From 39d29acaac27419c03dc81b75688ffffdc553c85 Mon Sep 17 00:00:00 2001 From: Jonas Larsson Date: Fri, 22 Jan 2021 15:18:11 -0800 Subject: [PATCH] Use pycoral APIs --- .gitignore | 1 + gstreamer.py | 43 +++++++------ install_requirements.sh | 2 +- pose_engine.py | 81 ++++++++++++++----------- posenet_lib/aarch64/posenet_decoder.so | Bin 0 -> 330168 bytes posenet_lib/armv7a/posenet_decoder.so | Bin 0 -> 186104 bytes posenet_lib/x86_64/posenet_decoder.so | Bin 0 -> 285224 bytes 7 files changed, 72 insertions(+), 55 deletions(-) create mode 100644 .gitignore create mode 100755 posenet_lib/aarch64/posenet_decoder.so create mode 100755 posenet_lib/armv7a/posenet_decoder.so create mode 100755 posenet_lib/x86_64/posenet_decoder.so diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..ed8ebf5 --- /dev/null +++ b/.gitignore @@ -0,0 +1 @@ +__pycache__ \ No newline at end of file diff --git a/gstreamer.py b/gstreamer.py index 7440af5..fad8711 100644 --- a/gstreamer.py +++ b/gstreamer.py @@ -64,14 +64,15 @@ def on_new_sample(sink, appsrc, overlay, screen_size, appsink_size, return Gst.FlowReturn.OK -def detectCoralDevBoard(): - try: - if 'MX8MQ' in open('/sys/firmware/devicetree/base/model').read(): - print('Detected Edge TPU dev board.') - return True - except: - pass - return False +def get_dev_board_model(): + try: + model = open('/sys/firmware/devicetree/base/model').read().lower() + if 'mx8mq' in model: + return 'mx8mq' + if 'mt8167' in model: + return 'mt8167' + except: pass + return None def run_pipeline(user_function, @@ -90,17 +91,21 @@ def run_pipeline(user_function, SRC_CAPS = 'video/x-raw,width={width},height={height},framerate=30/1' APPSRC_PIPELINE = 'appsrc name=appsrc ! {appsrc_caps} ' - if detectCoralDevBoard(): - print("***\nNOTE: On a Coral devboard use bodypix_gl_imx.py instead for much faster performance.\n***") - scale_caps = None - PIPELINE += """ - ! decodebin ! glupload ! glvideoflip video-direction={direction} ! {leaky_q} - ! glfilterbin filter=glbox name=glbox ! {sink_caps} ! {sink_element} - """ - APPSRC_PIPELINE += """ - ! {leaky_q} ! videoconvert n-threads=4 - ! rsvgoverlay name=overlay ! waylandsink - """ + coral = get_dev_board_model() + if coral: + print("***\nNOTE: On a Coral devboard use bodypix_gl_imx.py instead for much faster performance.\n***") + if 'mx8mq' in coral: + scale_caps = None + PIPELINE += """ + ! decodebin ! glupload ! glvideoflip video-direction={direction} ! {leaky_q} + ! glfilterbin filter=glbox name=glbox ! {sink_caps} ! {sink_element} + """ + APPSRC_PIPELINE += """ + ! {leaky_q} ! videoconvert n-threads=4 + ! rsvgoverlay name=overlay ! waylandsink + """ + elif 'mt8167' in coral: + print('Not implemented') else: # raspberry pi or linux scale = min(appsink_size[0] / src_size[0], appsink_size[1] / src_size[1]) scale = tuple(int(x * scale) for x in src_size) diff --git a/install_requirements.sh b/install_requirements.sh index b4cd110..e6b774b 100644 --- a/install_requirements.sh +++ b/install_requirements.sh @@ -13,7 +13,7 @@ # See the License for the specific language governing permissions and # limitations under the License. -if grep -s -q "MX8MQ" /sys/firmware/devicetree/base/model; then +if grep -s -q "Mendel" /etc/os-release; then echo "Installing DevBoard specific dependencies" sudo apt-get install python3-pip python3-scipy sudo pip3 install svgwrite diff --git a/pose_engine.py b/pose_engine.py index e410bc1..1b20dea 100644 --- a/pose_engine.py +++ b/pose_engine.py @@ -14,16 +14,23 @@ import collections import math +import os +import time import numpy as np -from pkg_resources import parse_version -from edgetpu import __version__ as edgetpu_version -assert parse_version(edgetpu_version) >= parse_version('2.11.1'), \ - 'This demo requires Edge TPU version >= 2.11.1' -from edgetpu.basic.basic_engine import BasicEngine from PIL import Image +from tflite_runtime.interpreter import load_delegate +from tflite_runtime.interpreter import Interpreter + +from pycoral.adapters.common import output_tensor +from pycoral.utils.edgetpu import run_inference + +EDGETPU_SHARED_LIB = 'libedgetpu.so.1' +POSENET_SHARED_LIB = os.path.join( + 'posenet_lib', os.uname().machine, 'posenet_decoder.so') + EDGES = ( ('nose', 'left eye'), ('nose', 'right eye'), @@ -117,7 +124,7 @@ def __repr__(self): return 'Pose({}, {})'.format(self.keypoints, self.score) -class PoseEngine(BasicEngine): +class PoseEngine: """Engine used for pose tasks.""" def __init__(self, model_path, mirror=False): @@ -130,38 +137,40 @@ def __init__(self, model_path, mirror=False): Raises: ValueError: An error occurred when model output is invalid. """ - BasicEngine.__init__(self, model_path) self._mirror = mirror - self._input_tensor_shape = self.get_input_tensor_shape() + edgetpu_delegate = load_delegate(EDGETPU_SHARED_LIB) + posenet_decoder_delegate = load_delegate(POSENET_SHARED_LIB) + self._interpreter = Interpreter( + model_path, experimental_delegates=[edgetpu_delegate, posenet_decoder_delegate]) + self._interpreter.allocate_tensors() + self._input_tensor_shape = self._interpreter.get_input_details()[0]['shape'] if (self._input_tensor_shape.size != 4 or self._input_tensor_shape[3] != 3 or self._input_tensor_shape[0] != 1): raise ValueError( ('Image model should have input shape [1, height, width, 3]!' ' This model has {}.'.format(self._input_tensor_shape))) - _, self.image_height, self.image_width, self.image_depth = self.get_input_tensor_shape() + _, self.image_height, self.image_width, self.image_depth = self._input_tensor_shape - # The API returns all the output tensors flattened and concatenated. We - # have to figure out the boundaries from the tensor shapes & sizes. - offset = 0 - self._output_offsets = [0] - for size in self.get_all_output_tensors_sizes(): - offset += int(size) - self._output_offsets.append(offset) # Auto-detect stride size def calcStride(h,w,L): return int((2*h*w)/(math.sqrt(h**2 + 4*h*L*w - 2*h*w + w**2) - h - w)) - heatmap_size = self.get_output_tensor_size(4) + details = self._interpreter.get_output_details()[4] + self.heatmap_zero_point = details['quantization_parameters']['zero_points'][0] + self.heatmap_scale = details['quantization_parameters']['scales'][0] + heatmap_size = self._interpreter.tensor(details['index'])().nbytes self.stride = calcStride(self.image_height, self.image_width, heatmap_size) self.heatmap_size = (self.image_width // self.stride + 1, self.image_height // self.stride + 1) + details = self._interpreter.get_output_details()[5] + self.parts_zero_point = details['quantization_parameters']['zero_points'][0] + self.parts_scale = details['quantization_parameters']['scales'][0] + print("Heatmap size: ", self.heatmap_size) print("Stride: ", self.stride, self.heatmap_size) - def _zip_output(self, output): - return [output[i:j] for i, j in zip(self._output_offsets, self._output_offsets[1:])] def DetectPosesInImage(self, img): """Detects poses in a given image. @@ -183,8 +192,7 @@ def DetectPosesInImage(self, img): assert (img.shape == tuple(self._input_tensor_shape[1:])) # Run the inference (API expects the data to be flattened) - inference_time, output = self.run_inference(img.flatten()) - outputs = self._zip_output(output) + inference_time, outputs = self.run_inference(img.flatten()) poses = self._parse_poses(outputs) heatmap, bodyparts = self._parse_heatmaps(outputs) return inference_time, poses, heatmap, bodyparts @@ -196,7 +204,6 @@ def DetectPosesInTensor(self, tensor): return inference_time, poses, heatmap, bodyparts def ParseOutputs(self, outputs): - outputs = self._zip_output(outputs) poses = self._parse_poses(outputs) heatmap, bodyparts = self._parse_heatmaps(outputs) return poses, heatmap, bodyparts @@ -206,7 +213,6 @@ def _parse_poses(self, outputs): keypoint_scores = outputs[1].reshape(-1, len(KEYPOINTS)) pose_scores = outputs[2] nposes = int(outputs[3][0]) - assert nposes < outputs[0].shape[0] # Convert the poses to a friendlier format of keypoints with associated # scores. @@ -228,17 +234,22 @@ def softmax(self, y, axis): return y / np.expand_dims(np.sum(y, axis = axis), axis) def _parse_heatmaps(self, outputs): - if len(outputs) < 5: return None, None - heatmap = np.reshape(outputs[4], - [self.heatmap_size[1], - self.heatmap_size[0]]) - - # If part heatmaps tensor is not present, move on - if len(outputs) < 6: - return heatmap, None - - part_heatmap = np.reshape(outputs[5], - [self.heatmap_size[1], - self.heatmap_size[0], -1]) + # Heatmaps are really float32. + heatmap = (outputs[4].astype(np.float32) - self.heatmap_zero_point) * self.heatmap_scale + heatmap = np.reshape(heatmap, [self.heatmap_size[1], self.heatmap_size[0]]) + part_heatmap = (outputs[5].astype(np.float32) - self.parts_zero_point) * self.parts_scale + part_heatmap = np.reshape(part_heatmap, [self.heatmap_size[1], self.heatmap_size[0], -1]) part_heatmap = self.softmax(part_heatmap, axis=2) return heatmap, part_heatmap + + def run_inference(self, input): + start_time = time.monotonic() + run_inference(self._interpreter, input) + duration_ms = (time.monotonic() - start_time) * 1000 + + output = [] + for details in self._interpreter.get_output_details(): + tensor = self._interpreter.get_tensor(details['index']) + output.append(tensor) + + return (duration_ms, output) diff --git a/posenet_lib/aarch64/posenet_decoder.so b/posenet_lib/aarch64/posenet_decoder.so new file mode 100755 index 0000000000000000000000000000000000000000..4c3777a01e23be72c2392beb4e6b563b67b5ed89 GIT binary patch literal 330168 zcmeFa34Bvk`aXUeDpW))i&#*hRMDz{NxBdeF$4-lw%AszYnrqv4R(obS_&vrDq`I$ 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