def predict_gender(name):
#loading data and images
image_path = source + name
m = re.search('.*(?=-)', name)
if m:
found = m.group(0)
else:
found = name
result = {'male': 0, 'female': 0, 'domain': found}
try:
rgb_image = load_image(image_path, grayscale=False)
except:
print('3. Doesn"t open')
if (os.path.isfile(image_path)):
os.remove(image_path)
to_remove.append(name)
return result
gray_image = load_image(image_path, grayscale=True)
gray_image = np.squeeze(gray_image)
gray_image = gray_image.astype('uint8')
#face and gender detection
faces = detect_faces(face_detection, gray_image)
if (len(faces) == 0):
print('no faces')
to_remove.append(name)
for face_coordinates in faces:
x1, x2, y1, y2 = apply_offsets(face_coordinates, gender_offsets)
rgb_face = rgb_image[y1:y2, x1:x2]
try:
rgb_face = cv2.resize(rgb_face, (gender_target_size))
except:
continue
rgb_face = preprocess_input(rgb_face, False)
rgb_face = np.expand_dims(rgb_face, 0)
gender_prediction = gender_classifier.predict(rgb_face)
gender_label_arg = np.argmax(gender_prediction)
gender_text = gender_labels[gender_label_arg]
if gender_text == gender_labels[0]:
result['female'] += 1
else:
result['male'] += 1
return result
def predict():
if request.method == "POST":
# get the file from the request
img_file = request.files["file"]
app.logger.info(img_file)
img_bytes = img_file.read()
img_bytes_arr = io.BytesIO(img_bytes)
app.logger.info(f"Loading image...")
img_arr = inference.load_image(img_bytes_arr)
app.logger.info(f"Making prediction...")
_, pred, prob = inference.make_prediction(model, img_arr)
pred = pred.replace("_", " ").title()
app.logger.info(f"Predicted {pred} with {round(prob,2)} probability")
return jsonify(food=pred, probability=str(round(prob, 3)))
else:
return "NIL"
def get_prediction():
image = request.files['file']
if image.filename != '':
fn = os.path.join(app.config['UPLOAD_FOLDER'],
image.filename + str(datetime.now().time()))
image.save(fn)
image = load_image(fn)
res, preprocessed_image = inference(image)
preprocessed_image = Image.fromarray(np.uint8(preprocessed_image *
255)).convert('RGB')
buffer = BytesIO()
preprocessed_image.save(buffer, format="PNG")
myimage = buffer.getvalue()
return jsonify(message=res, image=str(base64.b64encode(myimage))[2:-1])
def get_prediction():
image = request.files['file']
if image.filename != '':
fn = os.path.join(
app.config['UPLOAD_FOLDER'], image.filename +
str(datetime.now().time())
)
image.save(fn)
image = load_image(fn)
os.remove(fn)
res_image, res = run_on_image(image)
res_image = Image.fromarray(np.uint8(res_image)).convert('RGB')
image_height_over_width = res_image.size[1] / res_image.size[0]
#res_image = res_image.resize((260, int(image_height_over_width * 260)))
buffer = BytesIO()
res_image.save(buffer, format="PNG")
return_image = buffer.getvalue()
return jsonify(message=res, image=str(base64.b64encode(return_image))[2:-1])
elif is_EPGA:
EPGA_multi_model = Net(MODEL, 1, 12, 7, 5, 8, 2, False, False, None)
EPGA_multi_model.load_weights(EPGA_path)
else:
EPA_multi_model = Net(MODEL, 1, 9, is_dropout, 7, 5, 8, 2)
EPA_multi_model.load_weights(EPA_path)
EPGA_multi_model.summary()
emotion_window = []
gender_window = []
pose_window = []
age_window = []
# loading images
rgb_image = load_image(image_path, grayscale=False)
gray_image = load_image(image_path, grayscale=True)
gray_image = np.squeeze(gray_image)
gray_image = gray_image.astype('uint8')
start = time.clock()
face_cascade = cv2.CascadeClassifier(
'/home/user/anaconda3/pkgs/libopencv-3.4.2-hb342d67_1/share/OpenCV/haarcascades/haarcascade_frontalface_default.xml'
)
faces = face_cascade.detectMultiScale(gray_image, 1.3, 5)
print(np.shape(faces))
# faces = detect_faces(face_detection, gray_image)
for face_coordinates in faces:
x1, x2, y1, y2 = apply_offsets(face_coordinates, emotion_offsets)
rgb_face = rgb_image[y1:y2, x1:x2]
gray_face = gray_image[y1:y2, x1:x2]