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04_hand_detection_demo.py
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04_hand_detection_demo.py
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#!/usr/bin/env python
# -*- coding: utf-8 -*-
import time
import copy
import cv2 as cv
try:
import tensorflow.compat.v1 as tf
except Exception:
import tensorflow as tf
from utils import *
# バウンディングボックスリスト ##################################################
bba_function = [
[bba_rotate_dotted_ring3, None],
[bba_translucent_rectangle, None],
[bba_translucent_circle, None],
[bba_look_into_the_muzzle, None],
[bba_translucent_rectangle_fill1, u'HAND'],
[bba_square_obit, None],
[bba_annotation_line, u'検出結果:手'],
[bba_ground_glass, None],
]
def graph_load(path):
config = tf.ConfigProto(gpu_options=tf.compat.v1.GPUOptions(
allow_growth=True))
with tf.compat.v1.Graph().as_default() as net_graph:
graph_data = tf.gfile.FastGFile(path, 'rb').read()
graph_def = tf.compat.v1.GraphDef()
graph_def.ParseFromString(graph_data)
tf.import_graph_def(graph_def, name='')
sess = tf.compat.v1.Session(graph=net_graph, config=config)
sess.graph.as_default()
return sess
def session_run(sess, inp):
out = sess.run(
[
sess.graph.get_tensor_by_name('num_detections:0'),
sess.graph.get_tensor_by_name('detection_scores:0'),
sess.graph.get_tensor_by_name('detection_boxes:0'),
sess.graph.get_tensor_by_name('detection_classes:0')
],
feed_dict={
'image_tensor:0': inp.reshape(1, inp.shape[0], inp.shape[1], 3)
},
)
return out
def main():
print("Object Detection Start...\n")
# カメラ準備 ###############################################################
cap = cv.VideoCapture(0)
frame_width = 960
frame_height = 540
cap.set(cv.CAP_PROP_FRAME_WIDTH, frame_width)
cap.set(cv.CAP_PROP_FRAME_HEIGHT, frame_height)
# 手検出モデルロード #######################################################
sess = graph_load('model/mobilenetv2_ssd_lite/hand_detection.pb')
# FPS計測モジュール ########################################################
cvFpsCalc = CvFpsCalc()
index = 0
fps = 15
animation_count = 0
while True:
start_time = time.time()
animation_count += 1
display_fps = cvFpsCalc.get()
# カメラキャプチャ #####################################################
ret, frame = cap.read()
if not ret:
continue
frame_width, frame_height = frame.shape[1], frame.shape[0]
debug_image = copy.deepcopy(frame)
# 手検出実施 ###########################################################
inp = cv.resize(frame, (512, 512))
inp = inp[:, :, [2, 1, 0]] # BGR2RGB
out = session_run(sess, inp)
num_detections = int(out[0][0])
for i in range(num_detections):
score = float(out[1][0][i])
bbox = [float(v) for v in out[2][0][i]]
class_id = int(out[3][0][i])
if score < 0.8:
continue
# 手検出結果可視化 #################################################
x1, y1 = int(bbox[1] * frame_width), int(bbox[0] * frame_height)
x2, y2 = int(bbox[3] * frame_width), int(bbox[2] * frame_height)
debug_image = bba_function[index][0](
image=debug_image,
p1=(x1, y1),
p2=(x2, y2),
text=bba_function[index][1],
fps=fps,
animation_count=animation_count,
)
# 画面反映 #############################################################
cv.putText(debug_image, "FPS:" + str(display_fps), (10, 50),
cv.FONT_HERSHEY_SIMPLEX, 1.5, (0, 0, 0), 2, cv.LINE_AA)
cv.putText(debug_image, "FPS:" + str(display_fps), (10, 50),
cv.FONT_HERSHEY_SIMPLEX, 1.5, (252, 244, 234), 1,
cv.LINE_AA)
# cv.putText(debug_image,
# str(bba_function[index][0].__name__) + '()', (10, 50),
# cv.FONT_HERSHEY_COMPLEX, 1.0, (0, 255, 0))
cv.imshow('Demo', debug_image)
# キー処理(N:次へ、P:前へ、ESC:終了) #################################
key = cv.waitKey(1)
if key == 110: # N
index = 0 if ((index + 1) >= len(bba_function)) else (index + 1)
if key == 112: # P
index = len(bba_function) - 1 if ((index - 1) < 0) else (index - 1)
if key == 27: # ESC
break
# FPS調整 #############################################################
elapsed_time = time.time() - start_time
sleep_time = max(0, ((1.0 / fps) - elapsed_time))
time.sleep(sleep_time)
cap.release()
cv.destroyAllWindows()
if __name__ == '__main__':
main()