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Hi Saeidn95,
Thank you for sharing the details.
For your information, I was able to replicate the issue you observed with the Face Detection step. But, I received some errors in the Python code and had to change a few lines for it to work. I share the results here:
C++
x: 807.676 y: 117.246 w: 256.247 h: 399.059
Python
x: 807.89813 y: 116.80127 w: 255.90338 h: 399.40616
C++:
Python:
Python Code:
On the other hand, for Landmark Detection I was able to show the landmarks in the C++ code. However, the Python code resulted in the error: "AttributeError: 'LandmarksDetector' object has no attribute 'rois'". Are there any other modifications that need to be made to the Python demo to successfully run the "facial-landmarks-35-adas-0002" model? I show my results and my full landmarks_detector.py below:
C++:
Python:
Python (landmarks_detector.py) code:
import numpy as np
from utils import cut_rois, resize_input
from ie_module import Module
from copy import deepcopy
class LandmarksDetector(Module):
POINTS_NUMBER = 35
def __init__(self, core, model):
super(LandmarksDetector, self).__init__(core, model, 'Landmarks Detection')
if len(self.model.inputs) != 1:
raise RuntimeError("The model expects 1 input layer")
if len(self.model.outputs) != 1:
raise RuntimeError("The model expects 1 output layer")
self.input_tensor_name = self.model.inputs[0].get_any_name()
self.input_shape = self.model.inputs[0].shape
self.nchw_layout = self.input_shape[1] == 3
output_shape = self.model.outputs[0].shape
if not np.array_equal([1, self.POINTS_NUMBER * 2], output_shape):
raise RuntimeError("The model expects output shape {}, got {}".format(
[1, self.POINTS_NUMBER * 2], output_shape))
def preprocess(self, frame, rois):
inputs = cut_rois(frame, rois)
inputs = [resize_input(input, self.input_shape, self.nchw_layout) for input in inputs]
return inputs
def enqueue(self, input):
return super(LandmarksDetector, self).enqueue({self.input_tensor_name: input})
def start_async(self, frame, rois):
inputs = self.preprocess(frame, rois)
for input in inputs:
self.enqueue(input)
def postprocess(self):
results = [out.reshape((-1, 2)).astype(np.float64) for out in self.get_outputs()]
self.results_sc = deepcopy(results)
for k in range(len(results)):
for i in range(len(results[k])):
self.results_sc[k][i][0] = self.results_sc[k][i][0] * self.rois[k].size[0] + self.rois[k].position[0]
self.results_sc[k][i][1] = self.results_sc[k][i][1] * self.rois[k].size[1]+ self.rois[k].position[1]
print("x: ", self.results_sc[k][i][0], " y: ", self.results_sc[k][i][1])
return results
Regards,
Megat
Hi Saeidn95,
For your information, we have escalated to the relevant team regarding the Face Detection issue for further investigation.
On the other hand, after inserting self.rois = rois, I was able to get the first landmark however, I received an error before getting the second landmark. The error seems to happen in the face_identifier.py. Did you encountered such error before? I share the result below:
Regards,
Megat
Hi Saeidn95,
Thank you for the guide you provided.
I was able to run the Python demo for the Landmarks detection without any errors. However, my results only show one landmark instead of 35. The landmarks also did not appear in the image results. It seems like the code was unable to draw all 35 landmarks. I share the result here:
To help ease this investigation, is it possible for you to provide all the Python files included in the ..\open_model_zoo\demos\face_recognition_demo\python folder? If you would like to send it to me privately you can email me the files at megatx.muhammad.firdaus.bin.sahrir@intel.com.
Regards,
Megat
