image array
one batch
Returns:
-------
boxes: numpy array
detected boxes before calibration
boxes_align: numpy array
boxes after calibration
"""
# im = self.unique_image_format(im)
# original wider face data
h, w, c = im.shape
net_size = 12
current_scale = float(
net_size) / self.min_face_size # find initial scale
im_resized = self.resize_image(im, current_scale) # scale = 1.0
current_height, current_width, _ = im_resized.shape
# fcn
all_boxes = list()
i = 0
# print('-------------------------- >>> ')
while min(current_height, current_width) >= net_size:
feed_imgs = []
#
img = torch.from_numpy(im_resized)
img = img.unsqueeze_(0)
feed_imgs = img.permute(0, 3, 1, 2)
if torch.cuda.is_available():
feed_imgs = feed_imgs.cuda()
# receptive field is 12×12
# 12×12 --> score
# 12×12 --> bounding box
cls_map, reg = self.pnet_detector(feed_imgs.float())
cls_map_np = image_tools.convert_chwTensor_to_hwcNumpy(
cls_map.cpu())
reg_np = image_tools.convert_chwTensor_to_hwcNumpy(reg.cpu())
boxes = self.generate_bounding_box(cls_map_np[0, :, :], reg_np,
current_scale, self.thresh[0])
# cv2.imshow('pnet_image',im_resized)
# cv2.waitKey(0)
# cv2.destroyAllWindows()
# generate pyramid images
current_scale *= self.scale_factor # self.scale_factor = 0.709
im_resized = self.resize_image(im, current_scale)
current_height, current_width, _ = im_resized.shape
if boxes.size == 0:
continue
# non-maximum suppresion
keep = utils.nms(boxes[:, :5], 0.2, 'Union')
boxes = boxes[keep]
# print(boxes.shape)
all_boxes.append(boxes)
# i+=1
if len(all_boxes) == 0:
return None, None
all_boxes = np.vstack(all_boxes)
# print("shape of all boxes {0}".format(all_boxes.shape))
# time.sleep(5)
# merge the detection from first stage
keep = utils.nms(all_boxes[:, 0:5], 0.3, 'Union')
all_boxes = all_boxes[keep]
# boxes = all_boxes[:, :5]
# x2 - x1
# y2 - y1
bw = all_boxes[:, 2] - all_boxes[:, 0] + 1
bh = all_boxes[:, 3] - all_boxes[:, 1] + 1
boxes = np.vstack([
all_boxes[:, 0],
all_boxes[:, 1],
all_boxes[:, 2],
all_boxes[:, 3],
all_boxes[:, 4],
])
boxes = boxes.T
# boxes = boxes = [x1, y1, x2, y2, score, reg] reg= [px1, py1, px2, py2] (in prediction)
align_topx = all_boxes[:, 0] + all_boxes[:, 5] * bw
align_topy = all_boxes[:, 1] + all_boxes[:, 6] * bh
align_bottomx = all_boxes[:, 2] + all_boxes[:, 7] * bw
align_bottomy = all_boxes[:, 3] + all_boxes[:, 8] * bh
# refine the boxes
boxes_align = np.vstack([
align_topx,
align_topy,
align_bottomx,
align_bottomy,
all_boxes[:, 4],
])
boxes_align = boxes_align.T
return boxes, boxes_align
def video_without_saving(ip, threshold):
# 对方socket
des_socket = socket(AF_INET, SOCK_STREAM)
# 链接服务器
des_socket.connect(('127.0.0.1', 8000))
classes = utils.read_class_names(cfg.YOLO.CLASSES)
num_classes = len(classes)
return_elements = [
"input/input_data:0", "pred_sbbox/concat_2:0", "pred_mbbox/concat_2:0",
"pred_lbbox/concat_2:0"
]
pb_file = "./yolov3_coco.pb"
video_path = ip
input_size = 416
graph = tf.Graph()
return_tensors = utils.read_pb_return_tensors(graph, pb_file,
return_elements)
with tf.Session(graph=graph) as sess:
messageId = 0
encode_param = [int(cv2.IMWRITE_JPEG_QUALITY), 50]
# 之后修改
# vid = cv2.VideoCapture(video_path)
vid = cv2.VideoCapture(0)
while True:
# time.sleep(0.01)
curr_time = datetime.datetime.now()
timestamp = '%s-%s-%s %s:%s:%s' % (
curr_time.year, curr_time.month, curr_time.day, curr_time.hour,
curr_time.minute, curr_time.second)
return_value, frame = vid.read()
result_, imgencode = cv2.imencode('.jpg', frame, encode_param)
data = np.array(imgencode)
stringData = data.tostring()
length = len(stringData)
if return_value:
frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
image = Image.fromarray(frame)
else:
raise ValueError("No image!")
frame_size = frame.shape[:2]
image_data = utils.image_preporcess(np.copy(frame),
[input_size, input_size])
image_data = image_data[np.newaxis, ...]
pred_sbbox, pred_mbbox, pred_lbbox = sess.run(
[return_tensors[1], return_tensors[2], return_tensors[3]],
feed_dict={return_tensors[0]: image_data})
pred_bbox = np.concatenate([
np.reshape(pred_sbbox, (-1, 5 + num_classes)),
np.reshape(pred_mbbox, (-1, 5 + num_classes)),
np.reshape(pred_lbbox, (-1, 5 + num_classes))
],
axis=0)
bboxes = utils.postprocess_boxes(pred_bbox, frame_size, input_size,
threshold)
bboxes = utils.nms(bboxes, 0.45, method='nms')
image = utils.draw_bbox(frame, bboxes)
result = np.asarray(image)
cv2.namedWindow("result", cv2.WINDOW_AUTOSIZE)
result = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
cv2.imshow("result", result)
if cv2.waitKey(1) & 0xFF == ord('q'): break
messageId += 1
mess_send(des_socket, bboxes, timestamp, messageId, ip, length,stringData)
def detect_rnet(self, im, dets):
"""Get face candidates using rnet
Parameters:
----------
im: numpy array
input image array
dets: numpy array
detection results of pnet
Returns:
-------
boxes: numpy array
detected boxes before calibration
boxes_align: numpy array
boxes after calibration
"""
# im: an input image
h, w, c = im.shape
if dets is None:
return None, None
# return square boxes
dets = self.square_bbox(dets)
# rounds
dets[:, 0:4] = np.round(dets[:, 0:4])
[dy, edy, dx, edx, y, ey, x, ex, tmpw, tmph] = self.pad(dets, w, h)
num_boxes = dets.shape[0]
'''
# helper for setting RNet batch size
batch_size = self.rnet_detector.batch_size
ratio = float(num_boxes) / batch_size
if ratio > 3 or ratio < 0.3:
print "You may need to reset RNet batch size if this info appears frequently, \
face candidates:%d, current batch_size:%d"%(num_boxes, batch_size)
'''
cropped_ims_tensors = []
for i in range(num_boxes):
tmp = np.zeros((tmph[i], tmpw[i], 3), dtype=np.uint8)
tmp[dy[i]:edy[i] + 1, dx[i]:edx[i] + 1, :] = im[y[i]:ey[i] + 1,
x[i]:ex[i] + 1, :]
crop_im = cv2.resize(tmp, (24, 24), interpolation=cv2.INTER_LINEAR)
# crop_im = (crop_im - 127.5) / 128.
# print('------------>>>>>. rrr')
crop_im_tensor = torch.from_numpy(crop_im)
# cropped_ims_tensors[i, :, :, :] = crop_im_tensor
cropped_ims_tensors.append(crop_im_tensor)
# feed_imgs = Variable(torch.stack(cropped_ims_tensors))
feed_imgs = (torch.stack(cropped_ims_tensors))
feed_imgs = feed_imgs.permute(0, 3, 1, 2)
#-------------------------------------------------------------------------------
if self.rnet_detector.use_cuda:
feed_imgs = feed_imgs.cuda()
cls_map, reg = self.rnet_detector(feed_imgs.float())
cls_map = cls_map.cpu().data.numpy()
reg = reg.cpu().data.numpy()
# landmark = landmark.cpu().data.numpy()
keep_inds = np.where(cls_map > self.thresh[1])[0]
if len(keep_inds) > 0:
boxes = dets[keep_inds]
cls = cls_map[keep_inds]
reg = reg[keep_inds]
# landmark = landmark[keep_inds]
else:
return None, None
keep = utils.nms(boxes, 0.4)
if len(keep) == 0:
return None, None
keep_cls = cls[keep]
keep_boxes = boxes[keep]
keep_reg = reg[keep]
# keep_landmark = landmark[keep]
bw = keep_boxes[:, 2] - keep_boxes[:, 0] + 1
bh = keep_boxes[:, 3] - keep_boxes[:, 1] + 1
boxes = np.vstack([
keep_boxes[:, 0], keep_boxes[:, 1], keep_boxes[:, 2],
keep_boxes[:, 3], keep_cls[:, 0]
])
align_topx = keep_boxes[:, 0] + keep_reg[:, 0] * bw
align_topy = keep_boxes[:, 1] + keep_reg[:, 1] * bh
align_bottomx = keep_boxes[:, 2] + keep_reg[:, 2] * bw
align_bottomy = keep_boxes[:, 3] + keep_reg[:, 3] * bh
boxes_align = np.vstack([
align_topx, align_topy, align_bottomx, align_bottomy, keep_cls[:,
0]
])
boxes = boxes.T
boxes_align = boxes_align.T
return boxes, boxes_align
def car_alarm(image, car_detect, cfg, mask_path_list, alarm_list):
# img_file = get_latest_image(cfg['scene/' + cfg['camera_no']])
# image = cv2.imread(img_file)
# if image is None:
# return
# input_data transform
#
input_size = cfg['input_size']
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
original_image = image
original_image_size = original_image.shape[:2]
image_data = utils.image_preporcess(np.copy(original_image),
[input_size, input_size])
image_data = image_data[np.newaxis, ...]
# model infer
pred_sbbox, pred_mbbox, pred_lbbox = car_detect.infer(image_data)
# post process get final bboxes
num_classes = cfg['num_classes']
pred_bbox = np.concatenate([
np.reshape(pred_sbbox, (-1, 5 + num_classes)),
np.reshape(pred_mbbox, (-1, 5 + num_classes)),
np.reshape(pred_lbbox, (-1, 5 + num_classes))
],
axis=0)
bboxes = utils.postprocess_boxes(pred_bbox, original_image_size,
input_size, 0.5)
bboxes = utils.nms(bboxes, 0.45, method='nms')
# # set judgement_matrix and estimate
# obj_dict = cfg['obj_dict']
# mask_dict = cfg['mask_dict']
# estimate = get_obj_num(bboxes, mask_path_list, obj_dict)
# judgement_matrix = [[0,1,1], #可停车区域
# [1,1,1]] #不可停车区域
# alarm = 0
# for i, obj in obj_dict.items():
# for j, mask in mask_dict.items():
# if judgement_matrix[j][i] == 1 and estimate[j, i] !=0:
# # print('在%s上有%d个%s' % (mask_dict[j], estimate[j, i], obj_dict[i][0]))
# alarm = 1
# res = {'alarm': alarm}
res = {}
objs = []
for bbox in bboxes:
objs.append({
'x1': bbox[0],
'y1': bbox[1],
'x2': bbox[2],
'y2': bbox[3],
'confidence': bbox[4],
'class': bbox[5]
})
res['objs'] = objs
return res
def detect_onet(self, im, dets):
"""Get face candidates using onet
Parameters:
----------
im: numpy array
input image array
dets: numpy array
detection results of rnet
Returns:
-------
boxes_align: numpy array
boxes after calibration
landmarks_align: numpy array
landmarks after calibration
"""
h, w, c = im.shape
if dets is None:
return None, None
dets = self.square_bbox(dets)
dets[:, 0:4] = np.round(dets[:, 0:4])
[dy, edy, dx, edx, y, ey, x, ex, tmpw, tmph] = self.pad(dets, w, h)
num_boxes = dets.shape[0]
cropped_ims_tensors = []
for i in range(num_boxes):
tmp = np.zeros((tmph[i], tmpw[i], 3), dtype=np.uint8)
tmp[dy[i]:edy[i] + 1, dx[i]:edx[i] + 1, :] = im[y[i]:ey[i] + 1,
x[i]:ex[i] + 1, :]
crop_im = cv2.resize(tmp, (48, 48), interpolation=cv2.INTER_LINEAR)
crop_im_tensor = torch.from_numpy(crop_im)
# cropped_ims_tensors[i, :, :, :] = crop_im_tensor
cropped_ims_tensors.append(crop_im_tensor)
# feed_imgs = Variable(torch.stack(cropped_ims_tensors))
feed_imgs = (torch.stack(cropped_ims_tensors))
feed_imgs = feed_imgs.permute(0, 3, 1, 2)
if self.rnet_detector.use_cuda:
feed_imgs = feed_imgs.cuda()
cls_map, reg = self.onet_detector(feed_imgs.float())
cls_map = cls_map.cpu().data.numpy()
reg = reg.cpu().data.numpy()
# landmark = landmark.cpu().data.numpy()
keep_inds = np.where(cls_map > self.thresh[2])[0]
if len(keep_inds) > 0:
boxes = dets[keep_inds]
cls = cls_map[keep_inds]
reg = reg[keep_inds]
# landmark = landmark[keep_inds]
else:
return None, None
keep = utils.nms(boxes, 0.6, mode="Minimum")
if len(keep) == 0:
return None, None
keep_cls = cls[keep]
keep_boxes = boxes[keep]
keep_reg = reg[keep]
# keep_landmark = landmark[keep]
bw = keep_boxes[:, 2] - keep_boxes[:, 0] + 1
bh = keep_boxes[:, 3] - keep_boxes[:, 1] + 1
align_topx = keep_boxes[:, 0] + keep_reg[:, 0] * bw
align_topy = keep_boxes[:, 1] + keep_reg[:, 1] * bh
align_bottomx = keep_boxes[:, 2] + keep_reg[:, 2] * bw
align_bottomy = keep_boxes[:, 3] + keep_reg[:, 3] * bh
# align_landmark_topx = keep_boxes[:, 0]
# align_landmark_topy = keep_boxes[:, 1]
boxes_align = np.vstack([
align_topx,
align_topy,
align_bottomx,
align_bottomy,
keep_cls[:, 0],
])
boxes_align = boxes_align.T
return None, boxes_align
prev_time = time.time()
pred_bbox = model.predict_on_batch(
image_data
) # make predictions, output [[b,52,52,3,5+c], [b,26,26,3,5+c], [b,13,13,3,5+c]]
curr_time = time.time()
exec_time = curr_time - prev_time
pred_bbox = [tf.reshape(x, (-1, tf.shape(x)[-1])) for x in pred_bbox
] # reshape to [[-1,5+c], [-1,5+c], [-1,5+c]]
pred_bbox = tf.concat(pred_bbox, axis=0) # concat into [-1, 5+c]
bboxes = utils.postprocess_boxes(
pred_bbox, frame_size, input_size,
0.3) # discard low score and out-of-bound boxes
bboxes = utils.nms(
bboxes, 0.45,
method='nms') # discard duplicate boxes which point at the same object
image = utils.draw_bbox(frame, bboxes) # draw boxes on the image
result = np.asarray(image) # convert into numpy array
info = "time: %.2f ms" % (1000 * exec_time)
cv2.putText(result,
text=info,
org=(50, 70),
fontFace=cv2.FONT_HERSHEY_SIMPLEX,
fontScale=1,
color=(255, 0, 0),
thickness=2)
cv2.namedWindow("result", cv2.WINDOW_AUTOSIZE)
result = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
cv2.imshow("result", result)
def test(model, SCORE_THRESHOLD, draw_boxes=False):
INPUT_SIZE = 416
CLASSES = utils.read_class_names(cfg.YOLO.CLASSES)
# TODO cups:改动部分使用TODO标记
predicted_dir_path = "./mAP/predicted"
ground_truth_dir_path = "./mAP/ground-truth"
if os.path.exists(predicted_dir_path):
shutil.rmtree(predicted_dir_path)
if os.path.exists(ground_truth_dir_path):
shutil.rmtree(ground_truth_dir_path)
if os.path.exists(cfg.TEST.DECTECTED_IMAGE_PATH):
shutil.rmtree(cfg.TEST.DECTECTED_IMAGE_PATH)
os.mkdir(cfg.TEST.DECTECTED_IMAGE_PATH)
os.mkdir(predicted_dir_path)
os.mkdir(ground_truth_dir_path)
# import show_tools
time_sum = 0
with open(cfg.TEST.ANNOT_PATH, "r") as annotation_file:
ori_list = annotation_file.readlines()
'''
ori_fifty_list = ori_list[-50:]
other_list = ori_list[:-50]
other_25_list = random.choices(other_list, k=25)
annotation_file_list = ori_fifty_list + other_25_list
'''
test_sum = len(ori_list)
print("test " + str(test_sum) + " pictures")
j = "|"
k = "="
kong = " "
for num, line in enumerate(ori_list):
# show_tools.view_bar('image_enhancement', num, len(annotation_file.readlines()))
annotation = line.strip().split()
image_path = annotation[0]
image_name = image_path.split("/")[-1]
img_name = image_name.split(".")[0]
bbox_data_gt = np.array(
[list(map(int, box.split(","))) for box in annotation[1:]],
dtype=np.int16)
if len(bbox_data_gt) == 0:
bboxes_gt = []
classes_gt = []
else:
bboxes_gt, classes_gt = bbox_data_gt[:, :4], bbox_data_gt[:, 4]
ground_truth_path = os.path.join(ground_truth_dir_path,
img_name + ".txt")
# TODO cups
# print('=> ground truth of %s:' % image_name)
num_bbox_gt = len(bboxes_gt)
with open(ground_truth_path, "w") as f:
for i in range(num_bbox_gt):
class_name = CLASSES[classes_gt[i] - 1]
xmin, ymin, xmax, ymax = list(map(str, bboxes_gt[i]))
bbox_mess = " ".join([class_name, xmin, ymin, xmax, ymax
]) + "\n"
f.write(bbox_mess)
# TODO cups
# print('=> predict result of %s:' % image_name)
predict_result_path = os.path.join(predicted_dir_path,
img_name + ".txt")
# Predict Process
image = cv2.imread(image_path)
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
image_size = image.shape[:2]
image_data = utils.image_preporcess(np.copy(image),
[INPUT_SIZE, INPUT_SIZE])
image_data = image_data[np.newaxis, ...].astype(np.float32)
predict_model = tf.keras.Model(
inputs=model.get_layer('input_1').input,
outputs=[
model.get_layer('tf_op_layer_concat_4').output,
model.get_layer('tf_op_layer_concat_7').output,
model.get_layer('tf_op_layer_concat_10').output
])
start = timeit.default_timer()
pred_bbox = predict_model(image_data)
pred_bbox = [
tf.reshape(x, (-1, tf.shape(x)[-1])) for x in pred_bbox
]
pred_bbox = tf.concat(pred_bbox, axis=0)
bboxes = utils.postprocess_boxes(pred_bbox, image_size, INPUT_SIZE,
SCORE_THRESHOLD)
bboxes = utils.nms(bboxes, cfg.TEST.IOU_THRESHOLD, method="nms")
end = timeit.default_timer()
time_sum += end - start
if cfg.TEST.DECTECTED_IMAGE_PATH is not None and draw_boxes == True:
image = utils.draw_bbox(image, bboxes)
cv2.imwrite(cfg.TEST.DECTECTED_IMAGE_PATH + image_name, image)
with open(predict_result_path, "w") as f:
for bbox in bboxes:
coor = np.array(bbox[:4], dtype=np.int32)
score = bbox[4]
class_ind = int(bbox[5])
class_name = CLASSES[class_ind]
score = "%.4f" % score
xmin, ymin, xmax, ymax = list(map(str, coor))
bbox_mess = (
" ".join([class_name, score, xmin, ymin, xmax, ymax]) +
"\n")
f.write(bbox_mess)
final_str = j + k * (int((num + 1) / 3) + 1) + ">" + kong * (int(
(test_sum - 1 - num) / 3)) + "| %d" % (int(
(num + 1) / test_sum * 100)) + "%"
print(final_str, end='\r')
print("")
print("average consume time:", time_sum / (num + 1))
print("test predit done")
def main(_argv):
INPUT_SIZE = FLAGS.size
if FLAGS.tiny:
STRIDES = np.array(cfg.YOLO.STRIDES_TINY)
ANCHORS = utils.get_anchors(cfg.YOLO.ANCHORS_TINY, FLAGS.tiny)
else:
STRIDES = np.array(cfg.YOLO.STRIDES)
if FLAGS.model == 'yolov4':
ANCHORS = utils.get_anchors(cfg.YOLO.ANCHORS, FLAGS.tiny)
else:
ANCHORS = utils.get_anchors(cfg.YOLO.ANCHORS_V3, FLAGS.tiny)
NUM_CLASS = len(utils.read_class_names(cfg.YOLO.CLASSES))
CLASSES = utils.read_class_names(cfg.YOLO.CLASSES)
predicted_dir_path = './mAP/predicted'
ground_truth_dir_path = './mAP/ground-truth'
if os.path.exists(predicted_dir_path): shutil.rmtree(predicted_dir_path)
if os.path.exists(ground_truth_dir_path):
shutil.rmtree(ground_truth_dir_path)
if os.path.exists(cfg.TEST.DECTECTED_IMAGE_PATH):
shutil.rmtree(cfg.TEST.DECTECTED_IMAGE_PATH)
os.mkdir(predicted_dir_path)
os.mkdir(ground_truth_dir_path)
os.mkdir(cfg.TEST.DECTECTED_IMAGE_PATH)
# Build Model
if FLAGS.framework == 'tf':
input_layer = tf.keras.layers.Input([INPUT_SIZE, INPUT_SIZE, 3])
if FLAGS.tiny:
feature_maps = YOLOv3_tiny(input_layer, NUM_CLASS)
bbox_tensors = []
for i, fm in enumerate(feature_maps):
bbox_tensor = decode(fm, NUM_CLASS, i)
bbox_tensors.append(bbox_tensor)
model = tf.keras.Model(input_layer, bbox_tensors)
utils.load_weights_tiny(model, FLAGS.weights)
else:
if FLAGS.model == 'yolov3':
feature_maps = YOLOv3(input_layer, NUM_CLASS)
bbox_tensors = []
for i, fm in enumerate(feature_maps):
bbox_tensor = decode(fm, NUM_CLASS, i)
bbox_tensors.append(bbox_tensor)
model = tf.keras.Model(input_layer, bbox_tensors)
utils.load_weights_v3(model, FLAGS.weights)
elif FLAGS.model == 'yolov4':
feature_maps = YOLOv4(input_layer, NUM_CLASS)
bbox_tensors = []
for i, fm in enumerate(feature_maps):
bbox_tensor = decode(fm, NUM_CLASS, i)
bbox_tensors.append(bbox_tensor)
model = tf.keras.Model(input_layer, bbox_tensors)
utils.load_weights(model, FLAGS.weights)
else:
# Load TFLite model and allocate tensors.
interpreter = tf.lite.Interpreter(model_path=FLAGS.weights)
interpreter.allocate_tensors()
# Get input and output tensors.
input_details = interpreter.get_input_details()
output_details = interpreter.get_output_details()
print(input_details)
print(output_details)
num_lines = sum(1 for line in open(FLAGS.annotation_path))
with open(cfg.TEST.ANNOT_PATH, 'r') as annotation_file:
for num, line in enumerate(annotation_file):
annotation = line.strip().split()
image_path = annotation[0]
image_name = image_path.split('/')[-1]
image = cv2.imread(image_path)
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
bbox_data_gt = np.array(
[list(map(int, box.split(','))) for box in annotation[1:]])
if len(bbox_data_gt) == 0:
bboxes_gt = []
classes_gt = []
else:
bboxes_gt, classes_gt = bbox_data_gt[:, :4], bbox_data_gt[:, 4]
ground_truth_path = os.path.join(ground_truth_dir_path,
str(num) + '.txt')
print('=> ground truth of %s:' % image_name)
num_bbox_gt = len(bboxes_gt)
with open(ground_truth_path, 'w') as f:
for i in range(num_bbox_gt):
class_name = CLASSES[classes_gt[i]]
xmin, ymin, xmax, ymax = list(map(str, bboxes_gt[i]))
bbox_mess = ' '.join([class_name, xmin, ymin, xmax, ymax
]) + '\n'
f.write(bbox_mess)
print('\t' + str(bbox_mess).strip())
print('=> predict result of %s:' % image_name)
predict_result_path = os.path.join(predicted_dir_path,
str(num) + '.txt')
# Predict Process
image_size = image.shape[:2]
image_data = utils.image_preporcess(np.copy(image),
[INPUT_SIZE, INPUT_SIZE])
image_data = image_data[np.newaxis, ...].astype(np.float32)
if FLAGS.framework == "tf":
pred_bbox = model.predict(image_data)
else:
interpreter.set_tensor(input_details[0]['index'], image_data)
interpreter.invoke()
pred_bbox = [
interpreter.get_tensor(output_details[i]['index'])
for i in range(len(output_details))
]
if FLAGS.model == 'yolov3':
pred_bbox = utils.postprocess_bbbox(pred_bbox, ANCHORS,
STRIDES)
elif FLAGS.model == 'yolov4':
XYSCALE = cfg.YOLO.XYSCALE
pred_bbox = utils.postprocess_bbbox(pred_bbox,
ANCHORS,
STRIDES,
XYSCALE=XYSCALE)
pred_bbox = tf.concat(pred_bbox, axis=0)
bboxes = utils.postprocess_boxes(pred_bbox, image_size, INPUT_SIZE,
cfg.TEST.SCORE_THRESHOLD)
bboxes = utils.nms(bboxes, cfg.TEST.IOU_THRESHOLD, method='nms')
if cfg.TEST.DECTECTED_IMAGE_PATH is not None:
image = utils.draw_bbox(image, bboxes)
cv2.imwrite(cfg.TEST.DECTECTED_IMAGE_PATH + image_name, image)
with open(predict_result_path, 'w') as f:
for bbox in bboxes:
coor = np.array(bbox[:4], dtype=np.int32)
score = bbox[4]
class_ind = int(bbox[5])
class_name = CLASSES[class_ind]
score = '%.4f' % score
xmin, ymin, xmax, ymax = list(map(str, coor))
bbox_mess = ' '.join(
[class_name, score, xmin, ymin, xmax, ymax]) + '\n'
f.write(bbox_mess)
print('\t' + str(bbox_mess).strip())
print(num, num_lines)
def main(_argv):
physical_devices = tf.config.experimental.list_physical_devices('GPU')
for physical_device in physical_devices:
tf.config.experimental.set_memory_growth(physical_device, True)
if FLAGS.tiny:
STRIDES = np.array(cfg.YOLO.STRIDES_TINY)
XYSCALE = cfg.YOLO.XYSCALE_TINY
if FLAGS.model == 'yolov4':
ANCHORS = utils.get_anchors(cfg.YOLO.ANCHORS_TINY, FLAGS.tiny)
else:
ANCHORS = utils.get_anchors(cfg.YOLO.ANCHORS_TINY_V3, FLAGS.tiny)
else:
STRIDES = np.array(cfg.YOLO.STRIDES)
XYSCALE = cfg.YOLO.XYSCALE
if FLAGS.model == 'yolov4':
ANCHORS = utils.get_anchors(cfg.YOLO.ANCHORS, FLAGS.tiny)
else:
ANCHORS = utils.get_anchors(cfg.YOLO.ANCHORS_V3, FLAGS.tiny)
CLASSES = utils.read_class_names(cfg.YOLO.CLASSES)
NUM_CLASSES = len(CLASSES)
input_size = FLAGS.size
image_path = FLAGS.image
original_image = cv2.imread(image_path)
original_image = cv2.cvtColor(original_image, cv2.COLOR_BGR2RGB)
original_image_size = original_image.shape[:2]
image_data = utils.image_preprocess(np.copy(original_image),
[input_size, input_size])
image_data = image_data[np.newaxis, ...].astype(np.float32)
if FLAGS.framework == 'tf':
input_layer = tf.keras.layers.Input([input_size, input_size, 3])
if FLAGS.tiny:
if FLAGS.model == 'yolov3':
feature_maps = YOLOv3_tiny(input_layer, NUM_CLASSES)
else:
feature_maps = YOLOv4_tiny(input_layer, NUM_CLASSES)
bbox_tensors = []
for i, fm in enumerate(feature_maps):
bbox_tensor = decode(fm, NUM_CLASSES, i)
bbox_tensors.append(bbox_tensor)
model = tf.keras.Model(input_layer, bbox_tensors)
utils.load_weights_tiny(model, FLAGS.weights, FLAGS.model)
else:
if FLAGS.model == 'yolov3':
feature_maps = YOLOv3(input_layer, NUM_CLASSES)
bbox_tensors = []
for i, fm in enumerate(feature_maps):
bbox_tensor = decode(fm, NUM_CLASSES, i)
bbox_tensors.append(bbox_tensor)
model = tf.keras.Model(input_layer, bbox_tensors)
utils.load_weights_v3(model, FLAGS.weights)
elif FLAGS.model == 'yolov4':
feature_maps = YOLOv4(input_layer, NUM_CLASSES)
bbox_tensors = []
for i, fm in enumerate(feature_maps):
bbox_tensor = decode(fm, NUM_CLASSES, i)
bbox_tensors.append(bbox_tensor)
model = tf.keras.Model(input_layer, bbox_tensors)
if FLAGS.weights.split(".")[len(FLAGS.weights.split(".")) -
1] == "weights":
utils.load_weights(model, FLAGS.weights)
else:
model.load_weights(FLAGS.weights).expect_partial()
model.summary()
pred_bbox = model.predict(image_data)
elif FLAGS.framework == 'tflite':
interpreter = tf.lite.Interpreter(model_path=FLAGS.weights)
interpreter.allocate_tensors()
input_details = interpreter.get_input_details()
output_details = interpreter.get_output_details()
interpreter.set_tensor(input_details[0]['index'], image_data)
interpreter.invoke()
pred_bbox = [
interpreter.get_tensor(output_details[i]['index'])
for i in range(len(output_details))
]
elif FLAGS.framework == 'trt':
saved_model_loaded = tf.saved_model.load(FLAGS.weights,
tags=[tag_constants.SERVING])
infer = saved_model_loaded.signatures['serving_default']
batched_input = tf.constant(image_data)
pred_bbox = []
result = infer(batched_input)
for _, value in result.items():
value = value.numpy()
pred_bbox.append(value)
if FLAGS.model == 'yolov4':
pred_bbox = utils.postprocess_bbbox(pred_bbox, ANCHORS, STRIDES,
XYSCALE)
else:
pred_bbox = utils.postprocess_bbbox(pred_bbox, ANCHORS, STRIDES)
bboxes = utils.postprocess_boxes(pred_bbox, original_image_size,
input_size, 0.5) # 0.25
bboxes = utils.nms(bboxes, 0.5, method='nms') # 0.213
image = utils.draw_bbox(original_image, bboxes, classes=CLASSES)
image = cv2.cvtColor(np.array(image), cv2.COLOR_BGR2RGB)
cv2.imwrite(FLAGS.output, image)
if os.path.isfile(img_path_file):
img = cv2.imread(img_path_file)
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
img_size = img.shape[:2]
image_data = utils.image_preporcess(np.copy(img), [input_size, input_size])
image_data = image_data[np.newaxis, ...]
pred_sbbox, pred_mbbox, pred_lbbox = sess.run([return_tensors[1], return_tensors[2], return_tensors[3]],
feed_dict={ return_tensors[0]: image_data})
pred_bbox = np.concatenate([np.reshape(pred_sbbox, (-1, 5 + num_classes)), np.reshape(pred_mbbox, (-1, 5 + num_classes)),
np.reshape(pred_lbbox, (-1, 5 + num_classes))], axis=0)
bboxes = utils.postprocess_boxes(pred_bbox, img_size, input_size, score_thresh)
bboxes = utils.nms(bboxes, iou_thresh, method='nms')
if len(bboxes) > 0:
image = utils.draw_bbox(img, bboxes)
#image = Image.fromarray(image)
#image.show()
out_img = np.asarray(image)
score = bboxes[0][4]
file_name, file_path = os.path.split(img_path_file)
file, postfix = os.path.splitext(file_name)
out_file = os.path.join(out_path, str(score) + '_' + file_name)
cv2.imwrite(out_file, out_img)
elif os.path.isdir(img_path_file):
img_files = os.listdir(img_path_file)
def work_frame(filename, count):
global flag,model,server_addr,input_size
start_time = datetime.datetime.now()
# GET frame from server by server /static reference
video_id = "video_" + str(filename) + "_frame_" + str(count) + ".jpg"
ref_file = "static/" + video_id
response = requests.get(server_addr + ref_file)
# Image transformation to accepted format
arr = np.asarray(bytearray(response.content), dtype=np.uint8)
original_image = cv2.imdecode(arr, -1)
###### OBJECT DETECTION CODE #######
# Read class names
class_names = {}
with open(cfg.YOLO.CLASSES, 'r') as data:
for ID, name in enumerate(data):
class_names[ID] = name.strip('\n')
# Setup tensorflow, keras and YOLOv3
original_image = cv2.cvtColor(original_image, cv2.COLOR_BGR2RGB)
original_image_size = original_image.shape[:2]
image_data = utils.image_preporcess(np.copy(original_image), [input_size, input_size])
image_data = image_data[np.newaxis, ...].astype(np.float32)
if flag:
input_layer = tf.keras.layers.Input([input_size, input_size, 3])
feature_maps = YOLOv3(input_layer)
bbox_tensors = []
for i, fm in enumerate(feature_maps):
bbox_tensor = decode(fm, i)
bbox_tensors.append(bbox_tensor)
model = tf.keras.Model(input_layer, bbox_tensors)
utils.load_weights(model, "./yolov3.weights")
flag = False
pred_bbox = model.predict(image_data)
pred_bbox = [tf.reshape(x, (-1, tf.shape(x)[-1])) for x in pred_bbox]
pred_bbox = tf.concat(pred_bbox, axis=0)
bboxes = utils.postprocess_boxes(pred_bbox, original_image_size, input_size, 0.3)
bboxes = utils.nms(bboxes, 0.45, method='nms')
# We have our objects detected and boxed, lets move the class name into a list
objects_detected = []
for x0,y0,x1,y1,prob,class_id in bboxes:
objects_detected.append(class_names[class_id])
### END OF OBJECT DETECTION CODE ###
print(objects_detected)
final_time = datetime.datetime.now() - start_time
# Elaborate json with frame info and post to server in /return route
final_dict={}
people_count=0
for obj in objects_detected:
if str(obj) == "person":
people_count+=1
if str(obj) in final_dict:
final_dict[str(obj)] += 1
else:
final_dict[str(obj)] = 1
final_json = {
"video_id":filename,
"frame_no":count,
"processing_time":str(final_time),
"people_detected":people_count,
"objects_detected":json.dumps(final_dict)
}
requests.post(server_addr + "return", json=final_json)
return "\nDONE frame n. " + str(count) + "of video " + filename + "!\n"
def main(_argv):
import os
os.environ["CUDA_VISIBLE_DEVICES"] = "0"
if FLAGS.tiny:
STRIDES = np.array(cfg.YOLO.STRIDES_TINY)
ANCHORS = utils.get_anchors(cfg.YOLO.ANCHORS_TINY, FLAGS.tiny)
else:
STRIDES = np.array(cfg.YOLO.STRIDES)
if FLAGS.model == 'yolov4':
ANCHORS = utils.get_anchors(cfg.YOLO.ANCHORS, FLAGS.tiny)
else:
ANCHORS = utils.get_anchors(cfg.YOLO.ANCHORS_V3, FLAGS.tiny)
NUM_CLASS = len(utils.read_class_names(cfg.YOLO.CLASSES))
XYSCALE = cfg.YOLO.XYSCALE
input_size = FLAGS.size
video_path = FLAGS.video
if video_path == 'none':
possible_camera_index = [5, 6, 7, 8]
print("Searching for camera...")
for camera_index in possible_camera_index:
vid = cv2.VideoCapture(camera_index)
return_value, frame = vid.read()
if frame is not None:
print("Camera found at index", camera_index)
break
else:
print("Video from: ", video_path)
vid = cv2.VideoCapture(video_path)
if FLAGS.framework == 'tf':
input_layer = tf.keras.layers.Input([input_size, input_size, 3])
if FLAGS.tiny:
feature_maps = YOLOv3_tiny(input_layer, NUM_CLASS)
bbox_tensors = []
for i, fm in enumerate(feature_maps):
bbox_tensor = decode(fm, NUM_CLASS, i)
bbox_tensors.append(bbox_tensor)
model = tf.keras.Model(input_layer, bbox_tensors)
utils.load_weights_tiny(model, FLAGS.weights)
else:
if FLAGS.model == 'yolov3':
feature_maps = YOLOv3(input_layer, NUM_CLASS)
bbox_tensors = []
for i, fm in enumerate(feature_maps):
bbox_tensor = decode(fm, NUM_CLASS, i)
bbox_tensors.append(bbox_tensor)
model = tf.keras.Model(input_layer, bbox_tensors)
utils.load_weights_v3(model, FLAGS.weights)
elif FLAGS.model == 'yolov4':
feature_maps = YOLOv4(input_layer, NUM_CLASS)
bbox_tensors = []
for i, fm in enumerate(feature_maps):
bbox_tensor = decode(fm, NUM_CLASS, i)
bbox_tensors.append(bbox_tensor)
model = tf.keras.Model(input_layer, bbox_tensors)
utils.load_weights(model, FLAGS.weights)
model.summary()
else:
# Load TFLite model and allocate tensors.
interpreter = tf.lite.Interpreter(model_path=FLAGS.weights)
interpreter.allocate_tensors()
# Get input and output tensors.
input_details = interpreter.get_input_details()
output_details = interpreter.get_output_details()
print(input_details)
print(output_details)
while True:
return_value, frame = vid.read()
if return_value:
frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
image = Image.fromarray(frame)
else:
raise ValueError("No image! Try with another video format")
frame_size = frame.shape[:2]
image_data = utils.image_preprocess(np.copy(frame),
[input_size, input_size])
image_data = image_data[np.newaxis, ...].astype(np.float32)
prev_time = time.time()
if FLAGS.framework == 'tf':
pred_bbox = model.predict(image_data)
else:
interpreter.set_tensor(input_details[0]['index'], image_data)
interpreter.invoke()
pred_bbox = [
interpreter.get_tensor(output_details[i]['index'])
for i in range(len(output_details))
]
if FLAGS.model == 'yolov4':
pred_bbox = utils.postprocess_bbbox(pred_bbox, ANCHORS, STRIDES,
XYSCALE)
else:
pred_bbox = utils.postprocess_bbbox(pred_bbox, ANCHORS, STRIDES)
bboxes = utils.postprocess_boxes(pred_bbox, frame_size, input_size,
0.25)
bboxes = utils.nms(bboxes, 0.213, method='nms')
for box in bboxes:
print('x_min', box[0])
print('y_min', box[1])
print('x_max', box[2])
print('y_max', box[3])
print('probability', box[4])
print('object_id', box[5])
print('-----')
image = utils.draw_bbox(frame, bboxes)
curr_time = time.time()
exec_time = curr_time - prev_time
result = np.asarray(image)
info = "time: %.2f ms" % (1000 * exec_time)
print(info)
cv2.namedWindow("result", cv2.WINDOW_AUTOSIZE)
result = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
cv2.imshow("result", result)
print(cv2.getWindowImageRect('result'))
if cv2.waitKey(1) & 0xFF == ord('q'):
vid.release()
break
def main():
if not os.path.exists(args.output):
os.mkdir(args.output)
testset = Dataset('test')
test_generator = tf.data.Dataset.from_generator(
lambda: testset,
(tf.float32,
(tf.string, tf.int32, tf.int32))).batch(cfg.TEST.BATCH_SIZE)
classes = utils.read_class_names(cfg.YOLO.CLASSES)
STRIDES = np.array(cfg.YOLO.STRIDES)
ANCHORS = utils.get_anchors(cfg.YOLO.ANCHORS)
NUM_CLASS = len(classes)
XYSCALE = cfg.YOLO.XYSCALE
input_size = cfg.TEST.INPUT_SIZE
model = YOLOv4(NUM_CLASS, STRIDES, ANCHORS, XYSCALE, 'test')
if args.pretrained:
dummy_input = np.ones((1, input_size, input_size, 3))
model.predict(dummy_input)
model.load_weights('./weights/pretrained.h5')
print('Pretrained weights loaded')
elif args.weights is not None:
ckpt = tf.train.Checkpoint(model=model)
ckpt_manager = tf.train.CheckpointManager(ckpt,
args.weights,
max_to_keep=3)
if ckpt_manager.latest_checkpoint:
ckpt.restore(ckpt_manager.latest_checkpoint).expect_partial()
print('Latest checkpoint restored')
else:
print('Failed to load latest checkpoint')
times = 0.0
for index, (image_data, image_meta) in enumerate(test_generator):
if index % 100 == 0 or index == 0:
print('Processing {}/{} images...'.format(index + 1, len(testset)))
original_image_size = (image_meta[1].numpy().item(),
image_meta[2].numpy().item())
start_time = time.time()
pred_bbox = model.predict(image_data)
pred_bbox = utils.postprocess_bbbox(pred_bbox, ANCHORS, STRIDES,
XYSCALE)
bboxes = utils.postprocess_boxes(pred_bbox, original_image_size,
input_size, 0.25)
bboxes = utils.nms(bboxes, 0.213, method='nms')
times += (time.time() - start_time)
bboxes = sorted(bboxes, key=lambda x: -x[4])
f = open(
os.path.join(
args.output, image_meta[0].numpy().item().decode(
'utf-8').split('/')[-1][:-4] + '.txt'), 'w')
for i, b in enumerate(bboxes):
class_name = classes[int(b[5])]
conf = str(b[4])
xmin, ymin, xmax, ymax = str(int(b[0])), str(int(b[1])), str(
int(b[2])), str(int(b[3]))
predicted = [class_name, conf, xmin, ymin, xmax, ymax, '\n']
if i < len(bboxes) - 1:
f.write(' '.join(predicted))
else:
f.write(' '.join(predicted[:-1]))
f.close()
print('All test images were processed. FPS is {:.2f}'.format(
(len(testset) / times)))
pred_sbbox, pred_mbbox, pred_lbbox = sess.run(
[return_tensors[1], return_tensors[2], return_tensors[3]],
feed_dict={return_tensors[0]: image_data})
pred_bbox = np.concatenate([
np.reshape(pred_sbbox, (-1, 5 + num_classes)),
np.reshape(pred_mbbox, (-1, 5 + num_classes)),
np.reshape(pred_lbbox, (-1, 5 + num_classes))
],
axis=0)
bboxes = utils.postprocess_boxes(pred_bbox, original_image_size,
input_size, confidence,
only_save_one)
bboxes = utils.nms(bboxes, 0.45, method='nms')
image = Image.fromarray(
cv2.cvtColor(original_image, cv2.COLOR_RGB2BGR))
# Save originals
orig_path = os.path.join(os.path.join(output_dir, 'orig'),
str(index_dict['index']) + '.jpg')
image.save(orig_path)
row_string = orig_path
plate_num = plate_rules(url_dict['plates'][int(
index_dict['index'])])
for bbox in bboxes:
"""
bboxes: [x_min, y_min, x_max, y_max, probability, cls_id] format coordinates.
def main(argv):
NUM_CLASS = 2
ANCHORS = [
12, 16, 19, 36, 40, 28, 36, 75, 76, 55, 72, 146, 142, 110, 192, 243,
459, 401
]
ANCHORS = np.array(ANCHORS, dtype=np.float32)
ANCHORS = ANCHORS.reshape(3, 3, 2)
STRIDES = [8, 16, 32]
XYSCALE = [1.2, 1.1, 1.05]
input_size = FLAGS.size
video_path = FLAGS.video_path
score_thresh = FLAGS.score_thresh
iou_thresh = FLAGS.iou_thresh
save_path = FLAGS.save_path
print(f'[DEBUG][video] input_size : {input_size}')
print(f'[DEBUG][video] video_path : {video_path}')
print(f'[DEBUG][video] score_thresh : {score_thresh}')
print(f'[DEBUG][video] iou_thresh : {iou_thresh}')
print(f'[DEBUG][video] save_path : {save_path}')
print('[INFO] Bulding Yolov4 architecture')
tic = time.perf_counter()
input_layer = tf.keras.layers.Input([input_size, input_size, 3])
print(f'[INFO][video] Created input_layer of size {input_size}')
print(f'[DEBUG][video] input_layer : {input_layer}')
feature_maps = YOLOv4(input_layer, NUM_CLASS)
print(f'[DEBUG][video] feature_maps : {feature_maps}')
bbox_tensors = []
for i, fm in enumerate(feature_maps):
bbox_tensors.append(decode(fm, NUM_CLASS, i))
model = tf.keras.Model(input_layer, bbox_tensors)
utils.load_weights(model, FLAGS.weights)
toc = time.perf_counter()
print(f'[INFO] Architecture built.')
print(f'[DEBUG][video] Execution took {(1000 * (toc - tic)):0.4f} ms')
vid = cv2.VideoCapture(video_path)
if save_path:
width = int(vid.get(cv2.CAP_PROP_FRAME_WIDTH))
height = int(vid.get(cv2.CAP_PROP_FRAME_HEIGHT))
fps = int(vid.get(cv2.CAP_PROP_FPS))
print(f"[DEBUG][video] Video CODEC : {FLAGS.save_path.split('.')[1]}")
codec = cv2.VideoWriter_fourcc(*'MJPEG')
out = cv2.VideoWriter(FLAGS.save_path, codec, fps, (width, height))
while True:
return_value, frame = vid.read()
if return_value:
print(f'[DEBUG] Got video capture')
frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
image = Image.fromarray(frame)
else:
print(f"[DEBUG][video] Video Over")
vid.release()
if save_path:
out.release()
break
#raise ValueError("No image! Try with another video format")
frame_size = frame.shape[:2]
image_data = utils.image_preprocess(np.copy(frame),
[input_size, input_size])
image_data = image_data[np.newaxis, ...].astype(np.float32)
prev_time = time.perf_counter()
pred_bbox = model.predict(image_data)
print(f'[INFO][video] Finished initial predication on image')
pred_bbox = utils.postprocess_bbbox(pred_bbox, ANCHORS, STRIDES,
XYSCALE)
bboxes = utils.postprocess_boxes(pred_bbox, frame_size, input_size,
score_thresh)
bboxes = utils.nms(bboxes, iou_thresh, method='nms')
image = utils.draw_bbox(frame, bboxes)
curr_time = time.perf_counter()
exec_time = curr_time - prev_time
result = np.asarray(image)
info = "fdpms: %.2f ms" % (1000 * exec_time)
print(info)
cv2.namedWindow("result", cv2.WINDOW_AUTOSIZE)
result = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
cv2.imshow("result", result)
print(result.shape)
if save_path:
out.write(result)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
