def test_image_draw_mask():
mask = '../rainbow.jpg'
mask = Image.open(mask).convert('RGB')
mask = image_resize(mask, target_size=(500, 500))
visualize_image(mask, vis=True)
image_path = '../lena.png'
print('test with pil image')
img = Image.open(image_path).convert('RGB')
masked_img = image_draw_mask(image_resize(img, target_size=(500, 500)),
mask)
visualize_image(img, vis=True)
visualize_image(masked_img, vis=True)
print('test with numpy image with different transparency')
img = np.array(
Image.open(image_path).convert('RGB')).astype('float32') / 255.
img_bak = img.copy()
masked_img = image_draw_mask(image_resize(img, target_size=(500, 500)),
mask,
transparency=0.9)
visualize_image(img, vis=True)
visualize_image(masked_img, vis=True)
masked_img += 1
assert CHECK_EQ_NUMPY(
img_bak,
img), 'the original image should be equal to the backup version'
print('\n\nDONE! SUCCESSFUL!!\n')
def test_image_draw_mask():
# mask = '../rainbow.jpg'
mask = '/home/xinshuo/Dropbox/test7.png'
mask = Image.open(mask).convert('RGB')
# mask = image_resize(mask, target_size=(500, 500))
mask = image_resize(mask, target_size=(1148, 749))
visualize_image(mask, vis=True)
# image_path = '../lena.png'
image_path = '/home/xinshuo/test8.png'
print('test with pil image')
img = Image.open(image_path).convert('RGB')
img = image_resize(img, target_size=(1148, 749))
print(img.shape)
print(mask.shape)
masked_img = image_draw_mask(img, mask, transparency=0.5)
# visualize_image(img, vis=True)
# visualize_image(masked_img, vis=True)
save_image(masked_img, save_path='7716_new.png')
print('test with numpy image with different transparency')
img = np.array(
Image.open(image_path).convert('RGB')).astype('float32') / 255.
img_bak = img.copy()
masked_img = image_draw_mask(image_resize(img, target_size=(500, 500)),
mask,
transparency=0.9)
visualize_image(img, vis=True)
visualize_image(masked_img, vis=True)
masked_img += 1
assert CHECK_EQ_NUMPY(
img_bak,
img), 'the original image should be equal to the backup version'
print('\n\nDONE! SUCCESSFUL!!\n')
def test_image_resize():
print('test input image as numpy uint8 image with resize_factor')
img = (np.random.rand(400, 300, 1) * 255.).astype('uint8')
resized_img = image_resize(img, resize_factor=0.5)
assert resized_img.shape == (200, 150)
image_path = '../lena.png'
img = Image.open(image_path).convert('RGB')
im_height, im_width = img.size
visualize_image(img, vis=True)
print('test input image as pil image')
resized_img = image_resize(img, resize_factor=0.3)
assert resized_img.shape == (int(round(im_height * 0.3)),
int(round(im_width * 0.3)), 3)
print('test input image as numpy float32 image with target_size')
resized_img = image_resize(img, target_size=(1000, 1000))
assert resized_img.shape == (1000, 1000, 3)
visualize_image(resized_img, vis=True)
print(
'test input image as numpy float32 image with target_size and bilinear'
)
resized_img = image_resize(img,
target_size=(1000, 1000),
interp='bilinear')
assert resized_img.shape == (1000, 1000, 3)
visualize_image(resized_img, vis=True)
######################################## test failure cases
print('test random interp')
try:
resized_img = image_resize(img,
target_size=(800, 600),
interp='random')
sys.exit('\nwrong! never should be here\n\n')
except AssertionError:
print('the interp is not correct')
print('test both resize_factor and target_size')
try:
resized_img = image_resize(img,
resize_factor=0.4,
target_size=(800, 600),
interp='random')
sys.exit('\nwrong! never should be here\n\n')
except AssertionError:
print('the resize_factor and target_size coexist')
print('\n\nDONE! SUCCESSFUL!!\n')
def emotion_demo():
# parameters for loading data and images
detection_model_path = '../trained_models/detection_models/haarcascade_frontalface_default.xml'
emotion_model_path = '../trained_models/emotion_models/fer2013_mini_XCEPTION.102-0.66.hdf5'
emotion_labels = get_labels('fer2013')
# 目のカスケードファイル追加
lefteyecc__path = "../trained_models/detection_models/haarcascade_lefteye_2splits.xml"
righteyecc_path = "../trained_models/detection_models/haarcascade_righteye_2splits.xml"
nose_path = "../trained_models/detection_models/data_haarcascades_haarcascade_mcs_nose.xml"
lefteyecc = cv2.CascadeClassifier(lefteyecc__path)
righteyecc = cv2.CascadeClassifier(righteyecc_path)
nose = cv2.CascadeClassifier(nose_path)
lex = 0; ley = 0; lew = 0; leh = 0
rex = 0; rey = 0; rew = 0; reh = 0
nox = 0; noy = 0; now = 0; noh = 0
# dlib
dlib_ini()
# hyper-parameters for bounding boxes shape
frame_window = 10
emotion_offsets = (20, 40)
# loading models
face_detection = load_detection_model(detection_model_path)
emotion_classifier = load_model(emotion_model_path, compile=False)
# getting input model shapes for inference
emotion_target_size = emotion_classifier.input_shape[1:3]
# starting lists for calculating modes
emotion_window = []
global img, flag, slp_count
img = cv2.imread('../img/happy.png')
flag = 0
slp_count = 0
# dlib用グローバル変数
global gray_image, rgb_image, gray_face, mark_list
# starting video streaming
cv2.namedWindow('window_frame', cv2.WINDOW_NORMAL)
video_capture = cv2.VideoCapture(0) # 0は内蔵カメラ, 1はUSBカメラ
while True:
bgr_image = video_capture.read()[1]
gray_image = cv2.cvtColor(bgr_image, cv2.COLOR_BGR2GRAY)
rgb_image = cv2.cvtColor(bgr_image, cv2.COLOR_BGRA2RGBA)
faces = detect_faces(face_detection, gray_image)
for face_coordinates in faces:
# 目や鼻認識用
(x,y,w,h) = face_coordinates
video_face = gray_image[y:y+h,x:x+w]
x1, x2, y1, y2 = apply_offsets(face_coordinates, emotion_offsets)
gray_face = gray_image[y1:y2, x1:x2]
try:
gray_face = cv2.resize(gray_face, (emotion_target_size))
except:
continue
# ランドマーク検出
marks_list = marks_list_def(bgr_image, x, y, w, h)
print(marks_list)
gray_face = preprocess_input(gray_face, True)
gray_face = np.expand_dims(gray_face, 0)
gray_face = np.expand_dims(gray_face, -1)
emotion_prediction = emotion_classifier.predict(gray_face)
emotion_probability = np.max(emotion_prediction)
emotion_label_arg = np.argmax(emotion_prediction)
emotion_text = emotion_labels[emotion_label_arg]
emotion_window.append(emotion_text)
if len(emotion_window) > frame_window:
emotion_window.pop(0)
try:
emotion_mode = mode(emotion_window)
except:
continue
if flag == 0 or flag == 1:
if emotion_text == 'angry':
img = cv2.imread('../img/angry.png', -1)
color = emotion_probability * np.asarray((255, 0, 0))
elif emotion_text == 'sad':
img = cv2.imread('../img/sad.png', -1) # 関数にする
color = emotion_probability * np.asarray((0, 0, 255))
elif emotion_text == 'happy':
img = cv2.imread('../img/happy.png', -1)
color = emotion_probability * np.asarray((255, 255, 0))
elif emotion_text == 'surprise':
img = cv2.imread('../img/odoroki.png', -1)
color = emotion_probability * np.asarray((0, 255, 255))
else :
img = cv2.imread('../img/neutral.png', -1)
color = emotion_probability * np.asarray((0, 255, 0))
else:
if emotion_text == 'angry':
img = cv2.imread('../img/ikari.png', -1)
color = emotion_probability * np.asarray((255, 0, 0))
elif emotion_text == 'sad':
img = cv2.imread('../img/shock.png', -1)
color = emotion_probability * np.asarray((0, 0, 255))
elif emotion_text == 'happy':
img = cv2.imread('../img/kirakira.png', -1)
color = emotion_probability * np.asarray((255, 255, 0))
elif emotion_text == 'surprise':
img = cv2.imread('../img/bikkuri.png', -1)
color = emotion_probability * np.asarray((0, 255, 255))
else :
img = cv2.imread('../img/toumei.png', -1)
color = emotion_probability * np.asarray((0, 255, 0))
color = color.astype(int)
color = color.tolist()
if flag == 0:
draw_bounding_box(face_coordinates, rgb_image, color)
elif flag == 1:
rgb_image = draw_bounding_box2(face_coordinates, rgb_image, color, img, marks_list)
elif flag == 2:
overlay_pic = draw_bounding_box3(face_coordinates, rgb_image, color, img, marks_list)
rgb_image = overlay_pic
draw_text(face_coordinates, rgb_image, emotion_mode, color, 0, -45, 1, 1)
bgr_image = cv2.cvtColor(rgb_image, cv2.COLOR_RGB2BGR)
if flag == 0:
img = image_resize(img)
cv2.imshow('image', img)
cv2.destroyWindow('Window_frame')
elif flag == 1 or flag == 2:
cv2.destroyWindow('image')
cv2.imshow('window_frame', bgr_image)
cv2.waitKey(10)
# cv2.imshow('own_window', bgr_image)
if cv2.waitKey(1) & 0xFF == ord('z'):
flag = 0
elif cv2.waitKey(1) & 0xFF == ord('x'):
flag = 1
elif cv2.waitKey(1) & 0xFF == ord('c'):
flag = 2
elif cv2.waitKey(1) & 0xFF == ord('q'):
break
video_capture.release()
cv2.destroyAllWindows()