Esempio n. 1
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def main():

    #If there's no argument, run basic demonstration
    if (len(sys.argv) == 1):

        style_transfer("./images/all/man_with_sunglasses.jpg",
                       "./images/all/stained_glass_1.jpg",
                       "./output/stained_glasses_man.jpg",
                       max_size=150 * 150)

    #If there's three arguments, run a basic iteration with only path names.
    elif (len(sys.argv) == 4):

        style_transfer(sys.argv[1],
                       sys.argv[2],
                       sys.argv[3],
                       max_size=150 * 150)

    else:
        print(
            "Please enter no arguments for a basic demonstration, or three arguments with the content, style and output images file path."
        )
        print(
            "For more parameters please open this script and run it from an interpreter."
        )
def webcam_input(model):
    st.header("Webcam Live Feed")
    run = st.checkbox("Run")
    FRAME_WINDOW = st.image([], channels='BGR')
    SIDE_WINDOW = st.sidebar.image([], width=100, channels='BGR')
    camera = cv2.VideoCapture(0)
    WIDTH = st.sidebar.select_slider('QUALITY (May reduce the speed)', list(range(150, 501, 50))) 

    while run:
        _, frame = camera.read()
        frame = cv2.cvtColor(frame, cv2.COLOR_RGB2BGR)
        # orig = frame.copy()
        orig = imutils.resize(frame, width=300)
        frame = imutils.resize(frame, width=WIDTH)
        target = style_transfer(frame, model)
        FRAME_WINDOW.image(target)
        SIDE_WINDOW.image(orig)
    else:        
        st.warning("NOTE: Streamlit currently doesn't support webcam. So to use this, clone this repo and run it on local server.")
        st.warning('Stopped')
def image_input(model):
    
    if st.sidebar.checkbox('Upload'):
        content_file = st.sidebar.file_uploader("Choose a Content Image", type=["png", "jpg", "jpeg"])
    else:
        content_name = st.sidebar.selectbox("Choose the content images:", content_images_name)
        content_file = content_images_dict[content_name]

    if content_file is not None:
        content = Image.open(content_file)
        content = np.array(content) #pil to cv
        content = cv2.cvtColor(content, cv2.COLOR_RGB2BGR) 
    else:
        st.warning("Upload an Image OR Untick the Upload Button)")
        st.stop()
     
    WIDTH = st.sidebar.select_slider('QUALITY (May reduce the speed)', list(range(150, 501, 50)), value=200) 
    content = imutils.resize(content, width=WIDTH)
    generated = style_transfer(content, model)
    st.sidebar.image(content, width=300, channels='BGR')
    st.image(generated, channels='BGR', clamp=True)
Esempio n. 4
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                                        content_images_name)
    content_file = content_images_dict[content_name]

if content_file is not None:
    content = Image.open(content_file)
    content = np.array(content)  # pil to cv
    content = cv2.cvtColor(content, cv2.COLOR_RGB2BGR)
else:
    st.warning("Upload an Image OR Untick the Upload Button)")
    st.stop()

# ## Exercise 3 : Render your final results ## #

# 3.1. Create a slider on your sidebar to select the quality
#      from 150 to 500, step of 50, default value of 200
#      Store this value in a WIDTH variable

WIDTH = st.sidebar.select_slider('QUALITY (May reduce the speed)',
                                 list(range(150, 501, 50)), value=200)

content = imutils.resize(content, width=WIDTH)
generated = style_transfer(content, model)

# 3.2. Show the source image (content) in the sidebar in BGR channel

st.sidebar.image(content, width=300, channels='BGR')

# 3.3 Show the final result (generated) right in your app

st.image(generated, channels='BGR', clamp=True)
Esempio n. 5
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#default values : content = 0.025, style = 5.0, var_w = 1.0
content_weight = 0.025
style_weight = 5.0
var_weight = 1.

### Layer used for the Loss calculations
ln_content = ["block5_conv2"]
ln_style = [
    "block1_conv1", "block2_conv1", "block3_conv1", "block4_conv1",
    "block5_conv1"
]

style_transfer(
    content_image_path,  #Path of the content image
    style_image_path,  #Path of the style image
    output_image_path,  #Path of the output image
    loadDims,  #True if using native image dim
    withBaseImage,  #True if using content image as starting image, false for random noises.
    rescale,  #Rescale the image, only useful if loadDims = true
    max_size,  #Automatically rescale if height*width > max_size. -1 to ignore.
    height,  #wanted height of the images, useless if loadDims.
    width,  #wanted width of the images, useless if loadDims.
    nb_iterations,  #number of iterations
    stepsBeforeSaveAndShow,  #
    content_weight,
    style_weight,
    var_weight,
    ln_content,  #Layers used for loss content calculation
    ln_style  #Layers used for style content calculation.
)