def run_file(q, file):
cam = cv2.VideoCapture(file)
frame_num = 1
time.sleep(2)
success = True
while success == True:
reading_start = time.time()
success, image = cam.read()
if success:
# Our operations on the image come here
# convert image to cv2 format to RGB format
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
image = Image.fromarray(image)
image = image.resize((224, 224))
image_array = np.asarray(image)
# Add resulting frame to Queue for processing on controller
q.put(image_array)
image = image.resize((672, 672))
image_array = np.asarray(image)
# Display the resulting frame
cv2.imshow('frame', image_array)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
time.sleep(0.045)
frame_num += 1
#print("VPP Iteration Duration -> %s seconds ---" % (time.time() - reading_start))
# When everything done, release the capture
cam.release()
cv2.destroyAllWindows()
def imgToBytes(image):
assert isinstance(image, Image.Image) and 'give me correct PIL Image'
# resize
data = io.BytesIO()
image.resize(size=(IMAGE_SIZE, IMAGE_SIZE),
resample=Image.LANCZOS).save(data, 'JPEG', quality=90)
return data.getvalue()
def load_images_test(name, size):
x_images = []
y_images = []
for file in tqdm(os.listdir(name)):
image = Image.open(name + file)
if image.mode != "RGB":
image.convert("RGB")
x_image = image.resize(
(size[0] // 2,
size[1] // 2)) #change to 3, 4, or 8 for varying scale factors
x_image = x_image.resize(size, Image.BICUBIC)
x_image = np.array(x_image)
y_image = image.resize(size)
y_image = np.array(y_image)
x_images.append(x_image)
y_images.append(y_image)
x_images = np.array(x_images)
y_images = np.array(y_images)
x_images = x_images / 255
y_images = y_images / 255
x_images = x_images.reshape(
x_images.shape[0], size[0] // 1, size[1] // 1,
3) #for grayscale, change the number of channels to 1
y_images = y_images.reshape(
y_images.shape[0], size[0] // 1, size[1] // 1,
3) #for grayscale, change the number of channels to 1
return x_images, y_images
def processImage(imagePath):
image = Image.open(imagePath)
(currw, currh) = image.size
if currw >= currh:
image = image.resize((width, int(float(currh) * float(width) / float(currw))))
else:
image = image.resize((int(float(currw) * float(height) / float(currh)), height))
background = Image.new('RGB', (width, height), (0, 0, 0))
background.paste(
image, (int((width - image.size[0]) / 2), int((height - image.size[1]) / 2))
)
return background
def predictionImage():
# remove warnins for compile model after loading
with CustomObjectScope({'GlorotUniform': glorot_uniform()}):
test_model = load_model('../model/best_model.h5')
# open image on input path
with open('binarizedImage.jpg', 'r') as f:
with Image.open(f).convert('L') as image:
# change size of binarized image to 28x28 pixels
resized_image = image.resize((28, 28), Image.ANTIALIAS)
plt.imshow(resized_image)
plt.show()
# convert image to array
x = img_to_array(resized_image)
# add one dimension on image, [28, 28] -> [1, 28, 28]
x = np.expand_dims(x, axis=0)
# get predictions for all outputs(numbers)
predictions = test_model.predict_classes(x)
probabilities = test_model.predict_proba(x)
# write data on output
print("Number is: " + str(predictions))
# remove image from disc
os.remove('binarizedImage.jpg')
def upload_file():
if request.method == 'POST':
if 'file' not in request.files:
print('ファイルがアプロードされていません')
return redirect(request.url)
file = request.files['file']
if file.filename == '':
print('ファイルがアプロードされていません')
return redirect(request.url)
if file and allowed_file(file.filename):
filename = secure_filename(file.filename)
file.save(os.path.join(app.config['UPLOAD_FOLDER'], filename))
filepath = os.path.join(app.config['UPLOAD_FOLDER'], filename)
model = load_model('models/cloudcnn.h5')
model = model_from_json(open('models/cloudmodel.json').read())
image = Image.open(filepath)
image = image.convert('RGB')
image = image.resize((image_size, image_size))
data = np.asarray(image)
X = []
X.append(data)
X = np.array(X)
result = model.predict([X])[0]
predicted = result.argmax()
percentage = int(result[predicted] * 100)
return flash("その雲はおそらく" + classes[predicted] + ",で、その確率は" +
str(percentage) + "%ぐらいです！")
def preprocess_img(image):
img = image.resize((80, 80))
img = np.array(img, dtype="float32")
img /= 255
img = img.reshape(1, 80, 80, 3)
return img
def _extract_face(self, filename):
"""[summary]
Args:
filename ([type]): [description]
Returns:
[type]: [description]
"""
if self._model_name == 'facenet_keras.h5':
required_size = (160, 160)
else:
required_size = (200, 200)
# load image from file
image = Image.open(filename)
# convert to RGB, if needed
image = image.convert('RGB')
# convert to array
pixels = np.asarray(image)
# create the detector, using default weights
detector = MTCNN()
# detect faces in the image
results = detector.detect_faces(pixels)
# extract the bounding box from the first face
x1, y1, width, height = results[0]['box']
# deal with negative pixel index
x1, y1 = abs(x1), abs(y1)
x2, y2 = x1 + width, y1 + height
# extract the face
face = pixels[y1:y2, x1:x2]
# resize pixels to the model size
image = Image.fromarray(face)
image = image.resize(required_size)
face_array = np.asarray(image)
return face_array
def predict():
# initialize the data dictionary that will be returned from the view
response = {"success": False}
# ensure an image was properly uploaded to our endpoint
if flask.request.method == "POST":
if flask.request.files.get("image"):
# read the image in PIL format
image = flask.request.files["image"].read()
image = Image.open(io.BytesIO(image))
image = image.convert("RGB")
image = image.resize((image_w, image_h))
image = np.expand_dims(image, axis=0)
image = preprocess_input(image)
pred = model.predict(image)
index = np.argmax(pred)
pred_value = foods_sorted[index]
response["predictions"] = pred_value
response["success"] = True
# return the data dictionary as a JSON response
return flask.jsonify(response)
def generate_features(self,
input_feature: list = None,
model: str = None,
version: int = None,
type_input: str = "json"):
"""
Feature generation
:param input_feature: list with input features
:param model: name model
:param version: version model
"""
self.logger.info("Generate features for model")
if input_feature is None:
input_feature = []
if type_input == "json":
# TODO: for specific task
self.feature = {"inputs": input_feature}
if type_input == "file":
in_memory_file = BytesIO()
input_feature.save(in_memory_file)
image = Image.open(in_memory_file)
image = image.resize(self.target_size, Image.ANTIALIAS)
img_array = np.array(image)
img_array = np.expand_dims(img_array, axis=0)
img_array = preprocess_input(img_array)
self.feature = {"inputs": {"images": img_array.tolist()}}
def cover_status():
ret_val = 'unknown'
rtsp_url = app.rtsp_url
cap = cv2.VideoCapture(rtsp_url)
if not cap.isOpened():
app.logger.error("Failed to open stream!")
return ret_val
ret, frame = cap.read()
if ret == False:
app.logger.error("Failed to read stream!")
return ret_val
img_cropped = frame[400:1080, 100:1300]
image = Image.fromarray(cv2.cvtColor(img_cropped, cv2.COLOR_BGR2RGB))
image = image.resize((320, 170))
input_arr = keras.preprocessing.image.img_to_array(image)
input_arr /= 255.0
input_arr = np.array([input_arr]) # Convert single image to a batch.
classes = (app.model.predict(input_arr) > 0.5).astype("int32")
ret_val = "on" if classes[0][0] == 1 else "off"
return ret_val
def extract_face(filename, required_size=(224, 224)):
# load image from file
pixels = face_recognition.load_image_file(filename)
# extract the bounding box from the first face
face_bounding_boxes = face_recognition.face_locations(pixels)
# 可能返回 >0, 多个人脸
if len(face_bounding_boxes) == 0:
return [], []
face_list = []
for face_box in face_bounding_boxes:
top, right, bottom, left = face_box
x1, y1, width, height = left, top, right - left, bottom - top
x2, y2 = x1 + width, y1 + height
# extract the face
face = pixels[y1:y2, x1:x2]
# resize pixels to the model size
image = Image.fromarray(face)
image = image.resize(required_size)
face_array = np.asarray(image, 'float32')
face_list.append(face_array)
# show face
#from PIL import ImageDraw
#draw = ImageDraw.Draw(image)
#del draw
#image.show()
return face_list, face_bounding_boxes
def LoadImageFromBytes(bytes):
image = Image.open(io.BytesIO(bytes)).convert('L')
image = image.resize((28, 28))
image = numpy.asarray(image, dtype='float32')
image = image.reshape((1, image.shape[0], image.shape[1], 1))
image /= 255
return image
def load_data():
test_path = PATH+'/data/valid/'
train_path = PATH+'/data/train/'
x_train = []
x_test = []
classes = []
cnt = 0
for subdir, dir, files in os.walk(train_path):
classes.append(dir)
for file in files:
img_path = os.path.join(subdir, file)
image = Image.open(img_path)
resize = image.resize((150,150), Image.NEAREST)
resize.load()
data = np.asarray( resize, dtype="uint8" )
x_train.append(data)
# for subdir, dir, files in os.walk(test_path):
# for file in files:
# img_path = os.path.join(subdir, file)
# x = image.load_img(img_path)
# x_test.append(x)
x_train = np.array(x_train)
x_test = np.array(x_test)
return x_train, x_test
def preprocess_image(image,target_size):
if image.mode !="RGB":
image=image.convert("RGB")
image = image.resize(target_size)
image = img_to_array(image)
image = np.expand_dims(image,axis=0)
return image
def prepare_image(image, target):
image = image.resize(target)
image = img_to_array(image)
image = image / 255
image = np.expand_dims(image, axis=0)
return image
def preprocess_image(image, target_size):
if image.mode != 'RGB':
image = image.convert("RGB")
image = image.resize(target_size)
image = img_to_array(image)
image = image.reshape(1, target_size[0], target_size[1], 3)
print(image)
return image
def preprocess_image(image, target_size):
if image.mode != "RGB":
image = image.convert("RGB")
image = image.resize(target_size)
image = img_to_array(image) # to numpy array
image= np.expand_dims(image, axis=0) # expands the dimensions of img
return image # preprocessed image returned to be passed to keras model
def prepare_image(image):
if image.mode != "RGB":
image = image.convert("RGB")
image_size = (28, 28)
image = image.resize(image_size)
image = img_to_array(image)[:, :, 0]
image /= 255
image = 1 - image
return image.reshape((1, 28, 28, 1))
def prepare_image(image, target):
# resize the input image and preprocess it
image = image.resize(target)
image = img_to_array(image)
image = np.expand_dims(image, axis=0)
image = imagenet_utils.preprocess_input(image)
# return the processed image
return image
def preprocess_image(image,target_size=(26,26)):
if image.mode != "L":
image = image.convert("L") #Convert to grayscale
image = PIL.ImageOps.invert(image) # Flip Black/white pixels
image = image.resize(target_size) # Size appropriately
arr = img_to_array(image)
arr = np.expand_dims(arr, axis=0)
arr /= 255.
return arr
def adequacao_imagem(self, image, target_size):
if image.mode != "RGB":
image = image.convert("RGB")
image = image.resize(target_size)
image = img_to_array(image)
image = np.expand_dims(image, axis=0)
return image
def load_images_train(name, size):
x_images = []
y_images = []
for file in tqdm(os.listdir(name)):
image = Image.open(name + file)
if image.mode != "RGB": # comment these lines if using grasycale images
image.convert(
"RGB") # comment these lines if using grasycale images
#train for multiple scale factors
x_image1 = image.resize((size[0] // 2, size[1] // 2))
x_image1 = x_image1.resize(size, Image.BICUBIC)
x_image2 = image.resize((size[0] // 3, size[1] // 3))
x_image2 = x_image2.resize(size, Image.BICUBIC)
x_image3 = image.resize((size[0] // 4, size[1] // 4))
x_image3 = x_image3.resize(size, Image.BICUBIC)
x_image4 = image.resize((size[0] // 8, size[1] // 8))
x_image4 = x_image4.resize(size, Image.BICUBIC)
x_image1 = np.array(x_image1)
y_image1 = image.resize(size)
y_image1 = np.array(y_image1)
x_images.append(x_image1)
y_images.append(y_image1)
x_image2 = np.array(x_image2)
y_image2 = image.resize(size)
y_image2 = np.array(y_image2)
x_images.append(x_image2)
y_images.append(y_image2)
x_image3 = np.array(x_image3)
y_image3 = image.resize(size)
y_image3 = np.array(y_image3)
x_images.append(x_image3)
y_images.append(y_image3)
x_image4 = np.array(x_image4)
y_image4 = image.resize(size)
y_image4 = np.array(y_image4)
x_images.append(x_image4)
y_images.append(y_image4)
x_images = np.array(x_images)
y_images = np.array(y_images)
x_images = x_images / 255
y_images = y_images / 255
x_images = x_images.reshape(
x_images.shape[0], size[0] // 1, size[1] // 1,
3) #for grayscale, change the number of channels to 1
y_images = y_images.reshape(
y_images.shape[0], size[0] // 1, size[1] // 1,
3) #for grayscale, change the number of channels to 1
return x_images, y_images
def prepare_image(image):
if image.mode != "RGB":
image = image.convert("RGB")
image_size = (299, 299)
image = image.resize(image_size)
image = img_to_array(image)
image = np.expand_dims(image, axis=0)
image = preprocess_input(image)
# return the processed image
return image
def load_images_test(folder):
for i in range(18540,20549):
if 'DS_Store' not in "hello":
image = Image.open(os.path.join(folder,str(i)+".jpg"))
if image is not None:
h = image.size[0]
w = image.size[1]
image = np.array(image.resize((150,150)))
image= image / 255.
images_test.append(image)
def prepare_image(image):
if image.mode != "RGB":
image = image.convert("RGB")
image_size = (28, 28)
image = image.resize(image_size)
# image.save("fromform.png")
image = img_to_array(image)[:, :, 0]
image /= 255
image = 1 - image
return image.flatten().reshape(-1, 28 * 28)
def prepare_image(image, target):
if image.mode != 'RGB':
image = image.convert('RGB')
resized_img = image.resize(target)
img_as_array = image.img_to_array(resized_img)
expanded_img = np.expand_dims(img_as_array, axis=0)
processed_img = preprocess_input(expanded_img)
return processed_img
def prepare_image(image, size):
old_size = image.size
ratio = float(size) / max(old_size)
new_size = tuple([int(x * ratio) for x in old_size])
old_im = image.resize(new_size, Image.ANTIALIAS)
new_im = Image.new("RGB", (size, size))
new_im.paste(old_im,
((size - new_size[0]) // 2, (size - new_size[1]) // 2))
return new_im
def preprocess_image(image, target):
if image.mode != 'RGB':
image = image.convert("RGB")
# resize image input and preprocess it
image = image.resize(target)
image = img_to_array(image)
image = np.expand_dims(image, axis=0)
image = preprocess_input(image)
# return the processed image
return image
def upload_file():
global graph
with graph.as_default():
if request.method == 'POST':
myfile = request.form['snapShot'].split(',')
imgdata = base64.b64decode(myfile[1])
image = Image.open(io.BytesIO(imgdata))
# 保存
basename = datetime.now().strftime("%Y%m%d-%H%M%S")
image.save(os.path.join(UPLOAD_FOLDER, basename + ".png"))
filepath = os.path.join(UPLOAD_FOLDER, basename + ".png")
image = image.convert('RGB')
image = image.resize((image_size, image_size))
data = np.asarray(image)
X = []
X.append(data)
X = np.array(X).astype('float32')
X /= 256
result = model.predict([X])[0]
result_proba = model.predict_proba(X, verbose=1)[0]
percentage = (result_proba * 100).astype(float)
array_sort = sorted(list(zip(percentage, classes, classes_img)),
reverse=True)
percentage, array_class, array_img = zip(*array_sort)
pred_answer1 = "ラベル:" + \
str(array_class[0]) + ",確率："+str(percentage[0])+"%"
pred_answer2 = "ラベル:" + \
str(array_class[1]) + ",確率："+str(percentage[1])+"%"
pred_answer3 = "ラベル:" + \
str(array_class[2]) + ",確率："+str(percentage[2])+"%"
img_src1 = array_img[0]
img_src2 = array_img[1]
img_src3 = array_img[2]
basename = datetime.now().strftime("%Y%m%d-%H%M%S")
filepath3 = UPLOAD_FOLDER + basename + ".png"
return render_template("index.html",
answer1=pred_answer1,
img_data1=img_src1,
answer2=pred_answer2,
img_data2=img_src2,
answer3=pred_answer3,
img_data3=img_src3)
return render_template("index.html", answer="")
