def openpose_detect(request):
"""
Use OpenPose to extract human skeleton points from a given image.
"""
if myOpenPose is None:
error = "OpenPose not supported on this server"
logger.error(prepend_ip("%s" % error, request))
ret = {"success": "false", "error": error}
json_ret = json.dumps(ret)
return HttpResponse(json_ret, status=501)
logger.debug(prepend_ip("Request received: %s" % request, request))
if request.method != 'POST':
return HttpResponseBadRequest("Must send frame as a POST")
if request.content_type != 'application/x-www-form-urlencoded':
return HttpResponseBadRequest(
"Content-Type must be 'application/x-www-form-urlencoded'")
if request.POST.get("image", "") == "":
return HttpResponseBadRequest("Missing 'image' parameter")
image = base64.b64decode(request.POST.get("image"))
logger.debug(
prepend_ip(
"Size of received image: %d" % (len(request.POST.get("image"))),
request))
save_debug_image(image, request)
image = imread(io.BytesIO(image))
logger.debug(prepend_ip("Performing pose detection process", request))
start = time.time()
datum = myOpenPose.Datum()
datum.cvInputData = image
myOpWrapper.emplaceAndPop([datum])
poses = datum.poseKeypoints
# Return the human pose poses, i.e., a [#people x #poses x 3]-dimensional numpy object with the poses of all the people on that image
elapsed = "%.3f" % ((time.time() - start) * 1000)
poses2 = np.around(poses, decimals=6)
# poses2 = np.rint(poses)
# Create a JSON response to be returned in a consistent manner
if isinstance(poses2, np.float32) or len(poses2) == 0:
num_poses = 0
ret = {"success": "false", "server_processing_time": elapsed}
else:
num_poses = len(poses2)
ret = {
"success": "true",
"server_processing_time": elapsed,
"poses": poses2.tolist()
}
logger.info(
prepend_ip("%s ms to detect %d poses: %s" % (elapsed, num_poses, ret),
request))
json_ret = json.dumps(ret)
return HttpResponse(json_ret)
def receive(self, text_data=None, bytes_data=None):
if bytes_data != None:
if myOpenPose == None:
error = "OpenPose not supported"
logger.error(error)
ret = {
"success": "false",
"error": error,
"server_processing_time": 0
}
else:
image = imread(io.BytesIO(bytes_data))
logger.debug("Performing pose detection process")
start = time.time()
datum = myOpenPose.Datum()
datum.cvInputData = image
myOpWrapper.emplaceAndPop([datum])
poses = datum.poseKeypoints
# Return the human pose poses, i.e., a [#people x #poses x 3]-dimensional numpy object with the poses of all the people on that image
elapsed = "%.3f" % ((time.time() - start) * 1000)
poses2 = np.around(poses, decimals=6)
# poses2 = np.rint(poses)
# Create a JSON response to be returned in a consistent manner
if isinstance(poses2, np.float32) or len(poses2) == 0:
num_poses = 0
ret = {
"success": "false",
"server_processing_time": elapsed
}
else:
num_poses = len(poses2)
ret = {
"success": "true",
"server_processing_time": elapsed,
"poses": poses2.tolist()
}
response = json.dumps(ret)
else:
logger.info("text_data=%s" % (text_data))
# If text is received, just echo back what we received.
response = text_data
logger.info("response=%s" % response)
self.send(text_data=response)
def openpose_detect(request):
"""
Use OpenPose to extract human skeleton points from a given image.
"""
if myOpenPose is None:
error = "OpenPose not supported on this server"
logger.error(prepend_ip("%s" % error, request))
ret = {"success": "false", "error": error}
json_ret = json.dumps(ret)
return HttpResponse(json_ret, status=501)
logger.debug(prepend_ip("Request received: %s" % request, request))
image = get_image_from_request(request, "pose")
if not isinstance(image, np.ndarray):
return image
logger.debug(prepend_ip("Performing pose detection process", request))
start = time.time()
datum = myOpenPose.Datum()
datum.cvInputData = image
myOpWrapper.emplaceAndPop([datum])
poses = datum.poseKeypoints
# Return the human pose poses, i.e., a [#people x #poses x 3]-dimensional numpy object with the poses of all the people on that image
elapsed = "%.3f" % ((time.time() - start) * 1000)
poses2 = np.around(poses, decimals=6)
# poses2 = np.rint(poses)
# Create a JSON response to be returned in a consistent manner
if isinstance(poses2, np.float32) or len(poses2) == 0:
num_poses = 0
ret = {"success": "false", "server_processing_time": elapsed}
else:
num_poses = len(poses2)
ret = {
"success": "true",
"server_processing_time": elapsed,
"poses": poses2.tolist()
}
logger.info(
prepend_ip("%s ms to detect %d poses: %s" % (elapsed, num_poses, ret),
request))
json_ret = json.dumps(ret)
return HttpResponse(json_ret)