 | 1 | initial version |
I tried to do a facial recognition using Facenet, where in the program, this error shows up: E1101:Module 'cv2' has no 'VideoCapture' member E1101:Module 'cv2' has no 'INTER_CUBIC' member E1101:Module 'cv2' has no 'resize' member E1101:Module 'cv2' has no 'putText' member E1101:Module 'cv2' has no 'rectangle' member E1101:Module 'cv2' has no 'imshow' member
I tried to do a facial recognition using Facenet, where in the program, this error shows up: E1101:Module 'cv2' has no 'VideoCapture' member E1101:Module 'cv2' has no 'INTER_CUBIC' member E1101:Module 'cv2' has no 'resize' member E1101:Module 'cv2' has no 'putText' member E1101:Module 'cv2' has no 'rectangle' member E1101:Module 'cv2' has no 'imshow' member
the original Code: from __future__ import absolute_import from __future__ import division from __future__ import print_function
import tensorflow as tf from scipy import misc import cv2 import matplotlib.pyplot as plt import numpy as np import argparse import facenet import detect_face import os from os.path import join as pjoin import sys import time import copy import math import pickle from sklearn.svm import SVC from sklearn.externals import joblib
print('Creating networks and loading parameters') with tf.Graph().as_default(): gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.6) sess = tf.Session(config=tf.ConfigProto(gpu_options=gpu_options, log_device_placement=False)) with sess.as_default(): pnet, rnet, onet = detect_face.create_mtcnn(sess, './d_npy')
minsize = 20 # minimum size of face
threshold = [0.6, 0.7, 0.7] # three steps's threshold
factor = 0.709 # scale factor
margin = 44
frame_interval = 3
batch_size = 1000
image_size = 182
input_image_size = 160
HumanNames = os.listdir("./input_dir")
HumanNames.sort()
print('Loading feature extraction model')
modeldir = './pre_model/20170511-185253.pb'
facenet.load_model(modeldir)
images_placeholder = tf.get_default_graph().get_tensor_by_name("input:0")
embeddings = tf.get_default_graph().get_tensor_by_name("embeddings:0")
phase_train_placeholder = tf.get_default_graph().get_tensor_by_name("phase_train:0")
embedding_size = embeddings.get_shape()[1]
classifier_filename = './my_class/my_classifier.pkl'
classifier_filename_exp = os.path.expanduser(classifier_filename)
with open(classifier_filename_exp, 'rb') as infile:
(model, class_names) = pickle.load(infile)
print('load classifier file-> %s' % classifier_filename_exp)
video_capture = cv2.VideoCapture('./test.mp4')
c = 0
counter = 1
# #video writer
fourcc = cv2.VideoWriter_fourcc(*'DIVX')
out = cv2.VideoWriter('3F_0726.avi', fourcc, fps=14, frameSize=(640,480))
print('Start Recognition!')
prevTime = 0
while True:
ret, frame = video_capture.read()
frame = cv2.resize(frame, (0,0), fx=0.5, fy=0.5) #resize frame (optional)
curTime = time.time()+1 # calc fps
timeF = frame_interval
counter += 1
if (counter % 12 == 0):
if (c % timeF == 0):
find_results = []
if frame.ndim == 2:
frame = facenet.to_rgb(frame)
frame = frame[:, :, 0:3]
bounding_boxes, _ = detect_face.detect_face(frame, minsize, pnet, rnet, onet, threshold, factor)
nrof_faces = bounding_boxes.shape[0]
print('Detected_FaceNum: %d' % nrof_faces)
if nrof_faces > 0:
det = bounding_boxes[:, 0:4]
img_size = np.asarray(frame.shape)[0:2]
cropped = []
scaled = []
scaled_reshape = []
bb = np.zeros((nrof_faces,4), dtype=np.int32)
for i in range(nrof_faces):
emb_array = np.zeros((1, embedding_size))
bb[i][0] = det[i][0]
bb[i][1] = det[i][1]
bb[i][2] = det[i][2]
bb[i][3] = det[i][3]
# inner exception
if bb[i][0] <= 0 or bb[i][1] <= 0 or bb[i][2] >= len(frame[0]) or bb[i][3] >= len(frame):
print('face is inner of range!')
continue
cropped.append(frame[bb[i][1]:bb[i][3], bb[i][0]:bb[i][2], :])
cropped[i] = facenet.flip(cropped[i], False)
scaled.append(misc.imresize(cropped[i], (image_size, image_size), interp='bilinear'))
scaled[i] = cv2.resize(scaled[i], (input_image_size,input_image_size),
interpolation=cv2.INTER_CUBIC)
scaled[i] = facenet.prewhiten(scaled[i])
scaled_reshape.append(scaled[i].reshape(-1,input_image_size,input_image_size,3))
feed_dict = {images_placeholder: scaled_reshape[i], phase_train_placeholder: False}
emb_array[0, :] = sess.run(embeddings, feed_dict=feed_dict)
predictions = model.predict_proba(emb_array)
print(predictions)
best_class_indices = np.argmax(predictions, axis=1)
print(best_class_indices)
best_class_probabilities = predictions[np.arange(len(best_class_indices)), best_class_indices]
print(best_class_probabilities)
cv2.rectangle(frame, (bb[i][0], bb[i][1]), (bb[i][2], bb[i][3]), (0, 255, 0), 2) #boxing face
#plot result idx under box
text_x = bb[i][0]
text_y = bb[i][3] + 20
print('result: ', best_class_indices[0])
print(best_class_indices)
print(HumanNames)
for H_i in HumanNames:
print(H_i)
if HumanNames[best_class_indices[0]] == H_i:
result_names = HumanNames[best_class_indices[0]]
cv2.putText(frame, result_names, (text_x, text_y), cv2.FONT_HERSHEY_COMPLEX_SMALL,
1, (0, 0, 255), thickness=1, lineType=2)
else:
print('Unable to align')
sec = curTime - prevTime
prevTime = curTime
fps = 1 / (sec)
str = 'FPS: %2.3f' % fps
text_fps_x = len(frame[0]) - 150
text_fps_y = 20
cv2.putText(frame, str, (text_fps_x, text_fps_y),
cv2.FONT_HERSHEY_COMPLEX_SMALL, 1, (0, 0, 0), thickness=1, lineType=2)
# c+=1
cv2.imshow('Video', frame)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
video_capture.release()
# #video writer
out.release()
cv2.destroyAllWindows()
I tried to do a facial recognition using Facenet, where in the program, this error shows up:
E1101:Module 'cv2' has no 'VideoCapture' member
E1101:Module 'cv2' has no 'INTER_CUBIC' member
E1101:Module 'cv2' has no 'resize' member
E1101:Module 'cv2' has no 'putText' member
E1101:Module 'cv2' has no 'rectangle' member
E1101:Module 'cv2' has no 'imshow' member
the original Code: from __future__ import absolute_import from __future__ import division from __future__ import print_function
import tensorflow as tf from scipy import misc import cv2 import matplotlib.pyplot as plt import numpy as np import argparse import facenet import detect_face import os from os.path import join as pjoin import sys import time import copy import math import pickle from sklearn.svm import SVC from sklearn.externals import joblib
print('Creating networks and loading parameters') with tf.Graph().as_default(): gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.6) sess = tf.Session(config=tf.ConfigProto(gpu_options=gpu_options, log_device_placement=False)) with sess.as_default(): pnet, rnet, onet = detect_face.create_mtcnn(sess, './d_npy')
minsize = 20 # minimum size of face
threshold = [0.6, 0.7, 0.7] # three steps's threshold
factor = 0.709 # scale factor
margin = 44
frame_interval = 3
batch_size = 1000
image_size = 182
input_image_size = 160
HumanNames = os.listdir("./input_dir")
HumanNames.sort()
print('Loading feature extraction model')
modeldir = './pre_model/20170511-185253.pb'
facenet.load_model(modeldir)
images_placeholder = tf.get_default_graph().get_tensor_by_name("input:0")
embeddings = tf.get_default_graph().get_tensor_by_name("embeddings:0")
phase_train_placeholder = tf.get_default_graph().get_tensor_by_name("phase_train:0")
embedding_size = embeddings.get_shape()[1]
classifier_filename = './my_class/my_classifier.pkl'
classifier_filename_exp = os.path.expanduser(classifier_filename)
with open(classifier_filename_exp, 'rb') as infile:
(model, class_names) = pickle.load(infile)
print('load classifier file-> %s' % classifier_filename_exp)
video_capture = cv2.VideoCapture('./test.mp4')
c = 0
counter = 1
# #video writer
fourcc = cv2.VideoWriter_fourcc(*'DIVX')
out = cv2.VideoWriter('3F_0726.avi', fourcc, fps=14, frameSize=(640,480))
print('Start Recognition!')
prevTime = 0
while True:
ret, frame = video_capture.read()
frame = cv2.resize(frame, (0,0), fx=0.5, fy=0.5) #resize frame (optional)
curTime = time.time()+1 # calc fps
timeF = frame_interval
counter += 1
if (counter % 12 == 0):
if (c % timeF == 0):
find_results = []
if frame.ndim == 2:
frame = facenet.to_rgb(frame)
frame = frame[:, :, 0:3]
bounding_boxes, _ = detect_face.detect_face(frame, minsize, pnet, rnet, onet, threshold, factor)
nrof_faces = bounding_boxes.shape[0]
print('Detected_FaceNum: %d' % nrof_faces)
if nrof_faces > 0:
det = bounding_boxes[:, 0:4]
img_size = np.asarray(frame.shape)[0:2]
cropped = []
scaled = []
scaled_reshape = []
bb = np.zeros((nrof_faces,4), dtype=np.int32)
for i in range(nrof_faces):
emb_array = np.zeros((1, embedding_size))
bb[i][0] = det[i][0]
bb[i][1] = det[i][1]
bb[i][2] = det[i][2]
bb[i][3] = det[i][3]
# inner exception
if bb[i][0] <= 0 or bb[i][1] <= 0 or bb[i][2] >= len(frame[0]) or bb[i][3] >= len(frame):
print('face is inner of range!')
continue
cropped.append(frame[bb[i][1]:bb[i][3], bb[i][0]:bb[i][2], :])
cropped[i] = facenet.flip(cropped[i], False)
scaled.append(misc.imresize(cropped[i], (image_size, image_size), interp='bilinear'))
scaled[i] = cv2.resize(scaled[i], (input_image_size,input_image_size),
interpolation=cv2.INTER_CUBIC)
scaled[i] = facenet.prewhiten(scaled[i])
scaled_reshape.append(scaled[i].reshape(-1,input_image_size,input_image_size,3))
feed_dict = {images_placeholder: scaled_reshape[i], phase_train_placeholder: False}
emb_array[0, :] = sess.run(embeddings, feed_dict=feed_dict)
predictions = model.predict_proba(emb_array)
print(predictions)
best_class_indices = np.argmax(predictions, axis=1)
print(best_class_indices)
best_class_probabilities = predictions[np.arange(len(best_class_indices)), best_class_indices]
print(best_class_probabilities)
cv2.rectangle(frame, (bb[i][0], bb[i][1]), (bb[i][2], bb[i][3]), (0, 255, 0), 2) #boxing face
#plot result idx under box
text_x = bb[i][0]
text_y = bb[i][3] + 20
print('result: ', best_class_indices[0])
print(best_class_indices)
print(HumanNames)
for H_i in HumanNames:
print(H_i)
if HumanNames[best_class_indices[0]] == H_i:
result_names = HumanNames[best_class_indices[0]]
cv2.putText(frame, result_names, (text_x, text_y), cv2.FONT_HERSHEY_COMPLEX_SMALL,
1, (0, 0, 255), thickness=1, lineType=2)
else:
print('Unable to align')
sec = curTime - prevTime
prevTime = curTime
fps = 1 / (sec)
str = 'FPS: %2.3f' % fps
text_fps_x = len(frame[0]) - 150
text_fps_y = 20
cv2.putText(frame, str, (text_fps_x, text_fps_y),
cv2.FONT_HERSHEY_COMPLEX_SMALL, 1, (0, 0, 0), thickness=1, lineType=2)
# c+=1
cv2.imshow('Video', frame)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
video_capture.release()
# #video writer
out.release()
cv2.destroyAllWindows()
getting this error: I tried to do a facial recognition using Facenet, where in the program, this error shows up: E1101:Module 'cv2' has no 'VideoCapture' member E1101:Module 'cv2' has no 'INTER_CUBIC' member E1101:Module 'cv2' has no 'resize' member E1101:Module 'cv2' has no 'putText' member E1101:Module 'cv2' has no 'rectangle' member E1101:Module 'cv2' has no 'imshow' member
getting this error: I tried to do a facial recognition using Facenet, where in the program, this error shows up:
E1101:Module 'cv2' has no 'VideoCapture' member
E1101:Module 'cv2' has no 'INTER_CUBIC' member
E1101:Module 'cv2' has no 'resize' member
E1101:Module 'cv2' has no 'putText' member
E1101:Module 'cv2' has no 'rectangle' member
E1101:Module 'cv2' has no 'imshow' member
code:
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function print_function
import tensorflow as tf
from scipy import misc
import cv2
import matplotlib.pyplot as plt
import numpy as np
import argparse
import facenet
import detect_face
import os
from os.path import join as pjoin
import sys
import time
import copy
import math
import pickle
from sklearn.svm import SVC
from sklearn.externals import joblib joblib
print('Creating networks and loading parameters')
with tf.Graph().as_default():
gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.6)
sess = tf.Session(config=tf.ConfigProto(gpu_options=gpu_options, log_device_placement=False))
with sess.as_default():
pnet, rnet, onet = detect_face.create_mtcnn(sess, './d_npy')
'./d_npy')
minsize = 20 # minimum size of face
threshold = [0.6, 0.7, 0.7] # three steps's threshold
factor = 0.709 # scale factor
margin = 44
frame_interval = 3
batch_size = 1000
image_size = 182
input_image_size = 160
HumanNames = os.listdir("./input_dir")
HumanNames.sort()
print('Loading feature extraction model')
modeldir = './pre_model/20170511-185253.pb'
facenet.load_model(modeldir)
images_placeholder = tf.get_default_graph().get_tensor_by_name("input:0")
embeddings = tf.get_default_graph().get_tensor_by_name("embeddings:0")
phase_train_placeholder = tf.get_default_graph().get_tensor_by_name("phase_train:0")
embedding_size = embeddings.get_shape()[1]
classifier_filename = './my_class/my_classifier.pkl'
classifier_filename_exp = os.path.expanduser(classifier_filename)
with open(classifier_filename_exp, 'rb') as infile:
(model, class_names) = pickle.load(infile)
print('load classifier file-> %s' % classifier_filename_exp)
video_capture = cv2.VideoCapture('./test.mp4')
c = 0
counter = 1
# #video writer
fourcc = cv2.VideoWriter_fourcc(*'DIVX')
out = cv2.VideoWriter('3F_0726.avi', fourcc, fps=14, frameSize=(640,480))
print('Start Recognition!')
prevTime = 0
while True:
ret, frame = video_capture.read()
frame = cv2.resize(frame, (0,0), fx=0.5, fy=0.5) #resize frame (optional)
curTime = time.time()+1 # calc fps
timeF = frame_interval
counter += 1
if (counter % 12 == 0):
if (c % timeF == 0):
find_results = []
if frame.ndim == 2:
frame = facenet.to_rgb(frame)
frame = frame[:, :, 0:3]
bounding_boxes, _ = detect_face.detect_face(frame, minsize, pnet, rnet, onet, threshold, factor)
nrof_faces = bounding_boxes.shape[0]
print('Detected_FaceNum: %d' % nrof_faces)
if nrof_faces > 0:
det = bounding_boxes[:, 0:4]
img_size = np.asarray(frame.shape)[0:2]
cropped = []
scaled = []
scaled_reshape = []
bb = np.zeros((nrof_faces,4), dtype=np.int32)
for i in range(nrof_faces):
emb_array = np.zeros((1, embedding_size))
bb[i][0] = det[i][0]
bb[i][1] = det[i][1]
bb[i][2] = det[i][2]
bb[i][3] = det[i][3]
# inner exception
if bb[i][0] <= 0 or bb[i][1] <= 0 or bb[i][2] >= len(frame[0]) or bb[i][3] >= len(frame):
print('face is inner of range!')
continue
cropped.append(frame[bb[i][1]:bb[i][3], bb[i][0]:bb[i][2], :])
cropped[i] = facenet.flip(cropped[i], False)
scaled.append(misc.imresize(cropped[i], (image_size, image_size), interp='bilinear'))
scaled[i] = cv2.resize(scaled[i], (input_image_size,input_image_size),
interpolation=cv2.INTER_CUBIC)
scaled[i] = facenet.prewhiten(scaled[i])
scaled_reshape.append(scaled[i].reshape(-1,input_image_size,input_image_size,3))
feed_dict = {images_placeholder: scaled_reshape[i], phase_train_placeholder: False}
emb_array[0, :] = sess.run(embeddings, feed_dict=feed_dict)
predictions = model.predict_proba(emb_array)
print(predictions)
best_class_indices = np.argmax(predictions, axis=1)
print(best_class_indices)
best_class_probabilities = predictions[np.arange(len(best_class_indices)), best_class_indices]
print(best_class_probabilities)
cv2.rectangle(frame, (bb[i][0], bb[i][1]), (bb[i][2], bb[i][3]), (0, 255, 0), 2) #boxing face
#plot result idx under box
text_x = bb[i][0]
text_y = bb[i][3] + 20
print('result: ', best_class_indices[0])
print(best_class_indices)
print(HumanNames)
for H_i in HumanNames:
print(H_i)
if HumanNames[best_class_indices[0]] == H_i:
result_names = HumanNames[best_class_indices[0]]
cv2.putText(frame, result_names, (text_x, text_y), cv2.FONT_HERSHEY_COMPLEX_SMALL,
1, (0, 0, 255), thickness=1, lineType=2)
else:
print('Unable to align')
sec = curTime - prevTime
prevTime = curTime
fps = 1 / (sec)
str = 'FPS: %2.3f' % fps
text_fps_x = len(frame[0]) - 150
text_fps_y = 20
cv2.putText(frame, str, (text_fps_x, text_fps_y),
cv2.FONT_HERSHEY_COMPLEX_SMALL, 1, (0, 0, 0), thickness=1, lineType=2)
# c+=1
cv2.imshow('Video', frame)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
video_capture.release()
# #video writer
out.release()
cv2.destroyAllWindows()
getting this error: I tried to do a facial recognition using Facenet, where in the program, this error shows up: E1101:Module 'cv2' has no 'VideoCapture' member E1101:Module 'cv2' has no 'INTER_CUBIC' member E1101:Module 'cv2' has no 'resize' member E1101:Module 'cv2' has no 'putText' member E1101:Module 'cv2' has no 'rectangle' member E1101:Module 'cv2' has no 'imshow' member