def main():
if not os.path.exists(mp4):
print("mp4 not exists, please check: %s" % mp4)
cap = cv.VideoCapture(mp4)
my_graph = ai.Graph(model_path)
while True:
success, bgr_img = cap.read()
if bgr_img is None:
break
img = cv.resize(bgr_img, (resize_w, resize_h))
print("start")
#推理结果
result_list = my_graph.Inference_single(img)
if not result_list:
print("get no result")
return
h, w = bgr_img.shape[0], bgr_img.shape[1]
solution = (h, w)
# 后处理
detection_result_list = ai.SSDPostProcess(result_list, solution, 0.5, CLASSES)
for result in detection_result_list:
cv.rectangle(bgr_img, (result.lt.x,result.lt.y) , (result.rb.x, result.rb.y), (0,255,0))
p3 = (max(result.lt.x, 15), max(result.lt.y, 15))
cv.putText(bgr_img, result.result_text, p3, cv.FONT_ITALIC, 0.6, (0, 255, 0), 1)
print(result.result_text)
print("over")
def predict(raw_q, pred_q):
my_graph = ai.Graph(model_path)
while True:
raw_img = raw_q.get()
raw_img = cv2.resize(raw_img, img_shape)
result_list = my_graph.Inference(raw_img)
if not result_list:
print("get no result")
return
h, w = raw_img.shape[0], raw_img.shape[1]
solution = (h, w)
detection_result_list = ai.SSDPostProcess(result_list[0], solution,
0.5, CLASSES)
for result in detection_result_list:
pred_q.put(result.result_text)
def combine_images(queue_list, cam_addrs, img_shape):
my_graph = ai.Graph(model_path)
num_cameras = len(queue_list)
while True:
imgs = [cv2.resize(q.get(), img_shape) for q in queue_list]
imgs_list = []
for num in range(num_cameras):
imgs_list.append(imgs[num])
#ai/graph Inference(self, input_data)
result_list = my_graph.Inference(imgs_list)
if not result_list:
print("get no result")
return
h, w = imgs[0].shape[0], imgs[1].shape[1]
solution = (h, w)
for num in range(num_cameras):
detection_result_list = ai.SSDPostProcess(result_list[num],
solution, 0.5, CLASSES)
for result in detection_result_list:
global t2
t2 = time.time()
print(result.result_text, cam_addrs[num], t2 - t1)
def main():
bgr_img = cv.imread(img_path)
img = cv.resize(bgr_img, (resize_w, resize_h))
my_graph = ai.Graph(model_path)
result_list = my_graph.Inference_single(img)
if not result_list:
print("get no result")
return
# print(bgr_img.shape)
h, w = bgr_img.shape[0], bgr_img.shape[1]
solution = (h, w)
# 后处理
detection_result_list = ai.SSDPostProcess(result_list, solution, 0.5, CLASSES)
for result in detection_result_list:
cv.rectangle(bgr_img, (result.lt.x,result.lt.y) , (result.rb.x, result.rb.y), (0,255,0))
p3 = (max(result.lt.x, 15), max(result.lt.y, 15))
cv.putText(bgr_img, result.result_text, p3, cv.FONT_ITALIC, 0.6, (0, 255, 0), 1)
print(result.result_text)
print("over")
def main():
camera_width = 1280
camera_height = 720
presenter_config = './face_detection.conf'
cap = camera.Camera(id=0,
fps=20,
width=camera_width,
height=camera_height,
format=camera.CAMERA_IMAGE_FORMAT_YUV420_SP)
if not cap.IsOpened():
print("Open camera 0 failed")
return
dvpp_handle = dvpp_process.DvppProcess(camera_width, camera_height)
graph = ai.Graph('./model/face_detection_rgb.om')
chan = presenteragent.OpenChannel(presenter_config)
if chan == None:
print("Open presenter channel failed")
return
while True:
yuv_img = cap.Read()
orig_image = dvpp_handle.Yuv2Jpeg(yuv_img)
yuv_img = yuv_img.reshape((1080, 1280))
img = cv.cvtColor(yuv_img, cv.COLOR_YUV2RGB_I420)
img = cv.resize(img, (300, 300))
result = graph.Inference(img)
detection_list = ai.SSDPostProcess(result,
(camera_height, camera_width), 0.9,
['background', 'face'])
chan.SendDetectionData(camera_width, camera_height,
orig_image.tobytes(), detection_list)