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+                    GNU AFFERO GENERAL PUBLIC LICENSE
+                       Version 3, 19 November 2007
+
+ Copyright (C) 2007 Free Software Foundation, Inc. <https://fsf.org/>
+ Everyone is permitted to copy and distribute verbatim copies
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+
+                            Preamble
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+USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED TO LOSS OF
+DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY YOU OR THIRD
+PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER PROGRAMS),
+EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF
+SUCH DAMAGES.
+
+  17. Interpretation of Sections 15 and 16.
+
+  If the disclaimer of warranty and limitation of liability provided
+above cannot be given local legal effect according to their terms,
+reviewing courts shall apply local law that most closely approximates
+an absolute waiver of all civil liability in connection with the
+Program, unless a warranty or assumption of liability accompanies a
+copy of the Program in return for a fee.
+
+                     END OF TERMS AND CONDITIONS
+
+            How to Apply These Terms to Your New Programs
+
+  If you develop a new program, and you want it to be of the greatest
+possible use to the public, the best way to achieve this is to make it
+free software which everyone can redistribute and change under these terms.
+
+  To do so, attach the following notices to the program.  It is safest
+to attach them to the start of each source file to most effectively
+state the exclusion of warranty; and each file should have at least
+the "copyright" line and a pointer to where the full notice is found.
+
+    <one line to give the program's name and a brief idea of what it does.>
+    Copyright (C) <year>  <name of author>
+
+    This program is free software: you can redistribute it and/or modify
+    it under the terms of the GNU Affero General Public License as published
+    by the Free Software Foundation, either version 3 of the License, or
+    (at your option) any later version.
+
+    This program is distributed in the hope that it will be useful,
+    but WITHOUT ANY WARRANTY; without even the implied warranty of
+    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+    GNU Affero General Public License for more details.
+
+    You should have received a copy of the GNU Affero General Public License
+    along with this program.  If not, see <https://www.gnu.org/licenses/>.
+
+Also add information on how to contact you by electronic and paper mail.
+
+  If your software can interact with users remotely through a computer
+network, you should also make sure that it provides a way for users to
+get its source.  For example, if your program is a web application, its
+interface could display a "Source" link that leads users to an archive
+of the code.  There are many ways you could offer source, and different
+solutions will be better for different programs; see section 13 for the
+specific requirements.
+
+  You should also get your employer (if you work as a programmer) or school,
+if any, to sign a "copyright disclaimer" for the program, if necessary.
+For more information on this, and how to apply and follow the GNU AGPL, see
+<https://www.gnu.org/licenses/>.

app.py

@@ -0,0 +1,37 @@
+import streamlit as st
+
+st.set_page_config(
+    page_title="MyoQuant-Streamlit",
+    page_icon="🔬",
+)
+
+st.write("# Welcome to MyoQuant-Streamlit 👋")
+
+st.sidebar.success("Select the corresponding staining analysis above.")
+
+st.markdown(
+    """
+## MyoQuant-Streamlit🔬 is a demo web interface to showcase the usage of [MyoQuant](https://github.com/lambda-science/MyoQuant). 
+
+![MyoQuant Banner](https://i.imgur.com/mzALgZL.png)
+
+[MyoQuant🔬 is a command-line tool to automatically quantify pathological features in muscle fiber histology images.](https://github.com/lambda-science/MyoQuant)  
+It is built using CellPose, Stardist, custom neural-network models and image analysis techniques to automatically analyze myopathy histology images. Currently MyoQuant is capable of quantifying centralization of nuclei in muscle fiber with HE staining and anomaly in the mitochondria distribution in muscle fibers with SDH staining.  
+This web application is intended for demonstration purposes only.  
+
+## How to Use
+
+Once on the demo, click on the corresponding staining analysis on the sidebar, and upload your histology image. Results will be displayed in the main area automatically.  
+For HE Staining analysis, you can download this sample image: [HERE](https://www.lbgi.fr/~meyer/SDH_models/sample_he.jpg)  
+For SDH Staining analysis, you can download this sample image: [HERE](https://www.lbgi.fr/~meyer/SDH_models/sample_sdh.jpg)
+
+## Contact
+Creator and Maintainer: [**Corentin Meyer**, 3rd year PhD Student in the CSTB Team, ICube — CNRS — Unistra](https://lambda-science.github.io/)  <corentin.meyer@etu.unistra.fr>  
+The source code for MyoQuant is available [HERE](https://github.com/lambda-science/MyoQuant), for the demo website (MyoQuant-Streamlit) it is available [HERE](https://github.com/lambda-science/MyoQuant-Streamlit)
+
+## Partners
+
+![Partners Banner](https://i.imgur.com/Xk9wBFQ.png)  
+MyoQuant is born within the collaboration between the [CSTB Team @ ICube](https://cstb.icube.unistra.fr/en/index.php/Home) led by Julie D. Thompson, the [Morphological Unit of the Institute of Myology of Paris](https://www.institut-myologie.org/en/recherche-2/neuromuscular-investigation-center/morphological-unit/) led by Teresinha Evangelista, the [imagery platform MyoImage of Center of Research in Myology](https://recherche-myologie.fr/technologies/myoimage/) led by Bruno Cadot, [the photonic microscopy platform of the IGMBC](https://www.igbmc.fr/en/plateformes-technologiques/photonic-microscopy) led by Bertrand Vernay and the [Pathophysiology of neuromuscular diseases team @ IGBMC](https://www.igbmc.fr/en/igbmc/a-propos-de-ligbmc/directory/jocelyn-laporte) led by Jocelyn Laporte.  
+"""
+)

draw_line.py

@@ -0,0 +1,30 @@
+def line_equation(x1, y1, x2, y2):
+    m = (y2 - y1) / (x2 - x1)
+    b = y1 - m * x1
+    return m, b
+
+
+def calculate_intersection(m, b, image_dim=(256, 256)):
+    x = [0, image_dim[1]]
+    y = [0, image_dim[0]]
+    results = []
+    for i in x:
+        intersect = m * i + b
+        if intersect >= 0 and intersect < image_dim[0]:
+            results.append((i, intersect))
+    for i in y:
+        intersect = (i - b) / m
+        if intersect >= 0 and intersect < image_dim[1]:
+            results.append((intersect, i))
+    return results
+
+
+def calculate_distance(x1, y1, x2, y2):
+    return ((x1 - x2) ** 2 + (y1 - y2) ** 2) ** 0.5
+
+
+def calculate_closest_point(x1, y1, intersections):
+    distances = []
+    for coords in intersections:
+        distances.append(calculate_distance(x1, y1, coords[0], coords[1]))
+    return intersections[distances.index(min(distances))]

gradcam.py

@@ -0,0 +1,75 @@
+import streamlit as st
+import tensorflow as tf
+import matplotlib.cm as cm
+import numpy as np
+
+# GRAD-CAM
+@st.experimental_memo
+def get_img_array(img_path, size):
+    # `img` is a PIL image of size 299x299
+    img = tf.keras.preprocessing.image.load_img(img_path, target_size=size)
+    # `array` is a float32 Numpy array of shape (299, 299, 3)
+    array = tf.keras.preprocessing.image.img_to_array(img)
+    # We add a dimension to transform our array into a "batch"
+    # of size (1, 299, 299, 3)
+    array = np.expand_dims(array, axis=0)
+    return array
+
+
+@st.experimental_memo
+def make_gradcam_heatmap(img_array, _model, last_conv_layer_name, pred_index=None):
+    # First, we create a model that maps the input image to the activations
+    # of the last conv layer as well as the output predictions
+    grad_model = tf.keras.models.Model(
+        [_model.inputs], [_model.get_layer(last_conv_layer_name).output, _model.output]
+    )
+
+    # Then, we compute the gradient of the top predicted class for our input image
+    # with respect to the activations of the last conv layer
+    with tf.GradientTape() as tape:
+        last_conv_layer_output, preds = grad_model(img_array)
+        if pred_index is None:
+            pred_index = tf.argmax(preds[0])
+        class_channel = preds[:, pred_index]
+
+    # This is the gradient of the output neuron (top predicted or chosen)
+    # with regard to the output feature map of the last conv layer
+    grads = tape.gradient(class_channel, last_conv_layer_output)
+
+    # This is a vector where each entry is the mean intensity of the gradient
+    # over a specific feature map channel
+    pooled_grads = tf.reduce_mean(grads, axis=(0, 1, 2))
+
+    # We multiply each channel in the feature map array
+    # by "how important this channel is" with regard to the top predicted class
+    # then sum all the channels to obtain the heatmap class activation
+    last_conv_layer_output = last_conv_layer_output[0]
+    heatmap = last_conv_layer_output @ pooled_grads[..., tf.newaxis]
+    heatmap = tf.squeeze(heatmap)
+
+    # For visualization purpose, we will also normalize the heatmap between 0 & 1
+    heatmap = tf.maximum(heatmap, 0) / tf.math.reduce_max(heatmap)
+    return heatmap.numpy()
+
+
+@st.experimental_memo
+def save_and_display_gradcam(img, heatmap, cam_path="cam.jpg", alpha=0.5):
+    # Rescale heatmap to a range 0-255
+    heatmap = np.uint8(255 * heatmap)
+
+    # Use jet colormap to colorize heatmap
+    jet = cm.get_cmap("jet")
+
+    # Use RGB values of the colormap
+    jet_colors = jet(np.arange(256))[:, :3]
+    jet_heatmap = jet_colors[heatmap]
+
+    # Create an image with RGB colorized heatmap
+    jet_heatmap = tf.keras.preprocessing.image.array_to_img(jet_heatmap)
+    jet_heatmap = jet_heatmap.resize((img.shape[1], img.shape[0]))
+    jet_heatmap = tf.keras.preprocessing.image.img_to_array(jet_heatmap)
+
+    # Superimpose the heatmap on original image
+    superimposed_img = jet_heatmap * alpha + img
+    superimposed_img = tf.keras.preprocessing.image.array_to_img(superimposed_img)
+    return superimposed_img

pages/1_HE_Staining_Analysis.py

@@ -0,0 +1,408 @@
+import streamlit as st
+from cellpose import models, core
+from stardist.models import StarDist2D
+from csbdeep.utils import normalize
+import matplotlib
+
+try:
+    from imageio.v2 import imread
+except:
+    from imageio import imread
+from skimage.measure import regionprops_table
+import pandas as pd
+import matplotlib.pyplot as plt
+from stardist import random_label_cmap
+from tensorflow.config import list_physical_devices
+from draw_line import *
+from skimage.draw import line
+import numpy as np
+
+st.set_page_config(
+    page_title="MyoQuant HE Analysis",
+    page_icon="🔬",
+)
+
+if len(list_physical_devices("GPU")) >= 1:
+    use_GPU = True
+else:
+    use_GPU = False
+
+
+@st.experimental_singleton
+def load_cellpose():
+    model_c = models.Cellpose(gpu=use_GPU, model_type="cyto2")
+    return model_c
+
+
+@st.experimental_singleton
+def load_stardist():
+    model_s = StarDist2D.from_pretrained("2D_versatile_he")
+    return model_s
+
+
+@st.experimental_memo
+def run_cellpose(image):
+    channel = [[0, 0]]
+    mask_cellpose, flow, style, diam = model_cellpose.eval(
+        image, diameter=None, channels=channel
+    )
+    return mask_cellpose
+
+
+@st.experimental_memo
+def run_stardist(image, nms_thresh=0.4, prob_thresh=0.5):
+    img_norm = image / 255
+    img_norm = normalize(img_norm, 1, 99.8)
+    mask_stardist, details = model_stardist.predict_instances(
+        img_norm, nms_thresh=nms_thresh, prob_thresh=prob_thresh
+    )
+    return mask_stardist
+
+
+@st.experimental_memo
+def extract_ROIs(histo_img, index, cellpose_df, mask_stardist):
+    single_cell_img = histo_img[
+        cellpose_df.iloc[index, 5] : cellpose_df.iloc[index, 7],
+        cellpose_df.iloc[index, 6] : cellpose_df.iloc[index, 8],
+    ].copy()
+    nucleus_single_cell_img = mask_stardist[
+        cellpose_df.iloc[index, 5] : cellpose_df.iloc[index, 7],
+        cellpose_df.iloc[index, 6] : cellpose_df.iloc[index, 8],
+    ].copy()
+    single_cell_mask = cellpose_df.iloc[index, 9]
+    single_cell_img[~single_cell_mask] = 0
+    nucleus_single_cell_img[~single_cell_mask] = 0
+
+    props_nuc_single = regionprops_table(
+        nucleus_single_cell_img,
+        intensity_image=single_cell_img,
+        properties=[
+            "label",
+            "area",
+            "centroid",
+            "eccentricity",
+            "bbox",
+            "image",
+            "perimeter",
+        ],
+    )
+    df_nuc_single = pd.DataFrame(props_nuc_single)
+    return single_cell_img, nucleus_single_cell_img, single_cell_mask, df_nuc_single
+
+
+@st.experimental_memo
+def single_cell_analysis(
+    single_cell_img,
+    single_cell_mask,
+    df_nuc_single,
+    x_fiber,
+    y_fiber,
+    internalised_threshold=0.75,
+):
+    n_nuc, n_nuc_intern, n_nuc_periph = 0, 0, 0
+    for _, value in df_nuc_single.iterrows():
+        n_nuc += 1
+        # Extend line and find closest point
+        m, b = line_equation(x_fiber, y_fiber, value[3], value[2])
+
+        intersections_lst = calculate_intersection(
+            m, b, (single_cell_img.shape[0], single_cell_img.shape[1])
+        )
+        border_point = calculate_closest_point(value[3], value[2], intersections_lst)
+        rr, cc = line(
+            int(y_fiber),
+            int(x_fiber),
+            int(border_point[1]),
+            int(border_point[0]),
+        )
+        for index3, coords in enumerate(list(zip(rr, cc))):
+            try:
+                if single_cell_mask[coords] == 0:
+                    dist_nuc_cent = calculate_distance(
+                        x_fiber, y_fiber, value[3], value[2]
+                    )
+                    dist_out_of_fiber = calculate_distance(
+                        x_fiber, y_fiber, coords[1], coords[0]
+                    )
+                    ratio_dist = dist_nuc_cent / dist_out_of_fiber
+                    if ratio_dist < internalised_threshold:
+                        n_nuc_intern += 1
+                    else:
+                        n_nuc_periph += 1
+                    break
+            except IndexError:
+                coords = list(zip(rr, cc))[index3 - 1]
+                dist_nuc_cent = calculate_distance(x_fiber, y_fiber, value[3], value[2])
+                dist_out_of_fiber = calculate_distance(
+                    x_fiber, y_fiber, coords[1], coords[0]
+                )
+                ratio_dist = dist_nuc_cent / dist_out_of_fiber
+                if ratio_dist < internalised_threshold:
+                    n_nuc_intern += 1
+                else:
+                    n_nuc_periph += 1
+                break
+
+    return n_nuc, n_nuc_intern, n_nuc_periph
+
+
+@st.experimental_memo
+def predict_all_cells(
+    histo_img, cellpose_df, mask_stardist, internalised_threshold=0.75
+):
+    list_n_nuc, list_n_nuc_intern, list_n_nuc_periph = [], [], []
+    for index in range(len(cellpose_df)):
+        (
+            single_cell_img,
+            _,
+            single_cell_mask,
+            df_nuc_single,
+        ) = extract_ROIs(histo_img, index, cellpose_df, mask_stardist)
+        x_fiber = cellpose_df.iloc[index, 3] - cellpose_df.iloc[index, 6]
+        y_fiber = cellpose_df.iloc[index, 2] - cellpose_df.iloc[index, 5]
+        n_nuc, n_nuc_intern, n_nuc_periph = single_cell_analysis(
+            single_cell_img, single_cell_mask, df_nuc_single, x_fiber, y_fiber
+        )
+        list_n_nuc.append(n_nuc)
+        list_n_nuc_intern.append(n_nuc_intern)
+        list_n_nuc_periph.append(n_nuc_periph)
+        df_nuc_analysis = pd.DataFrame(
+            list(zip(list_n_nuc, list_n_nuc_intern, list_n_nuc_periph)),
+            columns=["N° Nuc", "N° Nuc Intern", "N° Nuc Periph"],
+        )
+    return df_nuc_analysis
+
+
+@st.experimental_memo
+def predict_single_cell(histo_img, cellpose_df, mask_stardist):
+    pass
+
+
+@st.experimental_memo
+def paint_histo_img(histo_img, cellpose_df, prediction_df):
+    paint_img = np.zeros((histo_img.shape[0], histo_img.shape[1]), dtype=np.uint8)
+    for index in range(len(cellpose_df)):
+        single_cell_mask = cellpose_df.iloc[index, 9].copy()
+        if prediction_df.iloc[index, 1] == 0:
+            paint_img[
+                cellpose_df.iloc[index, 5] : cellpose_df.iloc[index, 7],
+                cellpose_df.iloc[index, 6] : cellpose_df.iloc[index, 8],
+            ][single_cell_mask] = 1
+        elif prediction_df.iloc[index, 1] > 0:
+            paint_img[
+                cellpose_df.iloc[index, 5] : cellpose_df.iloc[index, 7],
+                cellpose_df.iloc[index, 6] : cellpose_df.iloc[index, 8],
+            ][single_cell_mask] = 2
+    return paint_img
+
+
+with st.sidebar:
+    st.write("Models Parameters")
+    nms_thresh = st.slider("Stardist NMS Tresh", 0.0, 1.0, 0.4, 0.1)
+    prob_thresh = st.slider("Stardist Prob Tresh", 0.5, 1.0, 0.5, 0.05)
+
+model_cellpose = load_cellpose()
+model_stardist = load_stardist()
+
+st.title("HE Staining Analysis")
+st.write(
+    "This demo will automatically detect cells and nucleus in the image and try to quantify a certain number of features."
+)
+st.write("Upload your HE Staining image")
+uploaded_file = st.file_uploader("Choose a file")
+
+if uploaded_file is not None:
+    image_ndarray = imread(uploaded_file)
+    st.write("Raw Image")
+    image = st.image(uploaded_file)
+
+    mask_cellpose = run_cellpose(image_ndarray)
+    mask_stardist = run_stardist(image_ndarray, nms_thresh, prob_thresh)
+    mask_stardist_copy = mask_stardist.copy()
+
+    st.header("Segmentation Results")
+    st.subheader("CellPose and Stardist overlayed results")
+    fig, ax = plt.subplots(1, 1)
+    ax.imshow(mask_cellpose, cmap="viridis")
+    lbl_cmap = random_label_cmap()
+    ax.imshow(mask_stardist, cmap=lbl_cmap, alpha=0.5)
+    ax.axis("off")
+    st.pyplot(fig)
+
+    st.subheader("All cells detected by CellPose")
+    props_cellpose = regionprops_table(
+        mask_cellpose,
+        properties=[
+            "label",
+            "area",
+            "centroid",
+            "eccentricity",
+            "bbox",
+            "image",
+            "perimeter",
+        ],
+    )
+    df_cellpose = pd.DataFrame(props_cellpose)
+    st.dataframe(df_cellpose.drop("image", axis=1))
+
+    st.header("Full Nucleus Analysis Results")
+    df_nuc_analysis = predict_all_cells(image_ndarray, df_cellpose, mask_stardist)
+    st.dataframe(df_nuc_analysis)
+    st.write("Total number of nucleus : ", df_nuc_analysis["N° Nuc"].sum())
+    st.write(
+        "Total number of internalized nucleus : ",
+        df_nuc_analysis["N° Nuc Intern"].sum(),
+        " (",
+        round(
+            100
+            * df_nuc_analysis["N° Nuc Intern"].sum()
+            / df_nuc_analysis["N° Nuc"].sum(),
+            2,
+        ),
+        "%)",
+    )
+    st.write(
+        "Total number of peripherical nucleus : ",
+        df_nuc_analysis["N° Nuc Periph"].sum(),
+        " (",
+        round(
+            100
+            * df_nuc_analysis["N° Nuc Periph"].sum()
+            / df_nuc_analysis["N° Nuc"].sum(),
+            2,
+        ),
+        "%)",
+    )
+    st.write(
+        "Number of cell with at least one internalized nucleus : ",
+        df_nuc_analysis["N° Nuc Intern"].astype(bool).sum(axis=0),
+        " (",
+        round(
+            100
+            * df_nuc_analysis["N° Nuc Intern"].astype(bool).sum(axis=0)
+            / len(df_nuc_analysis),
+            2,
+        ),
+        "%)",
+    )
+    st.header("Single Nucleus Analysis Details")
+    selected_fiber = st.selectbox("Select a cell", list(range(len(df_cellpose))))
+    selected_fiber = int(selected_fiber)
+    single_cell_img = image_ndarray[
+        df_cellpose.iloc[selected_fiber, 5] : df_cellpose.iloc[selected_fiber, 7],
+        df_cellpose.iloc[selected_fiber, 6] : df_cellpose.iloc[selected_fiber, 8],
+    ]
+    nucleus_single_cell_img = mask_stardist_copy[
+        df_cellpose.iloc[selected_fiber, 5] : df_cellpose.iloc[selected_fiber, 7],
+        df_cellpose.iloc[selected_fiber, 6] : df_cellpose.iloc[selected_fiber, 8],
+    ]
+    single_cell_mask = df_cellpose.iloc[selected_fiber, 9]
+    single_cell_img[~single_cell_mask] = 0
+    nucleus_single_cell_img[~single_cell_mask] = 0
+
+    props_nuc_single = regionprops_table(
+        nucleus_single_cell_img,
+        intensity_image=single_cell_img,
+        properties=[
+            "label",
+            "area",
+            "centroid",
+            "eccentricity",
+            "bbox",
+            "image",
+            "perimeter",
+        ],
+    )
+    df_nuc_single = pd.DataFrame(props_nuc_single)
+    st.markdown(
+        """
+        * White point represent cell centroid. 
+        * Green point represent nucleus centroid. Green dashed line represent the fiber centrer - nucleus distance. 
+        * Red point represent the cell border from a straight line between the cell centroid and the nucleus centroid. The red dashed line represent distance between the nucelus and the cell border. 
+        * The periphery ratio is calculated by the division of the distance centroid - nucleus and the distance centroid - cell border."""
+    )
+    fig2, (ax1, ax2, ax3) = plt.subplots(1, 3)
+    ax1.imshow(single_cell_img)
+    ax2.imshow(nucleus_single_cell_img, cmap="viridis")
+    # Plot Fiber centroid
+    x_fiber = df_cellpose.iloc[selected_fiber, 3] - df_cellpose.iloc[selected_fiber, 6]
+    y_fiber = df_cellpose.iloc[selected_fiber, 2] - df_cellpose.iloc[selected_fiber, 5]
+    ax3.scatter(x_fiber, y_fiber, color="white")
+    # Plot nucleus centroid
+    for index, value in df_nuc_single.iterrows():
+        ax3.scatter(value[3], value[2], color="blue", s=2)
+        # Extend line and find closest point
+        m, b = line_equation(x_fiber, y_fiber, value[3], value[2])
+
+        intersections_lst = calculate_intersection(
+            m, b, (single_cell_img.shape[0], single_cell_img.shape[1])
+        )
+        border_point = calculate_closest_point(value[3], value[2], intersections_lst)
+        ax3.plot(
+            (x_fiber, border_point[0]),
+            (y_fiber, border_point[1]),
+            "ro--",
+            linewidth=1,
+            markersize=1,
+        )
+        ax3.plot(
+            (x_fiber, value[3]),
+            (y_fiber, value[2]),
+            "go--",
+            linewidth=1,
+            markersize=1,
+        )
+
+        rr, cc = line(
+            int(y_fiber),
+            int(x_fiber),
+            int(border_point[1]),
+            int(border_point[0]),
+        )
+        for index, coords in enumerate(list(zip(rr, cc))):
+            try:
+                if single_cell_mask[coords] == 0:
+                    dist_nuc_cent = calculate_distance(
+                        x_fiber, y_fiber, value[3], value[2]
+                    )
+                    dist_out_of_fiber = calculate_distance(
+                        x_fiber, y_fiber, coords[1], coords[0]
+                    )
+                    ratio_dist = dist_nuc_cent / dist_out_of_fiber
+                    ax3.scatter(coords[1], coords[0], color="red", s=10)
+                    break
+            except IndexError:
+                coords = list(zip(rr, cc))[index - 1]
+                dist_nuc_cent = calculate_distance(x_fiber, y_fiber, value[3], value[2])
+                dist_out_of_fiber = calculate_distance(
+                    x_fiber, y_fiber, coords[1], coords[0]
+                )
+                ratio_dist = dist_nuc_cent / dist_out_of_fiber
+                ax3.scatter(coords[1], coords[0], color="red", s=10)
+                break
+
+        st.write("Nucleus #{} has a periphery ratio of: {}".format(index, ratio_dist))
+    ax3.imshow(single_cell_img)
+    ax3.imshow(nucleus_single_cell_img, cmap="viridis", alpha=0.5)
+    ax1.axis("off")
+    ax2.axis("off")
+    ax3.axis("off")
+    st.pyplot(fig2)
+
+    st.subheader("All nucleus inside selected cell")
+
+    st.dataframe(df_nuc_single.drop("image", axis=1))
+
+    st.header("Painted predicted image")
+    st.write(
+        "Green color indicates cells with only peripherical nuclei, red color indicates cells with at least one internal nucleus."
+    )
+    painted_img = paint_histo_img(image_ndarray, df_cellpose, df_nuc_analysis)
+    fig3, ax3 = plt.subplots(1, 1)
+    cmap = matplotlib.colors.LinearSegmentedColormap.from_list(
+        "", ["white", "green", "red"]
+    )
+    ax3.imshow(image_ndarray)
+    ax3.imshow(painted_img, cmap=cmap, alpha=0.5)
+    ax3.axis("off")
+    st.pyplot(fig3)

pages/2_SDH_Staining_Analysis.py

@@ -0,0 +1,244 @@
+import streamlit as st
+from cellpose.core import use_gpu
+from cellpose.models import Cellpose
+
+try:
+    from imageio.v2 import imread
+except:
+    from imageio import imread
+from skimage.measure import regionprops_table
+import pandas as pd
+import matplotlib
+import matplotlib.pyplot as plt
+from stardist import random_label_cmap
+import tensorflow as tf
+from tensorflow.config import list_physical_devices
+from tensorflow import keras
+
+from gradcam import *
+from os import path
+import urllib.request
+from random_brightness import *
+
+labels_predict = ["control", "sick"]
+
+tf.random.set_seed(42)
+np.random.seed(42)
+
+st.set_page_config(
+    page_title="MyoQuant SDH Analysis",
+    page_icon="🔬",
+)
+
+if path.exists("model.h5"):
+    st.success("SDH Model ready to use !")
+    pass
+else:
+    with st.spinner("Please wait we are downloading the SDH Model."):
+        urllib.request.urlretrieve(
+            "https://lbgi.fr/~meyer/SDH_models/model.h5", "model.h5"
+        )
+    st.success("SDH Model have been downloaded !")
+
+if len(list_physical_devices("GPU")) >= 1:
+    use_GPU = True
+else:
+    use_GPU = False
+
+
+@st.experimental_singleton
+def load_cellpose():
+    model_c = Cellpose(gpu=use_GPU, model_type="cyto2")
+    return model_c
+
+
+@st.experimental_singleton
+def load_sdh_model():
+    model_sdh = keras.models.load_model(
+        "model.h5", custom_objects={"RandomBrightness": RandomBrightness}
+    )
+    return model_sdh
+
+
+@st.experimental_memo
+def run_cellpose(image):
+    channel = [[0, 0]]
+    mask_cellpose, flow, style, diam = model_cellpose.eval(
+        image, diameter=None, channels=channel
+    )
+    return mask_cellpose
+
+
+@st.experimental_memo
+def predict_single_cell(single_cell_img, _model_SDH):
+    img_array = np.empty((1, 256, 256, 3))
+    img_array[0] = tf.image.resize(single_cell_img, (256, 256))
+    prediction = _model_SDH.predict(img_array)
+    predicted_class = prediction.argmax()
+    predicted_proba = round(np.amax(prediction), 2)
+    heatmap = make_gradcam_heatmap(
+        img_array, _model_SDH.get_layer("resnet50v2"), "conv5_block3_3_conv"
+    )
+    grad_cam_img = save_and_display_gradcam(img_array[0], heatmap)
+    return grad_cam_img, predicted_class, predicted_proba
+
+
+@st.experimental_memo
+def resize_batch_cells(histo_img, cellpose_df):
+    img_array_full = np.empty((len(cellpose_df), 256, 256, 3))
+    for index in range(len(cellpose_df)):
+        single_cell_img = histo_img[
+            cellpose_df.iloc[index, 5] : cellpose_df.iloc[index, 7],
+            cellpose_df.iloc[index, 6] : cellpose_df.iloc[index, 8],
+        ].copy()
+
+        single_cell_mask = cellpose_df.iloc[index, 9].copy()
+        single_cell_img[~single_cell_mask] = 0
+
+        img_array_full[index] = tf.image.resize(single_cell_img, (256, 256))
+    return img_array_full
+
+
+@st.experimental_memo
+def predict_all_cells(histo_img, cellpose_df, _model_SDH):
+    predicted_class_array = np.empty((len(cellpose_df)))
+    predicted_proba_array = np.empty((len(cellpose_df)))
+    img_array_full = resize_batch_cells(histo_img, cellpose_df)
+    prediction = _model_SDH.predict(img_array_full)
+    index_counter = 0
+    for prediction_result in prediction:
+        predicted_class_array[index_counter] = prediction_result.argmax()
+        predicted_proba_array[index_counter] = np.amax(prediction_result)
+        index_counter += 1
+    return predicted_class_array, predicted_proba_array
+
+
+@st.experimental_memo
+def paint_full_image(image_sdh, df_cellpose, class_predicted_all):
+    image_sdh_paint = np.zeros((image_sdh.shape[0], image_sdh.shape[1]), dtype=np.uint8)
+    for index in range(len(df_cellpose)):
+        single_cell_mask = df_cellpose.iloc[index, 9].copy()
+        if class_predicted_all[index] == 0:
+            image_sdh_paint[
+                df_cellpose.iloc[index, 5] : df_cellpose.iloc[index, 7],
+                df_cellpose.iloc[index, 6] : df_cellpose.iloc[index, 8],
+            ][single_cell_mask] = 1
+        elif class_predicted_all[index] == 1:
+            image_sdh_paint[
+                df_cellpose.iloc[index, 5] : df_cellpose.iloc[index, 7],
+                df_cellpose.iloc[index, 6] : df_cellpose.iloc[index, 8],
+            ][single_cell_mask] = 2
+    return image_sdh_paint
+
+
+model_cellpose = load_cellpose()
+
+model_SDH = load_sdh_model()
+
+st.title("SDH Staining Analysis")
+st.write(
+    "This demo will automatically detect cells classify the SDH stained cell as sick or healthy using our deep-learning model."
+)
+st.write("Upload your SDH Staining image")
+uploaded_file_sdh = st.file_uploader("Choose a file")
+
+if uploaded_file_sdh is not None:
+    image_ndarray_sdh = imread(uploaded_file_sdh)
+
+    st.write("Raw Image")
+    image = st.image(uploaded_file_sdh)
+
+    mask_cellpose = run_cellpose(image_ndarray_sdh)
+
+    st.header("Segmentation Results")
+    st.subheader("CellPose results")
+    fig, ax = plt.subplots(1, 1)
+    ax.imshow(mask_cellpose, cmap="viridis")
+    ax.axis("off")
+    st.pyplot(fig)
+
+    st.subheader("All cells detected by CellPose")
+
+    props_cellpose = regionprops_table(
+        mask_cellpose,
+        properties=[
+            "label",
+            "area",
+            "centroid",
+            "eccentricity",
+            "bbox",
+            "image",
+            "perimeter",
+        ],
+    )
+    df_cellpose = pd.DataFrame(props_cellpose)
+    st.dataframe(df_cellpose.drop("image", axis=1))
+
+    st.header("SDH Cell Classification Results")
+
+    class_predicted_all, proba_predicted_all = predict_all_cells(
+        image_ndarray_sdh, df_cellpose, model_SDH
+    )
+
+    count_per_label = np.unique(class_predicted_all, return_counts=True)
+    class_and_proba_df = pd.DataFrame(
+        list(zip(class_predicted_all, proba_predicted_all)),
+        columns=["class", "proba"],
+    )
+    class_and_proba_df
+    st.write("Total number of cells detected: ", len(class_predicted_all))
+    for elem in count_per_label[0]:
+        st.write(
+            "Number of cells classified as ",
+            labels_predict[int(elem)],
+            ": ",
+            count_per_label[1][int(elem)],
+            " ",
+            100 * count_per_label[1][int(elem)] / len(class_predicted_all),
+            "%",
+        )
+
+    st.header("Single Cell Grad-CAM")
+    selected_fiber = st.selectbox("Select a cell", list(range(len(df_cellpose))))
+    selected_fiber = int(selected_fiber)
+    single_cell_img = image_ndarray_sdh[
+        df_cellpose.iloc[selected_fiber, 5] : df_cellpose.iloc[selected_fiber, 7],
+        df_cellpose.iloc[selected_fiber, 6] : df_cellpose.iloc[selected_fiber, 8],
+    ].copy()
+
+    single_cell_mask = df_cellpose.iloc[selected_fiber, 9].copy()
+    single_cell_img[~single_cell_mask] = 0
+
+    grad_img, class_predicted, proba_predicted = predict_single_cell(
+        single_cell_img, model_SDH
+    )
+
+    fig2, (ax1, ax2) = plt.subplots(1, 2)
+    resized_single_cell_img = tf.image.resize(single_cell_img, (256, 256))
+    ax1.imshow(single_cell_img)
+    ax2.imshow(grad_img)
+    ax1.axis("off")
+    # ax2.axis("off")
+
+    xlabel = (
+        labels_predict[int(class_predicted)]
+        + " ("
+        + str(round(proba_predicted, 2))
+        + ")"
+    )
+    ax2.set_xlabel(xlabel)
+    st.pyplot(fig2)
+
+    st.header("Painted predicted image")
+    st.write(
+        "Green color indicates cells classified as control, red color indicates cells classified as sick"
+    )
+    paint_img = paint_full_image(image_ndarray_sdh, df_cellpose, class_predicted_all)
+    fig3, ax3 = plt.subplots(1, 1)
+    cmap = matplotlib.colors.LinearSegmentedColormap.from_list(
+        "", ["white", "green", "red"]
+    )
+    ax3.imshow(image_ndarray_sdh)
+    ax3.imshow(paint_img, cmap=cmap, alpha=0.5)
+    ax3.axis("off")
+    st.pyplot(fig3)

random_brightness.py

@@ -0,0 +1,345 @@
+# @title Random Brightness Layer
+import tensorflow as tf
+from keras import backend
+from keras.engine import base_layer
+from keras.engine import base_preprocessing_layer
+from keras.layers.preprocessing import preprocessing_utils as utils
+from keras.utils import tf_utils
+
+from tensorflow.python.ops import stateless_random_ops
+from tensorflow.python.util.tf_export import keras_export
+from tensorflow.tools.docs import doc_controls
+
+
+@keras_export("keras.__internal__.layers.BaseImageAugmentationLayer")
+class BaseImageAugmentationLayer(base_layer.BaseRandomLayer):
+    """Abstract base layer for image augmentaion.
+    This layer contains base functionalities for preprocessing layers which
+    augment image related data, eg. image and in future, label and bounding boxes.
+    The subclasses could avoid making certain mistakes and reduce code
+    duplications.
+    This layer requires you to implement one method: `augment_image()`, which
+    augments one single image during the training. There are a few additional
+    methods that you can implement for added functionality on the layer:
+    `augment_label()`, which handles label augmentation if the layer supports
+    that.
+    `augment_bounding_box()`, which handles the bounding box augmentation, if the
+    layer supports that.
+    `get_random_transformation()`, which should produce a random transformation
+    setting. The tranformation object, which could be any type, will be passed to
+    `augment_image`, `augment_label` and `augment_bounding_box`, to coodinate
+    the randomness behavior, eg, in the RandomFlip layer, the image and
+    bounding_box should be changed in the same way.
+    The `call()` method support two formats of inputs:
+    1. Single image tensor with 3D (HWC) or 4D (NHWC) format.
+    2. A dict of tensors with stable keys. The supported keys are:
+      `"images"`, `"labels"` and `"bounding_boxes"` at the moment. We might add
+      more keys in future when we support more types of augmentation.
+    The output of the `call()` will be in two formats, which will be the same
+    structure as the inputs.
+    The `call()` will handle the logic detecting the training/inference
+    mode, unpack the inputs, forward to the correct function, and pack the output
+    back to the same structure as the inputs.
+    By default the `call()` method leverages the `tf.vectorized_map()` function.
+    Auto-vectorization can be disabled by setting `self.auto_vectorize = False`
+    in your `__init__()` method.  When disabled, `call()` instead relies
+    on `tf.map_fn()`. For example:
+    ```python
+    class SubclassLayer(BaseImageAugmentationLayer):
+      def __init__(self):
+        super().__init__()
+        self.auto_vectorize = False
+    ```
+    Example:
+    ```python
+    class RandomContrast(BaseImageAugmentationLayer):
+      def __init__(self, factor=(0.5, 1.5), **kwargs):
+        super().__init__(**kwargs)
+        self._factor = factor
+      def augment_image(self, image, transformation=None):
+        random_factor = tf.random.uniform([], self._factor[0], self._factor[1])
+        mean = tf.math.reduced_mean(inputs, axis=-1, keep_dim=True)
+        return (inputs - mean) * random_factor + mean
+    ```
+    Note that since the randomness is also a common functionnality, this layer
+    also includes a tf.keras.backend.RandomGenerator, which can be used to produce
+    the random numbers.  The random number generator is stored in the
+    `self._random_generator` attribute.
+    """
+
+    def __init__(self, rate=1.0, seed=None, **kwargs):
+        super().__init__(seed=seed, **kwargs)
+        self.rate = rate
+
+    @property
+    def auto_vectorize(self):
+        """Control whether automatic vectorization occurs.
+        By default the `call()` method leverages the `tf.vectorized_map()` function.
+        Auto-vectorization can be disabled by setting `self.auto_vectorize = False`
+        in your `__init__()` method.  When disabled, `call()` instead relies
+        on `tf.map_fn()`. For example:
+        ```python
+        class SubclassLayer(BaseImageAugmentationLayer):
+          def __init__(self):
+            super().__init__()
+            self.auto_vectorize = False
+        ```
+        """
+        return getattr(self, "_auto_vectorize", True)
+
+    @auto_vectorize.setter
+    def auto_vectorize(self, auto_vectorize):
+        self._auto_vectorize = auto_vectorize
+
+    @property
+    def _map_fn(self):
+        if self.auto_vectorize:
+            return tf.vectorized_map
+        else:
+            return tf.map_fn
+
+    @doc_controls.for_subclass_implementers
+    def augment_image(self, image, transformation=None):
+        """Augment a single image during training.
+        Args:
+          image: 3D image input tensor to the layer. Forwarded from `layer.call()`.
+          transformation: The transformation object produced by
+            `get_random_transformation`. Used to coordinate the randomness between
+            image, label and bounding box.
+        Returns:
+          output 3D tensor, which will be forward to `layer.call()`.
+        """
+        raise NotImplementedError()
+
+    @doc_controls.for_subclass_implementers
+    def augment_label(self, label, transformation=None):
+        """Augment a single label during training.
+        Args:
+          label: 1D label to the layer. Forwarded from `layer.call()`.
+          transformation: The transformation object produced by
+            `get_random_transformation`. Used to coordinate the randomness between
+            image, label and bounding box.
+        Returns:
+          output 1D tensor, which will be forward to `layer.call()`.
+        """
+        raise NotImplementedError()
+
+    @doc_controls.for_subclass_implementers
+    def augment_bounding_box(self, bounding_box, transformation=None):
+        """Augment bounding boxes for one image during training.
+        Args:
+          bounding_box: 2D bounding boxes to the layer. Forwarded from `call()`.
+          transformation: The transformation object produced by
+            `get_random_transformation`. Used to coordinate the randomness between
+            image, label and bounding box.
+        Returns:
+          output 2D tensor, which will be forward to `layer.call()`.
+        """
+        raise NotImplementedError()
+
+    @doc_controls.for_subclass_implementers
+    def get_random_transformation(self, image=None, label=None, bounding_box=None):
+        """Produce random transformation config for one single input.
+        This is used to produce same randomness between image/label/bounding_box.
+        Args:
+          image: 3D image tensor from inputs.
+          label: optional 1D label tensor from inputs.
+          bounding_box: optional 2D bounding boxes tensor from inputs.
+        Returns:
+          Any type of object, which will be forwarded to `augment_image`,
+          `augment_label` and `augment_bounding_box` as the `transformation`
+          parameter.
+        """
+        return None
+
+    def call(self, inputs, training=True):
+        inputs = self._ensure_inputs_are_compute_dtype(inputs)
+        if training:
+            inputs, is_dict = self._format_inputs(inputs)
+            images = inputs["images"]
+            if images.shape.rank == 3:
+                return self._format_output(self._augment(inputs), is_dict)
+            elif images.shape.rank == 4:
+                return self._format_output(self._batch_augment(inputs), is_dict)
+            else:
+                raise ValueError(
+                    "Image augmentation layers are expecting inputs to be "
+                    "rank 3 (HWC) or 4D (NHWC) tensors. Got shape: "
+                    f"{images.shape}"
+                )
+        else:
+            return inputs
+
+    def _augment(self, inputs):
+        image = inputs.get("images", None)
+        label = inputs.get("labels", None)
+        bounding_box = inputs.get("bounding_boxes", None)
+        transformation = self.get_random_transformation(
+            image=image, label=label, bounding_box=bounding_box
+        )  # pylint: disable=assignment-from-none
+        image = self.augment_image(image, transformation=transformation)
+        result = {"images": image}
+        if label is not None:
+            label = self.augment_label(label, transformation=transformation)
+            result["labels"] = label
+        if bounding_box is not None:
+            bounding_box = self.augment_bounding_box(
+                bounding_box, transformation=transformation
+            )
+            result["bounding_boxes"] = bounding_box
+        return result
+
+    def _batch_augment(self, inputs):
+        return self._map_fn(self._augment, inputs)
+
+    def _format_inputs(self, inputs):
+        if tf.is_tensor(inputs):
+            # single image input tensor
+            return {"images": inputs}, False
+        elif isinstance(inputs, dict):
+            # TODO(scottzhu): Check if it only contains the valid keys
+            return inputs, True
+        else:
+            raise ValueError(
+                f"Expect the inputs to be image tensor or dict. Got {inputs}"
+            )
+
+    def _format_output(self, output, is_dict):
+        if not is_dict:
+            return output["images"]
+        else:
+            return output
+
+    def _ensure_inputs_are_compute_dtype(self, inputs):
+        if isinstance(inputs, dict):
+            inputs["images"] = utils.ensure_tensor(inputs["images"], self.compute_dtype)
+        else:
+            inputs = utils.ensure_tensor(inputs, self.compute_dtype)
+        return inputs
+
+
+@keras_export("keras.layers.RandomBrightness", v1=[])
+class RandomBrightness(BaseImageAugmentationLayer):
+    """A preprocessing layer which randomly adjusts brightness during training.
+    This layer will randomly increase/reduce the brightness for the input RGB
+    images. At inference time, the output will be identical to the input.
+    Call the layer with `training=True` to adjust the brightness of the input.
+    Note that different brightness adjustment factors
+    will be apply to each the images in the batch.
+    For an overview and full list of preprocessing layers, see the preprocessing
+    [guide](https://www.tensorflow.org/guide/keras/preprocessing_layers).
+    Args:
+      factor: Float or a list/tuple of 2 floats between -1.0 and 1.0. The
+        factor is used to determine the lower bound and upper bound of the
+        brightness adjustment. A float value will be chosen randomly between
+        the limits. When -1.0 is chosen, the output image will be black, and
+        when 1.0 is chosen, the image will be fully white. When only one float
+        is provided, eg, 0.2, then -0.2 will be used for lower bound and 0.2
+        will be used for upper bound.
+      value_range: Optional list/tuple of 2 floats for the lower and upper limit
+        of the values of the input data. Defaults to [0.0, 255.0]. Can be changed
+        to e.g. [0.0, 1.0] if the image input has been scaled before this layer.
+        The brightness adjustment will be scaled to this range, and the
+        output values will be clipped to this range.
+      seed: optional integer, for fixed RNG behavior.
+    Inputs: 3D (HWC) or 4D (NHWC) tensor, with float or int dtype. Input pixel
+      values can be of any range (e.g. `[0., 1.)` or `[0, 255]`)
+    Output: 3D (HWC) or 4D (NHWC) tensor with brightness adjusted based on the
+      `factor`. By default, the layer will output floats. The output value will
+      be clipped to the range `[0, 255]`, the valid range of RGB colors, and
+      rescaled based on the `value_range` if needed.
+    Sample usage:
+    ```python
+    random_bright = tf.keras.layers.RandomBrightness(factor=0.2)
+    # An image with shape [2, 2, 3]
+    image = [[[1, 2, 3], [4 ,5 ,6]], [[7, 8, 9], [10, 11, 12]]]
+    # Assume we randomly select the factor to be 0.1, then it will apply
+    # 0.1 * 255 to all the channel
+    output = random_bright(image, training=True)
+    # output will be int64 with 25.5 added to each channel and round down.
+    tf.Tensor([[[26.5, 27.5, 28.5]
+                [29.5, 30.5, 31.5]]
+               [[32.5, 33.5, 34.5]
+                [35.5, 36.5, 37.5]]],
+              shape=(2, 2, 3), dtype=int64)
+    ```
+    """
+
+    _FACTOR_VALIDATION_ERROR = (
+        "The `factor` argument should be a number (or a list of two numbers) "
+        "in the range [-1.0, 1.0]. "
+    )
+    _VALUE_RANGE_VALIDATION_ERROR = (
+        "The `value_range` argument should be a list of two numbers. "
+    )
+
+    def __init__(self, factor, value_range=(0, 255), seed=None, **kwargs):
+        base_preprocessing_layer.keras_kpl_gauge.get_cell("RandomBrightness").set(True)
+        super().__init__(seed=seed, force_generator=True, **kwargs)
+        self._set_factor(factor)
+        self._set_value_range(value_range)
+        self._seed = seed
+
+    def augment_image(self, image, transformation=None):
+        return self._brightness_adjust(image, transformation["rgb_delta"])
+
+    def augment_label(self, label, transformation=None):
+        return label
+
+    def get_random_transformation(self, image=None, label=None, bounding_box=None):
+        rgb_delta_shape = (1, 1, 1)
+        random_rgb_delta = self._random_generator.random_uniform(
+            shape=rgb_delta_shape,
+            minval=self._factor[0],
+            maxval=self._factor[1],
+        )
+        random_rgb_delta = random_rgb_delta * (
+            self._value_range[1] - self._value_range[0]
+        )
+        return {"rgb_delta": random_rgb_delta}
+
+    def _set_value_range(self, value_range):
+        if not isinstance(value_range, (tuple, list)):
+            raise ValueError(self._VALUE_RANGE_VALIDATION_ERROR + f"Got {value_range}")
+        if len(value_range) != 2:
+            raise ValueError(self._VALUE_RANGE_VALIDATION_ERROR + f"Got {value_range}")
+        self._value_range = sorted(value_range)
+
+    def _set_factor(self, factor):
+        if isinstance(factor, (tuple, list)):
+            if len(factor) != 2:
+                raise ValueError(self._FACTOR_VALIDATION_ERROR + f"Got {factor}")
+            self._check_factor_range(factor[0])
+            self._check_factor_range(factor[1])
+            self._factor = sorted(factor)
+        elif isinstance(factor, (int, float)):
+            self._check_factor_range(factor)
+            factor = abs(factor)
+            self._factor = [-factor, factor]
+        else:
+            raise ValueError(self._FACTOR_VALIDATION_ERROR + f"Got {factor}")
+
+    def _check_factor_range(self, input_number):
+        if input_number > 1.0 or input_number < -1.0:
+            raise ValueError(self._FACTOR_VALIDATION_ERROR + f"Got {input_number}")
+
+    def _brightness_adjust(self, image, rgb_delta):
+        image = utils.ensure_tensor(image, self.compute_dtype)
+        rank = image.shape.rank
+        if rank != 3:
+            raise ValueError(
+                "Expected the input image to be rank 3. Got "
+                f"inputs.shape = {image.shape}"
+            )
+        rgb_delta = tf.cast(rgb_delta, image.dtype)
+        image += rgb_delta
+        return tf.clip_by_value(image, self._value_range[0], self._value_range[1])
+
+    def get_config(self):
+        config = {
+            "factor": self._factor,
+            "value_range": self._value_range,
+            "seed": self._seed,
+        }
+        base_config = super().get_config()
+        return dict(list(base_config.items()) + list(config.items()))

requirements.txt

@@ -0,0 +1,1074 @@
+absl-py==1.3.0; python_version >= "3.7" \
+    --hash=sha256:463c38a08d2e4cef6c498b76ba5bd4858e4c6ef51da1a5a1f27139a022e20248 \
+    --hash=sha256:34995df9bd7a09b3b8749e230408f5a2a2dd7a68a0d33c12a3d0cb15a041a507
+altair==4.2.0; python_version >= "3.7"
+appnope==0.1.3; platform_system == "Darwin" and python_version >= "3.8" and sys_platform == "darwin" \
+    --hash=sha256:265a455292d0bd8a72453494fa24df5a11eb18373a60c7c0430889f22548605e \
+    --hash=sha256:02bd91c4de869fbb1e1c50aafc4098827a7a54ab2f39d9dcba6c9547ed920e24
+asttokens==2.1.0; python_version >= "3.8" \
+    --hash=sha256:1b28ed85e254b724439afc783d4bee767f780b936c3fe8b3275332f42cf5f561 \
+    --hash=sha256:4aa76401a151c8cc572d906aad7aea2a841780834a19d780f4321c0fe1b54635
+astunparse==1.6.3; python_version >= "3.7" \
+    --hash=sha256:c2652417f2c8b5bb325c885ae329bdf3f86424075c4fd1a128674bc6fba4b8e8 \
+    --hash=sha256:5ad93a8456f0d084c3456d059fd9a92cce667963232cbf763eac3bc5b7940872
+attrs==22.1.0; python_version >= "3.7"
+backcall==0.2.0; python_version >= "3.8" \
+    --hash=sha256:fbbce6a29f263178a1f7915c1940bde0ec2b2a967566fe1c65c1dfb7422bd255 \
+    --hash=sha256:5cbdbf27be5e7cfadb448baf0aa95508f91f2bbc6c6437cd9cd06e2a4c215e1e
+blinker==1.5; python_version >= "3.7" and python_full_version < "3.0.0" or python_full_version >= "3.5.0" and python_version >= "3.7"
+cachetools==5.2.0; python_version >= "3.7" and python_version < "4.0" and (python_version >= "3.7" and python_full_version < "3.0.0" or python_full_version >= "3.6.0" and python_version >= "3.7")
+cellpose==2.1.0
+certifi==2022.9.24; python_version >= "3.7" and python_version < "4" \
+    --hash=sha256:90c1a32f1d68f940488354e36370f6cca89f0f106db09518524c88d6ed83f382 \
+    --hash=sha256:0d9c601124e5a6ba9712dbc60d9c53c21e34f5f641fe83002317394311bdce14
+cffi==1.15.1; implementation_name == "pypy" and python_version >= "3.7"
+charset-normalizer==2.1.1; python_version >= "3.7" and python_version < "4" and python_full_version >= "3.6.0"
+click==8.1.3; python_version >= "3.7" \
+    --hash=sha256:bb4d8133cb15a609f44e8213d9b391b0809795062913b383c62be0ee95b1db48 \
+    --hash=sha256:7682dc8afb30297001674575ea00d1814d808d6a36af415a82bd481d37ba7b8e
+colorama==0.4.6; python_version >= "3.8" and python_full_version < "3.0.0" and platform_system == "Windows" and sys_platform == "win32" or python_full_version >= "3.7.0" and platform_system == "Windows" and python_version >= "3.8" and sys_platform == "win32" \
+    --hash=sha256:4f1d9991f5acc0ca119f9d443620b77f9d6b33703e51011c16baf57afb285fc6 \
+    --hash=sha256:08695f5cb7ed6e0531a20572697297273c47b8cae5a63ffc6d6ed5c201be6e44
+commonmark==0.9.1; python_full_version >= "3.6.3" and python_full_version < "4.0.0" and python_version >= "3.7"
+contourpy==1.0.5; python_version >= "3.8" \
+    --hash=sha256:87121b9428ac568fb84fae4af5e7852fc34f02eadc4e3e91f6c8989327692186 \
+    --hash=sha256:1fb782982c42cee667b892a0b0c52a9f6c7ecf1da5c5f4345845f04eaa862f93 \
+    --hash=sha256:689d7d2a840619915d0abd1ecc6e399fee202f8ad315acda2807f4ca420d0802 \
+    --hash=sha256:d88814befbd1433152c5f6dd536905149ba028d795a22555b149ae0a36024d9e \
+    --hash=sha256:df65f4b2b4e74977f0336bef12a88051ab24e6a16873cd9249f34d67cb3e345d \
+    --hash=sha256:bf6b4c0c723664f65c2a47c8cb6ebbf660b0b2e2d936adf2e8503d4e93359465 \
+    --hash=sha256:bcc98d397c3dea45d5b262029564b29cb8e945f2607a38bee6163694c0a8b4ef \
+    --hash=sha256:2bf5c846c257578b03d498b20f54f53551616a507d8e5463511c58bb58e9a9cf \
+    --hash=sha256:cdacddb18d55ffec42d1907079cdc04ec4fa8a990cdf5b9d9fe67d281fc0d12e \
+    --hash=sha256:434942fa2f9019b9ae525fb752dc523800c49a1a28fbd6d9240b0fa959573dcc \
+    --hash=sha256:3b3082ade8849130203d461b98c2a061b382c46074b43b4edd5cefd81af92b8a \
+    --hash=sha256:057114f698ffb9e54657e8fda6802e2f5c8fad609845cf6afaf31590ef6a33c0 \
+    --hash=sha256:218722a29c5c26677d37c44f5f8a372daf6f07870aad793a97d47eb6ad6b3290 \
+    --hash=sha256:6c02e22cf09996194bcb3a4784099975cf527d5c29caf759abadf29ebdb2fe27 \
+    --hash=sha256:c0d5ee865b5fd16bf62d72122aadcc90aab296c30c1adb0a32b4b66bd843163e \
+    --hash=sha256:d45822b0a2a452327ab4f95efe368d234d5294bbf89a99968be27c7938a21108 \
+    --hash=sha256:dca5be83a6dfaf933a46e3bc2b9f2685e5ec61b22f6a38ad740aac9c16e9a0ff \
+    --hash=sha256:3c3f2f6b898a40207843ae01970e57e33d22a26b22f23c6a5e07b4716751085f \
+    --hash=sha256:c2b4eab7c12f9cb460509bc34a3b086f9802f0dba27c89a63df4123819ad64af \
+    --hash=sha256:09ed9b63f4df8a7591b7a4a26c1ad066dcaafda1f846250fdcb534074a411692 \
+    --hash=sha256:f670686d99c867d0f24b28ce8c6f02429c6eef5e2674aab287850d0ee2d20437 \
+    --hash=sha256:c51568e94f7f232296de30002f2a50f77a7bd346673da3e4f2aaf9d2b833f2e5 \
+    --hash=sha256:7c9e99aac7b430f6a9f15eebf058c742097cea3369f23a2bfc5e64d374b67e3a \
+    --hash=sha256:3210d93ad2af742b6a96cf39792f7181822edbb8fe11c3ef29d1583fe637a8d8 \
+    --hash=sha256:128bd7acf569f8443ad5b2227f30ac909e4f5399ed221727eeacf0c6476187e6 \
+    --hash=sha256:813c2944e940ef8dccea71305bacc942d4b193a021140874b3e58933ec44f5b6 \
+    --hash=sha256:a74afd8d560eaafe0d9e3e1db8c06081282a05ca4de00ee416195085a79d7d3d \
+    --hash=sha256:2d0ad9a85f208473b1f3613c45756c7aa6fcc288266a8c7b873f896aaf741b6b \
+    --hash=sha256:60f37acd4e4227c5a29f737d9a85ca3145c529a8dd4bf70af7f0637c61b49222 \
+    --hash=sha256:b50e481a4317a8efcfffcfddcd4c9b36eacba440440e70cbe0256aeb6fd6abae \
+    --hash=sha256:0395ae71164bfeb2dedd136e03c71a2718a5aa9873a46f518f4133be0d63e1d2 \
+    --hash=sha256:3ca40d7844b391d90b864c6a6d1bb6b88b09035fb4d866d64d43c4d26fb0ab64 \
+    --hash=sha256:3109fa601d2a448cec4643abd3a31f972bf05b7c2f2e83df9d3429878f8c10ae \
+    --hash=sha256:06c4d1dde5ee4f909a8a95ba1eb04040c6c26946b4f3b5beaf10d45f14e940ee \
+    --hash=sha256:2f54dcc9bb9390fd0636301ead134d46d5229fe86da0db4d974c0fda349f560e \
+    --hash=sha256:46b8e24813e2fb5a3e598c1f8b9ae403e1438cb846a80cc2b33cddf19dddd7f2 \
+    --hash=sha256:061e1f066c419ffe25b615a1df031b4832ea1d7f2676937e69e8e00e24512005 \
+    --hash=sha256:19ea64fa0cf389d2ebc10974616acfa1fdecbd73d1fd9c72215b782f3c40f561 \
+    --hash=sha256:dfe924e5a63861c82332a12adeeab955dc8c8009ddbbd80cc2fcca049ff89a49 \
+    --hash=sha256:bed3a2a823a041e8d249b1a7ec132933e1505299329b5cfe1b2b5ec689ec7675 \
+    --hash=sha256:0389349875424aa8c5e61f757e894687916bc4e9616cc6afcbd8051aa2428952 \
+    --hash=sha256:2b5e334330d82866923015b455260173cb3b9e3b4e297052d758abd262031289 \
+    --hash=sha256:def9a01b73c9e27d70ea03b381fb3e7aadfac1f398dbd63751313c3a46747ef5 \
+    --hash=sha256:59c827e536bb5e3ef58e06da0faba61fd89a14f30b68bcfeca41f43ca83a1942 \
+    --hash=sha256:f05d311c937da03b0cd26ac3e14cb991f6ff8fc94f98b3df9713537817539795 \
+    --hash=sha256:970a4be7ec84ccda7c27cb4ae74930bbbd477bc8d849ed55ea798084dd5fca8c \
+    --hash=sha256:0f7672148f8fca48e4efc16aba24a7455b40c22d4f8abe42475dec6a12b0bb9a \
+    --hash=sha256:eba62b7c21a33e72dd8adab2b92dd5610d8527f0b2ac28a8e0770e71b21a13f9 \
+    --hash=sha256:dd084459ecdb224e617e4ab3f1d5ebe4d1c48facb41f24952b76aa6ba9712bb0 \
+    --hash=sha256:c5158616ab39d34b76c50f40c81552ee180598f7825dc7a66fd187d29958820f \
+    --hash=sha256:f856652f9b533c6cd2b9ad6836a7fc0e43917d7ff15be46c5baf1350f8cdc5d9 \
+    --hash=sha256:f1cc623fd6855b25da52b3275e0c9e51711b86a9dccc75f8c9ab4432fd8e42c7 \
+    --hash=sha256:e67dcaa34dcd908fcccbf49194211d847c731b6ebaac661c1c889f1bf6af1e44 \
+    --hash=sha256:bfd634cb9685161b2a51f73a7fc4736fd0d67a56632d52319317afaa27f08243 \
+    --hash=sha256:79908b9d02b1d6c1c71ff3b7ad127f3f82e14a8e091ab44b3c7e34b649fea733 \
+    --hash=sha256:b4963cf08f4320d98ae72ec7694291b8ab85cb7da3b0cd824bc32701bc992edf \
+    --hash=sha256:3cfc067ddde78b76dcbc9684d82688b7d3c5158fa2254a085f9bcb9586c1e2d8 \
+    --hash=sha256:9939796abcadb2810a63dfb26ff8ca4595fe7dd70a3ceae7f607a2639b714307 \
+    --hash=sha256:d8150579bf30cdf896906baf256aa200cd50dbe6e565c17d6fd3d678e21ff5de \
+    --hash=sha256:ed9c91bf4ce614efed5388c3f989a7cfe08728ab871d995a486ea74ff88993db \
+    --hash=sha256:b46a04588ceb7cf132568e0e564a854627ef87a1ed3bf536234540a79ced44b0 \
+    --hash=sha256:b85553699862c09937a7a5ea14ee6229087971a7d51ae97d5f4b407f571a2c17 \
+    --hash=sha256:99a8071e351b50827ad976b92ed91845fb614ac67a3c41109b24f3d8bd3afada \
+    --hash=sha256:fb0458d74726937ead9e2effc91144aea5a58ecee9754242f8539a782bed685a \
+    --hash=sha256:0f89f0608a5aa8142ed0e53957916623791a88c7f5e5f07ae530c328beeb888f \
+    --hash=sha256:ce763369e646e59e4ca2c09735cd1bdd3048d909ad5f2bc116e83166a9352f3c \
+    --hash=sha256:9c16fa267740d67883899e054cccb4279e002f3f4872873b752c1ba15045ff49 \
+    --hash=sha256:a30e95274f5c0e007ccc759ec258aa5708c534ec058f153ee25ac700a2f1438b \
+    --hash=sha256:896631cd40222aef3697e4e51177d14c3709fda49d30983269d584f034acc8a4
+csbdeep==0.7.2; python_version >= "3.6"
+cycler==0.11.0; python_version >= "3.8" \
+    --hash=sha256:3a27e95f763a428a739d2add979fa7494c912a32c17c4c38c4d5f082cad165a3 \
+    --hash=sha256:9c87405839a19696e837b3b818fed3f5f69f16f1eec1a1ad77e043dcea9c772f
+debugpy==1.6.3; python_version >= "3.7"
+decorator==5.1.1; python_version >= "3.8" \
+    --hash=sha256:b8c3f85900b9dc423225913c5aace94729fe1fa9763b38939a95226f02d37186 \
+    --hash=sha256:637996211036b6385ef91435e4fae22989472f9d571faba8927ba8253acbc330
+entrypoints==0.4; python_version >= "3.7" \
+    --hash=sha256:f174b5ff827504fd3cd97cc3f8649f3693f51538c7e4bdf3ef002c8429d42f9f \
+    --hash=sha256:b706eddaa9218a19ebcd67b56818f05bb27589b1ca9e8d797b74affad4ccacd4
+executing==1.2.0; python_version >= "3.8" \
+    --hash=sha256:0314a69e37426e3608aada02473b4161d4caf5a4b244d1d0c48072b8fee7bacc \
+    --hash=sha256:19da64c18d2d851112f09c287f8d3dbbdf725ab0e569077efb6cdcbd3497c107
+fastremap==1.13.3; python_version >= "3.6" and python_version < "4.0"
+flatbuffers==22.10.26; python_version >= "3.7" \
+    --hash=sha256:e36d5ba7a5e9483ff0ec1d238fdc3011c866aab7f8ce77d5e9d445ac12071d84 \
+    --hash=sha256:8698aaa635ca8cf805c7d8414d4a4a8ecbffadca0325fa60551cb3ca78612356
+fonttools==4.38.0; python_version >= "3.8" \
+    --hash=sha256:820466f43c8be8c3009aef8b87e785014133508f0de64ec469e4efb643ae54fb \
+    --hash=sha256:2bb244009f9bf3fa100fc3ead6aeb99febe5985fa20afbfbaa2f8946c2fbdaf1
+gast==0.4.0; python_version >= "3.7" and python_full_version < "3.0.0" or python_full_version >= "3.4.0" and python_version >= "3.7" \
+    --hash=sha256:b7adcdd5adbebf1adf17378da5ba3f543684dbec47b1cda1f3997e573cd542c4 \
+    --hash=sha256:40feb7b8b8434785585ab224d1568b857edb18297e5a3047f1ba012bc83b42c1
+gitdb==4.0.9; python_version >= "3.7"
+gitpython==3.1.29; python_version >= "3.7" \
+    --hash=sha256:41eea0deec2deea139b459ac03656f0dd28fc4a3387240ec1d3c259a2c47850f \
+    --hash=sha256:cc36bfc4a3f913e66805a28e84703e419d9c264c1077e537b54f0e1af85dbefd
+google-auth-oauthlib==0.4.6; python_version >= "3.7" \
+    --hash=sha256:a90a072f6993f2c327067bf65270046384cda5a8ecb20b94ea9a687f1f233a7a \
+    --hash=sha256:3f2a6e802eebbb6fb736a370fbf3b055edcb6b52878bf2f26330b5e041316c73
+google-auth==2.13.0; python_version >= "3.7" and python_full_version < "3.0.0" or python_full_version >= "3.6.0" and python_version >= "3.7" \
+    --hash=sha256:9352dd6394093169157e6971526bab9a2799244d68a94a4a609f0dd751ef6f5e \
+    --hash=sha256:99510e664155f1a3c0396a076b5deb6367c52ea04d280152c85ac7f51f50eb42
+google-pasta==0.2.0; python_version >= "3.7" \
+    --hash=sha256:c9f2c8dfc8f96d0d5808299920721be30c9eec37f2389f28904f454565c8a16e \
+    --hash=sha256:4612951da876b1a10fe3960d7226f0c7682cf901e16ac06e473b267a5afa8954 \
+    --hash=sha256:b32482794a366b5366a32c92a9a9201b107821889935a02b3e51f6b432ea84ed
+grpcio==1.50.0; python_version >= "3.7" \
+    --hash=sha256:906f4d1beb83b3496be91684c47a5d870ee628715227d5d7c54b04a8de802974 \
+    --hash=sha256:2d9fd6e38b16c4d286a01e1776fdf6c7a4123d99ae8d6b3f0b4a03a34bf6ce45 \
+    --hash=sha256:4b123fbb7a777a2fedec684ca0b723d85e1d2379b6032a9a9b7851829ed3ca9a \
+    --hash=sha256:b2f77a90ba7b85bfb31329f8eab9d9540da2cf8a302128fb1241d7ea239a5469 \
+    --hash=sha256:9eea18a878cffc804506d39c6682d71f6b42ec1c151d21865a95fae743fda500 \
+    --hash=sha256:2b71916fa8f9eb2abd93151fafe12e18cebb302686b924bd4ec39266211da525 \
+    --hash=sha256:95ce51f7a09491fb3da8cf3935005bff19983b77c4e9437ef77235d787b06842 \
+    --hash=sha256:f7025930039a011ed7d7e7ef95a1cb5f516e23c5a6ecc7947259b67bea8e06ca \
+    --hash=sha256:05f7c248e440f538aaad13eee78ef35f0541e73498dd6f832fe284542ac4b298 \
+    --hash=sha256:ca8a2254ab88482936ce941485c1c20cdeaef0efa71a61dbad171ab6758ec998 \
+    --hash=sha256:3b611b3de3dfd2c47549ca01abfa9bbb95937eb0ea546ea1d762a335739887be \
+    --hash=sha256:1a4cd8cb09d1bc70b3ea37802be484c5ae5a576108bad14728f2516279165dd7 \
+    --hash=sha256:156f8009e36780fab48c979c5605eda646065d4695deea4cfcbcfdd06627ddb6 \
+    --hash=sha256:de411d2b030134b642c092e986d21aefb9d26a28bf5a18c47dd08ded411a3bc5 \
+    --hash=sha256:d144ad10eeca4c1d1ce930faa105899f86f5d99cecfe0d7224f3c4c76265c15e \
+    --hash=sha256:92d7635d1059d40d2ec29c8bf5ec58900120b3ce5150ef7414119430a4b2dd5c \
+    --hash=sha256:ce8513aee0af9c159319692bfbf488b718d1793d764798c3d5cff827a09e25ef \
+    --hash=sha256:8e8999a097ad89b30d584c034929f7c0be280cd7851ac23e9067111167dcbf55 \
+    --hash=sha256:a50a1be449b9e238b9bd43d3857d40edf65df9416dea988929891d92a9f8a778 \
+    --hash=sha256:cf151f97f5f381163912e8952eb5b3afe89dec9ed723d1561d59cabf1e219a35 \
+    --hash=sha256:a23d47f2fc7111869f0ff547f771733661ff2818562b04b9ed674fa208e261f4 \
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+    --hash=sha256:e07fe0d7ae395897981d16be61f0db9791f482f03fee7d1851fe20ddb4f69c03 \
+    --hash=sha256:d75061367a69808ab2e84c960e9dce54749bcc1e44ad3f85deee3a6c75b4ede9 \
+    --hash=sha256:ae23daa7eda93c1c49a9ecc316e027ceb99adbad750fbd3a56fa9e4a2ffd5ae0 \
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+    --hash=sha256:12b479839a5e753580b5e6053571de14006157f2ef9b71f38c56dc9b23b95ad6
+h5py==3.7.0; python_version >= "3.9"
+idna==3.4; python_version >= "3.7" and python_version < "4" \
+    --hash=sha256:90b77e79eaa3eba6de819a0c442c0b4ceefc341a7a2ab77d7562bf49f425c5c2 \
+    --hash=sha256:814f528e8dead7d329833b91c5faa87d60bf71824cd12a7530b5526063d02cb4
+imagecodecs==2022.9.26; python_version >= "3.8" \
+    --hash=sha256:c8dd5ffb3b37486fd621ebd1c27ce2f0f5936711d736e8b00cb3d8b3a6f484ae \
+    --hash=sha256:1d0f397e86fe731f845a7845769d91e49062562b88f09c9cf982e4534c4237c3 \
+    --hash=sha256:b3222cb01e9764fcccf90e21920ee76ccf36a4dd1e69d7a7824f9956d2173324 \
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+    --hash=sha256:04d5757d8fd7819844b0f8d9eed05025dca4962f280d0010b42c7c9c993fe371
+imageio==2.22.2; python_version >= "3.7" \
+    --hash=sha256:9bdafe9c5a3d336a187f3f554f3e30bcdbf8a1d7d46f0e4d94e4a535adfb64c7 \
+    --hash=sha256:db7010cd10712518819a4187baf61b05988361ea20c23e829918727b27acb977
+importlib-metadata==5.0.0; python_version < "3.10" and python_version >= "3.7" \
+    --hash=sha256:ddb0e35065e8938f867ed4928d0ae5bf2a53b7773871bfe6bcc7e4fcdc7dea43 \
+    --hash=sha256:da31db32b304314d044d3c12c79bd59e307889b287ad12ff387b3500835fc2ab
+ipykernel==6.16.2; python_version >= "3.7" \
+    --hash=sha256:67daf93e5b52456cd8eea87a8b59405d2bb80ae411864a1ea206c3631d8179af \
+    --hash=sha256:463f3d87a92e99969b1605cb7a5b4d7b36b7145a0e72d06e65918a6ddefbe630
+ipython==8.5.0; python_version >= "3.8" \
+    --hash=sha256:6f090e29ab8ef8643e521763a4f1f39dc3914db643122b1e9d3328ff2e43ada2 \
+    --hash=sha256:097bdf5cd87576fd066179c9f7f208004f7a6864ee1b20f37d346c0bcb099f84
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+    --hash=sha256:637c9635fcf47945ceb91cd7f320234a7be540ded6f3e99a50cb6febdfd1ba8d \
+    --hash=sha256:74137626a64a99c8eb6ae5832d99b3bdd7d29a3850fe2aa80a4126b2a7d949ab
+jinja2==3.1.2; python_version >= "3.7" \
+    --hash=sha256:6088930bfe239f0e6710546ab9c19c9ef35e29792895fed6e6e31a023a182a61 \
+    --hash=sha256:31351a702a408a9e7595a8fc6150fc3f43bb6bf7e319770cbc0db9df9437e852
+jsonschema==4.16.0; python_version >= "3.7" \
+    --hash=sha256:9e74b8f9738d6a946d70705dc692b74b5429cd0960d58e79ffecfc43b2221eb9 \
+    --hash=sha256:165059f076eff6971bae5b742fc029a7b4ef3f9bcf04c14e4776a7605de14b23
+jupyter-client==7.4.4; python_version >= "3.7" \
+    --hash=sha256:1c1d418ef32a45a1fae0b243e6f01cc9bf65fa8ddbd491a034b9ba6ac6502951 \
+    --hash=sha256:5616db609ac720422e6a4b893d6572b8d655ff41e058367f4459a0d2c0726832
+jupyter-core==4.11.2; python_version >= "3.7" \
+    --hash=sha256:3815e80ec5272c0c19aad087a0d2775df2852cfca8f5a17069e99c9350cecff8 \
+    --hash=sha256:c2909b9bc7dca75560a6c5ae78c34fd305ede31cd864da3c0d0bb2ed89aa9337
+keras-preprocessing==1.1.2; python_version >= "3.7" \
+    --hash=sha256:7b82029b130ff61cc99b55f3bd27427df4838576838c5b2f65940e4fcec99a7b \
+    --hash=sha256:add82567c50c8bc648c14195bf544a5ce7c1f76761536956c3d2978970179ef3
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+    --hash=sha256:26a6e2c2522e7468ddea22710a99b3290493768fc08a39e75d1173a0e3452fdf
+kiwisolver==1.4.4; python_version >= "3.8"
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+llvmlite==0.39.1; python_version >= "3.7" \
+    --hash=sha256:6717c7a6e93c9d2c3d07c07113ec80ae24af45cde536b34363d4bcd9188091d9 \
+    --hash=sha256:ddab526c5a2c4ccb8c9ec4821fcea7606933dc53f510e2a6eebb45a418d3488a \
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+    --hash=sha256:b43abd7c82e805261c425d50335be9a6c4f84264e34d6d6e475207300005d572
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+    --hash=sha256:86b1f75c4e7c2ac2ccdaec2b9022845dbb81880ca318bb7a0a01fbf7813e3812 \
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+    --hash=sha256:7f91197cc9e48f989d12e4e6fbc46495c446636dfc81b9ccf50bb0ec74b91d4b
+matplotlib-inline==0.1.6; python_version >= "3.8"
+matplotlib==3.6.1; python_version >= "3.8" \
+    --hash=sha256:7730e60e751cfcfe7fcb223cf03c0b979e9a064c239783ad37929d340a364cef \
+    --hash=sha256:9dd40505ccc526acaf9a5db1b3029e237c64b58f1249983b28a291c2d6a1d0fa \
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+    --hash=sha256:8245e85fd793f58edf29b8a9e3be47e8ecf76ea1a1e8240545f2746181ca5787 \
+    --hash=sha256:1e2c75d5d1ff6b7ef9870360bfa23bea076b8dc0945a60d19453d7619ed9ea8f \
+    --hash=sha256:c9756a8e69f6e1f76d47eb42132175b6814da1fbeae0545304c6d0fc2aae252a \
+    --hash=sha256:6f5788168da2661b42f7468063b725cc73fdbeeb80f2704cb2d8c415e9a57c50 \
+    --hash=sha256:0bab7564aafd5902128d54b68dca04f5755413fb6b502100bb0235a545882c48 \
+    --hash=sha256:3c53486278a0629fd892783271dc994b962fba8dfe207445d039e14f1928ea46 \
+    --hash=sha256:27337bcb38d5db7430c14f350924542d75416ec1546d5d9d9f39b362b71db3fb \
+    --hash=sha256:fad858519bd6d52dbfeebdbe04d00dd8e932ed436f1c535e61bcc970a96c11e4 \
+    --hash=sha256:4a3d903588b519b38ed085d0ae762a1dcd4b70164617292175cfd91b90d6c415 \
+    --hash=sha256:87bdbd37d0a41e025879863fe9b17bab15c0421313bc33e77e5e1aa54215c9c5 \
+    --hash=sha256:e632f66218811d4cf8b7a2a649e25ec15406c3c498f72d19e2bcf8377f38445d \
+    --hash=sha256:8ddd58324dc9a77e2e56d7b7aea7dbd0575b6f7cd1333c3ca9d388ac70978344 \
+    --hash=sha256:12ab21d0cad122f5b23688d453a0280676e7c42f634f0dbd093d15d42d142b1f \
+    --hash=sha256:563896ba269324872ace436a57775dcc8322678a9496b28a8c25cdafa5ec2b92 \
+    --hash=sha256:52935b7d4ccbf0dbc9cf454dbb10ca99c11cbe8da9467596b96e5e21fd4dfc5c \
+    --hash=sha256:87027ff7b2edeb14476900261ef04d4beae949e1dfa0a3eb3ad6a6efbf9d0e1d \
+    --hash=sha256:a4de03085afb3b80fab341afaf8e60dfe06ce439b6dfed55d657cf34a7bc3c40 \
+    --hash=sha256:b53387d4e59432ff221540a4ffb5ee9669c69417805e4faf0148a00d701c61f9 \
+    --hash=sha256:02561141c434154f7bae8e5449909d152367cb40aa57bfb2a27f2748b9c5f95f \
+    --hash=sha256:d0161ebf87518ecfe0980c942d5f0d5df0e080c1746ebaab2027a969967014b7 \
+    --hash=sha256:2469f57e4c5cc0e85eddc7b30995ea9c404a78c0b1856da75d1a5887156ca350 \
+    --hash=sha256:5f97141e05baf160c3ec125f06ceb2a44c9bb62f42fcb8ee1c05313c73e99432 \
+    --hash=sha256:e2d1b7225666f7e1bcc94c0bc9c587a82e3e8691da4757e357e5c2515222ee37
+natsort==8.2.0; python_version >= "3.6" \
+    --hash=sha256:04fe18fdd2b9e5957f19f687eb117f102ef8dde6b574764e536e91194bed4f5f \
+    --hash=sha256:57f85b72c688b09e053cdac302dd5b5b53df5f73ae20b4874fcbffd8bf783d11
+nest-asyncio==1.5.6; python_version >= "3.7" \
+    --hash=sha256:b9a953fb40dceaa587d109609098db21900182b16440652454a146cffb06e8b8 \
+    --hash=sha256:d267cc1ff794403f7df692964d1d2a3fa9418ffea2a3f6859a439ff482fef290
+networkx==2.8.7; python_version >= "3.8" \
+    --hash=sha256:15cdf7f7c157637107ea690cabbc488018f8256fa28242aed0fb24c93c03a06d \
+    --hash=sha256:815383fd52ece0a7024b5fd8408cc13a389ea350cd912178b82eed8b96f82cd3
+numba==0.56.3; python_version >= "3.7" \
+    --hash=sha256:8848697e1e952b56886e84eecd5ae83d890ec0481c48ac803e13e2ab75a7d294 \
+    --hash=sha256:2256fee4332ab1e51cd0ec57086989e4011616ddb438659d99bb35822f43939b \
+    --hash=sha256:73529b33c0195e9f9e77b60896a5c5dc7ae0190bd8a1e3db15916b03befd641e \
+    --hash=sha256:c6b0539a30ab68903730f86ff6a9140e705b33c572402a42962b24a4e1fe8eb2 \
+    --hash=sha256:a776b5b177605569fbc04e6e68ba62e461ec5bbb1ea007a0c8d2e5f2a872c1a8 \
+    --hash=sha256:70f834f17136f6b403304ea2ffdeda0893c976540c4e9cd8f32b99258f75ada3 \
+    --hash=sha256:35da8eb35659cc7d8a603ecc3e65359166f5496b1e7b9b77f712a97893e9d81a \
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+    --hash=sha256:f9ae7b37e5cb53b62eb958f159f8defabd666af4ff1a9eb1de48cf2faf1dc918 \
+    --hash=sha256:99463b892e2e71fe6a10ffd4bd7e8517fac66cd73909b738fee715a2111e423c \
+    --hash=sha256:18f8c98641bf746b06b3d37efc09fe5ebb84c951df5ad8bd573795756d07e7f8 \
+    --hash=sha256:63c9fb3fdda56fababaa128a51209c8fabef12dd21491b1af41e0a6888e4c63e \
+    --hash=sha256:5398e6c75017f4dc04875cbc1efe28556b0e1719cff6c41b6cd7509a896698f2 \
+    --hash=sha256:79c154566b1a8f0644eb9c8f1808f66007bc8278791a44623cc3b7f3ddc61edd \
+    --hash=sha256:22c493a20d816980712768cb302eea417609a200348b784523e106a45a6e2185 \
+    --hash=sha256:ea63562915c15dff559c1eb45dced16b61cfbc7233ffb9444063421ec6c5ffaf \
+    --hash=sha256:c230f875a157772f51d82ba3e510cd97bdfec8fed4603e49f97d221332adc714 \
+    --hash=sha256:32a16bd6257b5e09e3227b886dea40ca16cf62df45c1914c630f43c5bf4ebf7e \
+    --hash=sha256:49c6ac44877d11523040ec1b92323184b23afa5619931a4e0137ed8d2b958819 \
+    --hash=sha256:20e2a02937b27800c2126d1efcb6c8e0535292e82434ab0d98b1060ffeb1867a \
+    --hash=sha256:9439331818d38fa1ca87ccbc211fe6bec48aad51d028a93a5d7214aee4de94d9 \
+    --hash=sha256:e668e2be37c76c74d4011f590f611b4fd3dac79438a491365391f2739ff2f233 \
+    --hash=sha256:0a413c2ce289071c62905308fc2fa66bc5f21b6f55d7539625c1ff3a11a17edf \
+    --hash=sha256:b4e300e749430e77bcc8b4d990dcc8e217a6d9bcae8f9f402686933d3a6ee53d \
+    --hash=sha256:bf8fbaff80aa9969da316b54280dd2707bb9dcfafcaaa58cc681206ccc1f8c57 \
+    --hash=sha256:35aeaae51bbbc87035e8c07189eac0d1d4b2490e43c503a585fa4a220b9d0320 \
+    --hash=sha256:0744cf4214ed795eb2df3ed1635d77a6ffcbd990a66a06125548b5fb8ee46323 \
+    --hash=sha256:07a2d8a149ecc6eca4ef5c7216e58511d48184854e07b7f59d0c32fab0742e8f
+numpy==1.23.4 \
+    --hash=sha256:95d79ada05005f6f4f337d3bb9de8a7774f259341c70bc88047a1f7b96a4bcb2 \
+    --hash=sha256:926db372bc4ac1edf81cfb6c59e2a881606b409ddc0d0920b988174b2e2a767f \
+    --hash=sha256:c237129f0e732885c9a6076a537e974160482eab8f10db6292e92154d4c67d71 \
+    --hash=sha256:a8365b942f9c1a7d0f0dc974747d99dd0a0cdfc5949a33119caf05cb314682d3 \
+    --hash=sha256:2341f4ab6dba0834b685cce16dad5f9b6606ea8a00e6da154f5dbded70fdc4dd \
+    --hash=sha256:d331afac87c92373826af83d2b2b435f57b17a5c74e6268b79355b970626e329 \
+    --hash=sha256:488a66cb667359534bc70028d653ba1cf307bae88eab5929cd707c761ff037db \
+    --hash=sha256:ce03305dd694c4873b9429274fd41fc7eb4e0e4dea07e0af97a933b079a5814f \
+    --hash=sha256:8981d9b5619569899666170c7c9748920f4a5005bf79c72c07d08c8a035757b0 \
+    --hash=sha256:7a70a7d3ce4c0e9284e92285cba91a4a3f5214d87ee0e95928f3614a256a1488 \
+    --hash=sha256:5e13030f8793e9ee42f9c7d5777465a560eb78fa7e11b1c053427f2ccab90c79 \
+    --hash=sha256:7607b598217745cc40f751da38ffd03512d33ec06f3523fb0b5f82e09f6f676d \
+    --hash=sha256:7ab46e4e7ec63c8a5e6dbf5c1b9e1c92ba23a7ebecc86c336cb7bf3bd2fb10e5 \
+    --hash=sha256:a8aae2fb3180940011b4862b2dd3756616841c53db9734b27bb93813cd79fce6 \
+    --hash=sha256:8c053d7557a8f022ec823196d242464b6955a7e7e5015b719e76003f63f82d0f \
+    --hash=sha256:a0882323e0ca4245eb0a3d0a74f88ce581cc33aedcfa396e415e5bba7bf05f68 \
+    --hash=sha256:dada341ebb79619fe00a291185bba370c9803b1e1d7051610e01ed809ef3a4ba \
+    --hash=sha256:0fe563fc8ed9dc4474cbf70742673fc4391d70f4363f917599a7fa99f042d5a8 \
+    --hash=sha256:c67b833dbccefe97cdd3f52798d430b9d3430396af7cdb2a0c32954c3ef73894 \
+    --hash=sha256:f76025acc8e2114bb664294a07ede0727aa75d63a06d2fae96bf29a81747e4a7 \
+    --hash=sha256:12ac457b63ec8ded85d85c1e17d85efd3c2b0967ca39560b307a35a6703a4735 \
+    --hash=sha256:95de7dc7dc47a312f6feddd3da2500826defdccbc41608d0031276a24181a2c0 \
+    --hash=sha256:f2f390aa4da44454db40a1f0201401f9036e8d578a25f01a6e237cea238337ef \
+    --hash=sha256:f260da502d7441a45695199b4e7fd8ca87db659ba1c78f2bbf31f934fe76ae0e \
+    --hash=sha256:61be02e3bf810b60ab74e81d6d0d36246dbfb644a462458bb53b595791251911 \
+    --hash=sha256:296d17aed51161dbad3c67ed6d164e51fcd18dbcd5dd4f9d0a9c6055dce30810 \
+    --hash=sha256:4d52914c88b4930dafb6c48ba5115a96cbab40f45740239d9f4159c4ba779962 \
+    --hash=sha256:ed2cc92af0efad20198638c69bb0fc2870a58dabfba6eb722c933b48556c686c
+nvidia-cublas-cu11==11.10.3.66; python_version >= "3" and python_full_version >= "3.7.0" \
+    --hash=sha256:d32e4d75f94ddfb93ea0a5dda08389bcc65d8916a25cb9f37ac89edaeed3bded \
+    --hash=sha256:8ac17ba6ade3ed56ab898a036f9ae0756f1e81052a317bf98f8c6d18dc3ae49e
+nvidia-cuda-nvrtc-cu11==11.7.99; python_version >= "3" and python_full_version >= "3.7.0" \
+    --hash=sha256:9f1562822ea264b7e34ed5930567e89242d266448e936b85bc97a3370feabb03 \
+    --hash=sha256:f7d9610d9b7c331fa0da2d1b2858a4a8315e6d49765091d28711c8946e7425e7 \
+    --hash=sha256:f2effeb1309bdd1b3854fc9b17eaf997808f8b25968ce0c7070945c4265d64a3
+nvidia-cuda-runtime-cu11==11.7.99; python_version >= "3" and python_full_version >= "3.7.0" \
+    --hash=sha256:cc768314ae58d2641f07eac350f40f99dcb35719c4faff4bc458a7cd2b119e31 \
+    --hash=sha256:bc77fa59a7679310df9d5c70ab13c4e34c64ae2124dd1efd7e5474b71be125c7
+nvidia-cudnn-cu11==8.5.0.96; python_version >= "3" and python_full_version >= "3.7.0" \
+    --hash=sha256:402f40adfc6f418f9dae9ab402e773cfed9beae52333f6d86ae3107a1b9527e7 \
+    --hash=sha256:71f8111eb830879ff2836db3cccf03bbd735df9b0d17cd93761732ac50a8a108
+oauthlib==3.2.2; python_version >= "3.7" and python_full_version < "3.0.0" or python_full_version >= "3.4.0" and python_version >= "3.7" \
+    --hash=sha256:8139f29aac13e25d502680e9e19963e83f16838d48a0d71c287fe40e7067fbca \
+    --hash=sha256:9859c40929662bec5d64f34d01c99e093149682a3f38915dc0655d5a633dd918
+opencv-python-headless==4.6.0.66; python_version >= "3.6"
+opt-einsum==3.3.0; python_version >= "3.7" \
+    --hash=sha256:2455e59e3947d3c275477df7f5205b30635e266fe6dc300e3d9f9646bfcea147 \
+    --hash=sha256:59f6475f77bbc37dcf7cd748519c0ec60722e91e63ca114e68821c0c54a46549
+packaging==21.3; python_version >= "3.8" \
+    --hash=sha256:ef103e05f519cdc783ae24ea4e2e0f508a9c99b2d4969652eed6a2e1ea5bd522 \
+    --hash=sha256:dd47c42927d89ab911e606518907cc2d3a1f38bbd026385970643f9c5b8ecfeb
+pandas==1.5.1; python_version >= "3.8" \
+    --hash=sha256:0a78e05ec09731c5b3bd7a9805927ea631fe6f6cb06f0e7c63191a9a778d52b4 \
+    --hash=sha256:5b0c970e2215572197b42f1cff58a908d734503ea54b326412c70d4692256391 \
+    --hash=sha256:f340331a3f411910adfb4bbe46c2ed5872d9e473a783d7f14ecf49bc0869c594 \
+    --hash=sha256:d8c709f4700573deb2036d240d140934df7e852520f4a584b2a8d5443b71f54d \
+    --hash=sha256:32e3d9f65606b3f6e76555bfd1d0b68d94aff0929d82010b791b6254bf5a4b96 \
+    --hash=sha256:a52419d9ba5906db516109660b114faf791136c94c1a636ed6b29cbfff9187ee \
+    --hash=sha256:66a1ad667b56e679e06ba73bb88c7309b3f48a4c279bd3afea29f65a766e9036 \
+    --hash=sha256:36aa1f8f680d7584e9b572c3203b20d22d697c31b71189322f16811d4ecfecd3 \
+    --hash=sha256:bcf1a82b770b8f8c1e495b19a20d8296f875a796c4fe6e91da5ef107f18c5ecb \
+    --hash=sha256:2c25e5c16ee5c0feb6cf9d982b869eec94a22ddfda9aa2fbed00842cbb697624 \
+    --hash=sha256:932d2d7d3cab44cfa275601c982f30c2d874722ef6396bb539e41e4dc4618ed4 \
+    --hash=sha256:eb7e8cf2cf11a2580088009b43de84cabbf6f5dae94ceb489f28dba01a17cb77 \
+    --hash=sha256:cb2a9cf1150302d69bb99861c5cddc9c25aceacb0a4ef5299785d0f5389a3209 \
+    --hash=sha256:81f0674fa50b38b6793cd84fae5d67f58f74c2d974d2cb4e476d26eee33343d0 \
+    --hash=sha256:17da7035d9e6f9ea9cdc3a513161f8739b8f8489d31dc932bc5a29a27243f93d \
+    --hash=sha256:669c8605dba6c798c1863157aefde959c1796671ffb342b80fcb80a4c0bc4c26 \
+    --hash=sha256:683779e5728ac9138406c59a11e09cd98c7d2c12f0a5fc2b9c5eecdbb4a00075 \
+    --hash=sha256:ddf46b940ef815af4e542697eaf071f0531449407a7607dd731bf23d156e20a7 \
+    --hash=sha256:db45b94885000981522fb92349e6b76f5aee0924cc5315881239c7859883117d \
+    --hash=sha256:927e59c694e039c75d7023465d311277a1fc29ed7236b5746e9dddf180393113 \
+    --hash=sha256:e675f8fe9aa6c418dc8d3aac0087b5294c1a4527f1eacf9fe5ea671685285454 \
+    --hash=sha256:04e51b01d5192499390c0015630975f57836cc95c7411415b499b599b05c0c96 \
+    --hash=sha256:5cee0c74e93ed4f9d39007e439debcaadc519d7ea5c0afc3d590a3a7b2edf060 \
+    --hash=sha256:b156a971bc451c68c9e1f97567c94fd44155f073e3bceb1b0d195fd98ed12048 \
+    --hash=sha256:05c527c64ee02a47a24031c880ee0ded05af0623163494173204c5b72ddce658 \
+    --hash=sha256:6bb391659a747cf4f181a227c3e64b6d197100d53da98dcd766cc158bdd9ec68 \
+    --hash=sha256:249cec5f2a5b22096440bd85c33106b6102e0672204abd2d5c014106459804ee
+parso==0.8.3; python_version >= "3.8" \
+    --hash=sha256:c001d4636cd3aecdaf33cbb40aebb59b094be2a74c556778ef5576c175e19e75 \
+    --hash=sha256:8c07be290bb59f03588915921e29e8a50002acaf2cdc5fa0e0114f91709fafa0
+pexpect==4.8.0; sys_platform != "win32" and python_version >= "3.8" \
+    --hash=sha256:0b48a55dcb3c05f3329815901ea4fc1537514d6ba867a152b581d69ae3710937 \
+    --hash=sha256:fc65a43959d153d0114afe13997d439c22823a27cefceb5ff35c2178c6784c0c
+pickleshare==0.7.5; python_version >= "3.8" \
+    --hash=sha256:9649af414d74d4df115d5d718f82acb59c9d418196b7b4290ed47a12ce62df56 \
+    --hash=sha256:87683d47965c1da65cdacaf31c8441d12b8044cdec9aca500cd78fc2c683afca
+pillow==9.3.0; python_version >= "3.8" \
+    --hash=sha256:0b7257127d646ff8676ec8a15520013a698d1fdc48bc2a79ba4e53df792526f2 \
+    --hash=sha256:b90f7616ea170e92820775ed47e136208e04c967271c9ef615b6fbd08d9af0e3 \
+    --hash=sha256:68943d632f1f9e3dce98908e873b3a090f6cba1cbb1b892a9e8d97c938871fbe \
+    --hash=sha256:be55f8457cd1eac957af0c3f5ece7bc3f033f89b114ef30f710882717670b2a8 \
+    --hash=sha256:5d77adcd56a42d00cc1be30843d3426aa4e660cab4a61021dc84467123f7a00c \
+    --hash=sha256:829f97c8e258593b9daa80638aee3789b7df9da5cf1336035016d76f03b8860c \
+    --hash=sha256:801ec82e4188e935c7f5e22e006d01611d6b41661bba9fe45b60e7ac1a8f84de \
+    --hash=sha256:871b72c3643e516db4ecf20efe735deb27fe30ca17800e661d769faab45a18d7 \
+    --hash=sha256:655a83b0058ba47c7c52e4e2df5ecf484c1b0b0349805896dd350cbc416bdd91 \
+    --hash=sha256:9f47eabcd2ded7698106b05c2c338672d16a6f2a485e74481f524e2a23c2794b \
+    --hash=sha256:57751894f6618fd4308ed8e0c36c333e2f5469744c34729a27532b3db106ee20 \
+    --hash=sha256:7db8b751ad307d7cf238f02101e8e36a128a6cb199326e867d1398067381bff4 \
+    --hash=sha256:3033fbe1feb1b59394615a1cafaee85e49d01b51d54de0cbf6aa8e64182518a1 \
+    --hash=sha256:22b012ea2d065fd163ca096f4e37e47cd8b59cf4b0fd47bfca6abb93df70b34c \
+    --hash=sha256:b9a65733d103311331875c1dca05cb4606997fd33d6acfed695b1232ba1df193 \
+    --hash=sha256:502526a2cbfa431d9fc2a079bdd9061a2397b842bb6bc4239bb176da00993812 \
+    --hash=sha256:90fb88843d3902fe7c9586d439d1e8c05258f41da473952aa8b328d8b907498c \
+    --hash=sha256:89dca0ce00a2b49024df6325925555d406b14aa3efc2f752dbb5940c52c56b11 \
+    --hash=sha256:3168434d303babf495d4ba58fc22d6604f6e2afb97adc6a423e917dab828939c \
+    --hash=sha256:18498994b29e1cf86d505edcb7edbe814d133d2232d256db8c7a8ceb34d18cef \
+    --hash=sha256:772a91fc0e03eaf922c63badeca75e91baa80fe2f5f87bdaed4280662aad25c9 \
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+prompt-toolkit==3.0.31; python_full_version >= "3.6.2" and python_version >= "3.8" \
+    --hash=sha256:9696f386133df0fc8ca5af4895afe5d78f5fcfe5258111c2a79a1c3e41ffa96d \
+    --hash=sha256:9ada952c9d1787f52ff6d5f3484d0b4df8952787c087edf6a1f7c2cb1ea88148
+protobuf==3.19.6; python_version >= "3.7" \
+    --hash=sha256:010be24d5a44be7b0613750ab40bc8b8cedc796db468eae6c779b395f50d1fa1 \
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+    --hash=sha256:5f5540d57a43042389e87661c6eaa50f47c19c6176e8cf1c4f287aeefeccb5c4
+psutil==5.9.3; python_version >= "3.7" and python_full_version < "3.0.0" or python_full_version >= "3.4.0" and python_version >= "3.7" \
+    --hash=sha256:b4a247cd3feaae39bb6085fcebf35b3b8ecd9b022db796d89c8f05067ca28e71 \
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+    --hash=sha256:7ccfcdfea4fc4b0a02ca2c31de7fcd186beb9cff8207800e14ab66f79c773af6
+ptyprocess==0.7.0; sys_platform != "win32" and python_version >= "3.8" \
+    --hash=sha256:4b41f3967fce3af57cc7e94b888626c18bf37a083e3651ca8feeb66d492fef35 \
+    --hash=sha256:5c5d0a3b48ceee0b48485e0c26037c0acd7d29765ca3fbb5cb3831d347423220
+pure-eval==0.2.2; python_version >= "3.8" \
+    --hash=sha256:01eaab343580944bc56080ebe0a674b39ec44a945e6d09ba7db3cb8cec289350 \
+    --hash=sha256:2b45320af6dfaa1750f543d714b6d1c520a1688dec6fd24d339063ce0aaa9ac3
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+    --hash=sha256:607c53218732647dff4acdfcd50cb62615cedf612e72d1724fb1a0cc6405b378 \
+    --hash=sha256:51c75c4126074b472f746a24399ad32f6053d1b34b68d2fa41e558e6f4a98719
+pyarrow==10.0.0; python_version >= "3.7" \
+    --hash=sha256:10e031794d019425d34406edffe7e32157359e9455f9edb97a1732f8dabf802f \
+    --hash=sha256:e4c6da9f9e1ff96781ee1478f7cc0860e66c23584887b8e297c4b9905c3c9066 \
+    --hash=sha256:4051664d354b14939b5da35cfa77821ade594bc1cf56dd2032b3068c96697d74 \
+    --hash=sha256:65d4a312f3ced318423704355acaccc7f7bdfe242472e59bdd54aa0f8837adf8 \
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+    --hash=sha256:7be7f42f713068293308c989a4a3a2de03b70199bdbe753901c6595ff8640c64 \
+    --hash=sha256:b3e3148468d3eed3779d68241f1d13ed4ee7cca4c6dbc7c07e5062b93ad4da33 \
+    --hash=sha256:2d326a9d47ac237d81b8c4337e9d30a0b361835b536fc7ea53991455ce761fbd \
+    --hash=sha256:25f51dca780fc22cfd7ac30f6bdfe70eb99145aee9acfda987f2c49955d66ed9 \
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+    --hash=sha256:b153b05765393557716e3729cf988442b3ae4f5567364ded40d58c07feed27c2
+pyasn1-modules==0.2.8; python_version >= "3.7" and python_full_version < "3.0.0" or python_full_version >= "3.6.0" and python_version >= "3.7" \
+    --hash=sha256:905f84c712230b2c592c19470d3ca8d552de726050d1d1716282a1f6146be65e \
+    --hash=sha256:0fe1b68d1e486a1ed5473f1302bd991c1611d319bba158e98b106ff86e1d7199 \
+    --hash=sha256:fe0644d9ab041506b62782e92b06b8c68cca799e1a9636ec398675459e031405 \
+    --hash=sha256:a99324196732f53093a84c4369c996713eb8c89d360a496b599fb1a9c47fc3eb \
+    --hash=sha256:0845a5582f6a02bb3e1bde9ecfc4bfcae6ec3210dd270522fee602365430c3f8 \
+    --hash=sha256:a50b808ffeb97cb3601dd25981f6b016cbb3d31fbf57a8b8a87428e6158d0c74 \
+    --hash=sha256:f39edd8c4ecaa4556e989147ebf219227e2cd2e8a43c7e7fcb1f1c18c5fd6a3d \
+    --hash=sha256:b80486a6c77252ea3a3e9b1e360bc9cf28eaac41263d173c032581ad2f20fe45 \
+    --hash=sha256:65cebbaffc913f4fe9e4808735c95ea22d7a7775646ab690518c056784bc21b4 \
+    --hash=sha256:15b7c67fabc7fc240d87fb9aabf999cf82311a6d6fb2c70d00d3d0604878c811 \
+    --hash=sha256:426edb7a5e8879f1ec54a1864f16b882c2837bfd06eee62f2c982315ee2473ed \
+    --hash=sha256:cbac4bc38d117f2a49aeedec4407d23e8866ea4ac27ff2cf7fb3e5b570df19e0 \
+    --hash=sha256:c29a5e5cc7a3f05926aff34e097e84f8589cd790ce0ed41b67aed6857b26aafd
+pyasn1==0.4.8; python_version >= "3.7" and python_full_version < "3.0.0" and python_version < "4" and (python_version >= "3.7" and python_full_version < "3.0.0" or python_full_version >= "3.6.0" and python_version >= "3.7") or python_full_version >= "3.6.0" and python_version >= "3.7" and python_version < "4" and (python_version >= "3.7" and python_full_version < "3.0.0" or python_full_version >= "3.6.0" and python_version >= "3.7") \
+    --hash=sha256:fec3e9d8e36808a28efb59b489e4528c10ad0f480e57dcc32b4de5c9d8c9fdf3 \
+    --hash=sha256:0458773cfe65b153891ac249bcf1b5f8f320b7c2ce462151f8fa74de8934becf \
+    --hash=sha256:5c9414dcfede6e441f7e8f81b43b34e834731003427e5b09e4e00e3172a10f00 \
+    --hash=sha256:6e7545f1a61025a4e58bb336952c5061697da694db1cae97b116e9c46abcf7c8 \
+    --hash=sha256:39c7e2ec30515947ff4e87fb6f456dfc6e84857d34be479c9d4a4ba4bf46aa5d \
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+    --hash=sha256:7ab8a544af125fb704feadb008c99a88805126fb525280b2270bb25cc1d78a12 \
+    --hash=sha256:e89bf84b5437b532b0803ba5c9a5e054d21fec423a89952a74f87fa2c9b7bce2 \
+    --hash=sha256:014c0e9976956a08139dc0712ae195324a75e142284d5f87f1a87ee1b068a359 \
+    --hash=sha256:99fcc3c8d804d1bc6d9a099921e39d827026409a58f2a720dcdb89374ea0c776 \
+    --hash=sha256:aef77c9fb94a3ac588e87841208bdec464471d9871bd5050a287cc9a475cd0ba
+pycparser==2.21; python_version >= "3.7" and python_full_version < "3.0.0" and implementation_name == "pypy" or implementation_name == "pypy" and python_version >= "3.7" and python_full_version >= "3.4.0" \
+    --hash=sha256:8ee45429555515e1f6b185e78100aea234072576aa43ab53aefcae078162fca9 \
+    --hash=sha256:e644fdec12f7872f86c58ff790da456218b10f863970249516d60a5eaca77206
+pydeck==0.8.0b4; python_version >= "3.7" \
+    --hash=sha256:714ec23fb21e47171e4ae350668ca8a1552bae29de94e5e749bd595a60100a09 \
+    --hash=sha256:103f303ef0a8f18edb10444d8136b901514779e84e340a86218e67427559e6d2
+pygments==2.13.0; python_full_version >= "3.6.3" and python_full_version < "4.0.0" and python_version >= "3.8"
+pympler==1.0.1; python_version >= "3.7"
+pyngrok==5.1.0; python_version >= "3.5"
+pyparsing==3.0.9; python_full_version >= "3.6.8" and python_version >= "3.8" \
+    --hash=sha256:5026bae9a10eeaefb61dab2f09052b9f4307d44aee4eda64b309723d8d206bbc \
+    --hash=sha256:2b020ecf7d21b687f219b71ecad3631f644a47f01403fa1d1036b0c6416d70fb
+pyrsistent==0.18.1; python_version >= "3.7" \
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+    --hash=sha256:6bc66318fb7ee012071b2792024564973ecc80e9522842eb4e17743604b5e045 \
+    --hash=sha256:914474c9f1d93080338ace89cb2acee74f4f666fb0424896fcfb8d86058bf17c \
+    --hash=sha256:1b34eedd6812bf4d33814fca1b66005805d3640ce53140ab8bbb1e2651b0d9bc \
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+    --hash=sha256:d4d61f8b993a7255ba714df3aca52700f8125289f84f704cf80916517c46eb96
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+    --hash=sha256:0123cacc1627ae19ddf3c27a5de5bd67ee4586fbdd6440d9748f8abb483d3e86 \
+    --hash=sha256:961d03dc3453ebbc59dbdea9e4e11c5651520a876d0f4db161e8674aae935da9
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+pytz==2022.5; python_version >= "3.8" \
+    --hash=sha256:335ab46900b1465e714b4fda4963d87363264eb662aab5e65da039c25f1f5b22 \
+    --hash=sha256:c4d88f472f54d615e9cd582a5004d1e5f624854a6a27a6211591c251f22a6914
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+pywin32==304; sys_platform == "win32" and platform_python_implementation != "PyPy" and python_version >= "3.7" \
+    --hash=sha256:3c7bacf5e24298c86314f03fa20e16558a4e4138fc34615d7de4070c23e65af3 \
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+    --hash=sha256:80093b595921eed1a2cead546a683b9e2ae7f4a4592bb2ab22f70d30174f003a \
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+    --hash=sha256:abb756147314430bee5d10919b8493c0ccb109ddb7f5dfd2fcd7441266a25b75 \
+    --hash=sha256:44e706bac34e9f50779cb8c39f10b53a4d15aebb97235643d3112ac20bd577b4 \
+    --hash=sha256:687700f8371643916a1d2c61f3fdaa630407dd205c38afff936545d7b7466066 \
+    --hash=sha256:216f5d7dbb67166759e59b0479bca82b8acf9bed6015b526b8eb10143fb08e77
+requests-oauthlib==1.3.1; python_version >= "3.7" and python_full_version < "3.0.0" or python_full_version >= "3.4.0" and python_version >= "3.7" \
+    --hash=sha256:75beac4a47881eeb94d5ea5d6ad31ef88856affe2332b9aafb52c6452ccf0d7a \
+    --hash=sha256:2577c501a2fb8d05a304c09d090d6e47c306fef15809d102b327cf8364bddab5
+requests==2.28.1; python_version >= "3.7" and python_version < "4" and (python_version >= "3.7" and python_full_version < "3.0.0" or python_full_version >= "3.4.0" and python_version >= "3.7")
+rich==12.6.0; python_full_version >= "3.6.3" and python_full_version < "4.0.0" and python_version >= "3.7" \
+    --hash=sha256:a4eb26484f2c82589bd9a17c73d32a010b1e29d89f1604cd9bf3a2097b81bb5e \
+    --hash=sha256:ba3a3775974105c221d31141f2c116f4fd65c5ceb0698657a11e9f295ec93fd0
+rsa==4.9; python_version >= "3.6" and python_version < "4" and (python_version >= "3.7" and python_full_version < "3.0.0" or python_full_version >= "3.6.0" and python_version >= "3.7")
+scikit-image==0.19.3; python_version >= "3.7"
+scipy==1.9.3; python_version >= "3.8" \
+    --hash=sha256:1884b66a54887e21addf9c16fb588720a8309a57b2e258ae1c7986d4444d3bc0 \
+    --hash=sha256:83b89e9586c62e787f5012e8475fbb12185bafb996a03257e9675cd73d3736dd \
+    --hash=sha256:1a72d885fa44247f92743fc20732ae55564ff2a519e8302fb7e18717c5355a8b \
+    --hash=sha256:d01e1dd7b15bd2449c8bfc6b7cc67d630700ed655654f0dfcf121600bad205c9 \
+    --hash=sha256:68239b6aa6f9c593da8be1509a05cb7f9efe98b80f43a5861cd24c7557e98523 \
+    --hash=sha256:b41bc822679ad1c9a5f023bc93f6d0543129ca0f37c1ce294dd9d386f0a21096 \
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+    --hash=sha256:d644a64e174c16cb4b2e41dfea6af722053e83d066da7343f333a54dae9bc31c \
+    --hash=sha256:da8245491d73ed0a994ed9c2e380fd058ce2fa8a18da204681f2fe1f57f98f95 \
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+    --hash=sha256:5b88e6d91ad9d59478fafe92a7c757d00c59e3bdc3331be8ada76a4f8d683f58 \
+    --hash=sha256:fbc5c05c85c1a02be77b1ff591087c83bc44579c6d2bd9fb798bb64ea5e1a027
+semver==2.13.0; python_version >= "3.7" and python_full_version < "3.0.0" or python_full_version >= "3.4.0" and python_version >= "3.7"
+setuptools-scm==7.0.5; python_version >= "3.8" \
+    --hash=sha256:7930f720905e03ccd1e1d821db521bff7ec2ac9cf0ceb6552dd73d24a45d3b02 \
+    --hash=sha256:031e13af771d6f892b941adb6ea04545bbf91ebc5ce68c78aaf3fff6e1fb4844
+six==1.16.0; python_version >= "3.8" and python_full_version < "3.0.0" or python_full_version >= "3.6.0" and python_version >= "3.8" \
+    --hash=sha256:8abb2f1d86890a2dfb989f9a77cfcfd3e47c2a354b01111771326f8aa26e0254 \
+    --hash=sha256:1e61c37477a1626458e36f7b1d82aa5c9b094fa4802892072e49de9c60c4c926
+smmap==5.0.0; python_version >= "3.7"
+stack-data==0.6.0; python_version >= "3.8" \
+    --hash=sha256:b92d206ef355a367d14316b786ab41cb99eb453a21f2cb216a4204625ff7bc07 \
+    --hash=sha256:8e515439f818efaa251036af72d89e4026e2b03993f3453c000b200fb4f2d6aa
+stardist==0.8.3; python_version >= "3.6"
+streamlit==1.12.0; python_version >= "3.7"
+tensorboard-data-server==0.6.1; python_version >= "3.7" \
+    --hash=sha256:809fe9887682d35c1f7d1f54f0f40f98bb1f771b14265b453ca051e2ce58fca7 \
+    --hash=sha256:fa8cef9be4fcae2f2363c88176638baf2da19c5ec90addb49b1cde05c95c88ee \
+    --hash=sha256:d8237580755e58eff68d1f3abefb5b1e39ae5c8b127cc40920f9c4fb33f4b98a
+tensorboard-plugin-wit==1.8.1; python_version >= "3.7" \
+    --hash=sha256:ff26bdd583d155aa951ee3b152b3d0cffae8005dc697f72b44a8e8c2a77a8cbe
+tensorboard==2.10.1; python_version >= "3.7" \
+    --hash=sha256:fb9222c1750e2fa35ef170d998a1e229f626eeced3004494a8849c88c15d8c1c
+tensorflow-estimator==2.10.0; python_version >= "3.7" \
+    --hash=sha256:f324ea17cd57f16e33bf188711d5077e6b2e5f5a12c328d6e01a07b23888edcd
+tensorflow-io-gcs-filesystem==0.27.0; python_version >= "3.7" and python_version < "3.11" \
+    --hash=sha256:babca2a12755badd1517043f9d633823533fbd7b463d7d36e9e6179b246731dc \
+    --hash=sha256:b3a0ebfeac11507f6fc96162b8b22010b7d715bb0848311e54ef18d88f07014a \
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+    --hash=sha256:564a7de156650cac9e1e361dabd6b5733a4ef31f5f11ef5eebf7fe694128334f \
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+    --hash=sha256:f7d24da555e2a1fe890b020b1953819ad990e31e63088a77ce87b7ffa67a7aaf \
+    --hash=sha256:ed17c281a28df9ab0547cdf166e885208d2a43db0f0f8fbe66addc4e23ee36ff \
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+    --hash=sha256:152f4c20e5341d486df35f7ce9751a441ed89b43c1036491cd2b30a742fbe20a
+tensorflow==2.10.0; python_version >= "3.7" \
+    --hash=sha256:60d5b4fbbb7a1304d96352372fa032e861e98bb3f23aced7ce53bc475a2df97d \
+    --hash=sha256:25e1e898bc1df521af9a8bfe0e511124379a6414083234ec67c6ab212ad12b2f \
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+    --hash=sha256:c588a1f34d9db51ea856aff07da9aa877c1d1d109336eee2c3bbb16dabd3f605 \
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+    --hash=sha256:741a74278f471dc21991a6c7dc802d454d42fd39515900c6363b8c38a898fb0f
+termcolor==2.0.1; python_version >= "3.7" \
+    --hash=sha256:7e597f9de8e001a3208c4132938597413b9da45382b6f1d150cff8d062b7aaa3 \
+    --hash=sha256:6b2cf769e93364a2676e1de56a7c0cff2cf5bd07f37e9cc80b0dd6320ebfe388
+tifffile==2022.10.10; python_version >= "3.8" \
+    --hash=sha256:87f3aee8a0d06b74655269a105de75c1958a24653e1930d523eb516100043503 \
+    --hash=sha256:50b61ba943b866d191295bc38a00191c9fdab23ece063544c7f1a264e3f6aa8e
+toml==0.10.2; python_version >= "3.7" and python_full_version < "3.0.0" or python_full_version >= "3.3.0" and python_version >= "3.7" \
+    --hash=sha256:806143ae5bfb6a3c6e736a764057db0e6a0e05e338b5630894a5f779cabb4f9b \
+    --hash=sha256:b3bda1d108d5dd99f4a20d24d9c348e91c4db7ab1b749200bded2f839ccbe68f
+tomli==2.0.1; python_version >= "3.8" \
+    --hash=sha256:939de3e7a6161af0c887ef91b7d41a53e7c5a1ca976325f429cb46ea9bc30ecc \
+    --hash=sha256:de526c12914f0c550d15924c62d72abc48d6fe7364aa87328337a31007fe8a4f
+toolz==0.12.0; python_version >= "3.7"
+torch==1.13.0; python_full_version >= "3.7.0" \
+    --hash=sha256:f68edfea71ade3862039ba66bcedf954190a2db03b0c41a9b79afd72210abd97 \
+    --hash=sha256:d2d2753519415d154de4d3e64d2eaaeefdba6b6fd7d69d5ffaef595988117700 \
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+    --hash=sha256:e643ac8d086706e82f77b5d4dfcf145a9dd37b69e03e64177fc23821754d2ed7 \
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+    --hash=sha256:f01a9ae0d4b69d2fc4145e8beab45b7877342dddbd4838a7d3c11ca7f6680745 \
+    --hash=sha256:9ac382cedaf2f70afea41380ad8e7c06acef6b5b7e2aef3971cdad666ca6e185 \
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+    --hash=sha256:4a378f5091307381abfb30eb821174e12986f39b1cf7c4522bf99155256819eb \
+    --hash=sha256:922a4910613b310fbeb87707f00cb76fec328eb60cc1349ed2173e7c9b6edcd8 \
+    --hash=sha256:47fe6228386bff6d74319a2ffe9d4ed943e6e85473d78e80502518c607d644d2
+tornado==6.2; python_version >= "3.7"
+tqdm==4.64.1; python_version >= "3.6" and python_full_version < "3.0.0" or python_full_version >= "3.4.0" and python_version >= "3.6" \
+    --hash=sha256:6fee160d6ffcd1b1c68c65f14c829c22832bc401726335ce92c52d395944a6a1 \
+    --hash=sha256:5f4f682a004951c1b450bc753c710e9280c5746ce6ffedee253ddbcbf54cf1e4
+traitlets==5.5.0; python_version >= "3.8" \
+    --hash=sha256:1201b2c9f76097195989cdf7f65db9897593b0dfd69e4ac96016661bb6f0d30f \
+    --hash=sha256:b122f9ff2f2f6c1709dab289a05555be011c87828e911c0cf4074b85cb780a79
+typing-extensions==4.4.0; python_version >= "3.8" and python_full_version >= "3.7.0" \
+    --hash=sha256:16fa4864408f655d35ec496218b85f79b3437c829e93320c7c9215ccfd92489e \
+    --hash=sha256:1511434bb92bf8dd198c12b1cc812e800d4181cfcb867674e0f8279cc93087aa
+tzdata==2022.5; platform_system == "Windows" and python_version >= "3.7" and (python_version >= "3.7" and python_full_version < "3.0.0" or python_full_version >= "3.6.0" and python_version >= "3.7") \
+    --hash=sha256:323161b22b7802fdc78f20ca5f6073639c64f1a7227c40cd3e19fd1d0ce6650a \
+    --hash=sha256:e15b2b3005e2546108af42a0eb4ccab4d9e225e2dfbf4f77aad50c70a4b1f3ab
+tzlocal==4.2; python_version >= "3.7"
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+validators==0.20.0; python_version >= "3.7"
+watchdog==2.1.9; platform_system != "Darwin" and python_version >= "3.7"
+wcwidth==0.2.5; python_full_version >= "3.6.2" and python_version >= "3.8" \
+    --hash=sha256:beb4802a9cebb9144e99086eff703a642a13d6a0052920003a230f3294bbe784 \
+    --hash=sha256:c4d647b99872929fdb7bdcaa4fbe7f01413ed3d98077df798530e5b04f116c83
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+wrapt==1.14.1; python_version >= "3.7" and python_full_version < "3.0.0" or python_full_version >= "3.5.0" and python_version >= "3.7" \
+    --hash=sha256:1b376b3f4896e7930f1f772ac4b064ac12598d1c38d04907e696cc4d794b43d3 \
+    --hash=sha256:903500616422a40a98a5a3c4ff4ed9d0066f3b4c951fa286018ecdf0750194ef \
+    --hash=sha256:5a9a0d155deafd9448baff28c08e150d9b24ff010e899311ddd63c45c2445e28 \
+    --hash=sha256:ddaea91abf8b0d13443f6dac52e89051a5063c7d014710dcb4d4abb2ff811a59 \
+    --hash=sha256:36f582d0c6bc99d5f39cd3ac2a9062e57f3cf606ade29a0a0d6b323462f4dd87 \
+    --hash=sha256:7ef58fb89674095bfc57c4069e95d7a31cfdc0939e2a579882ac7d55aadfd2a1 \
+    --hash=sha256:e2f83e18fe2f4c9e7db597e988f72712c0c3676d337d8b101f6758107c42425b \
+    --hash=sha256:ee2b1b1769f6707a8a445162ea16dddf74285c3964f605877a20e38545c3c462 \
+    --hash=sha256:833b58d5d0b7e5b9832869f039203389ac7cbf01765639c7309fd50ef619e0b1 \
+    --hash=sha256:80bb5c256f1415f747011dc3604b59bc1f91c6e7150bd7db03b19170ee06b320 \
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+    --hash=sha256:02b41b633c6261feff8ddd8d11c711df6842aba629fdd3da10249a53211a72c4 \
+    --hash=sha256:2fe803deacd09a233e4762a1adcea5db5d31e6be577a43352936179d14d90069 \
+    --hash=sha256:257fd78c513e0fb5cdbe058c27a0624c9884e735bbd131935fd49e9fe719d310 \
+    --hash=sha256:4fcc4649dc762cddacd193e6b55bc02edca674067f5f98166d7713b193932b7f \
+    --hash=sha256:11871514607b15cfeb87c547a49bca19fde402f32e2b1c24a632506c0a756656 \
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+    --hash=sha256:6d323e1554b3d22cfc03cd3243b5bb815a51f5249fdcbb86fda4bf62bab9e164 \
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+    --hash=sha256:6b1a564e6cb69922c7fe3a678b9f9a3c54e72b469875aa8018f18b4d1dd1adf3 \
+    --hash=sha256:00b6d4ea20a906c0ca56d84f93065b398ab74b927a7a3dbd470f6fc503f95dc3 \
+    --hash=sha256:a85d2b46be66a71bedde836d9e41859879cc54a2a04fad1191eb50c2066f6e9d \
+    --hash=sha256:dbcda74c67263139358f4d188ae5faae95c30929281bc6866d00573783c422b7 \
+    --hash=sha256:b21bb4c09ffabfa0e85e3a6b623e19b80e7acd709b9f91452b8297ace2a8ab00 \
+    --hash=sha256:9e0fd32e0148dd5dea6af5fee42beb949098564cc23211a88d799e434255a1f4 \
+    --hash=sha256:9736af4641846491aedb3c3f56b9bc5568d92b0692303b5a305301a95dfd38b1 \
+    --hash=sha256:5b02d65b9ccf0ef6c34cba6cf5bf2aab1bb2f49c6090bafeecc9cd81ad4ea1c1 \
+    --hash=sha256:21ac0156c4b089b330b7666db40feee30a5d52634cc4560e1905d6529a3897ff \
+    --hash=sha256:9f3e6f9e05148ff90002b884fbc2a86bd303ae847e472f44ecc06c2cd2fcdb2d \
+    --hash=sha256:6e743de5e9c3d1b7185870f480587b75b1cb604832e380d64f9504a0535912d1 \
+    --hash=sha256:d79d7d5dc8a32b7093e81e97dad755127ff77bcc899e845f41bf71747af0c569 \
+    --hash=sha256:81b19725065dcb43df02b37e03278c011a09e49757287dca60c5aecdd5a0b8ed \
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+    --hash=sha256:b5901a312f4d14c59918c221323068fad0540e34324925c8475263841dbdfe68 \
+    --hash=sha256:d77c85fedff92cf788face9bfa3ebaa364448ebb1d765302e9af11bf449ca36d \
+    --hash=sha256:8d649d616e5c6a678b26d15ece345354f7c2286acd6db868e65fcc5ff7c24a77 \
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+    --hash=sha256:ee6acae74a2b91865910eef5e7de37dc6895ad96fa23603d1d27ea69df545015 \
+    --hash=sha256:2b39d38039a1fdad98c87279b48bc5dce2c0ca0d73483b12cb72aa9609278e8a \
+    --hash=sha256:60db23fa423575eeb65ea430cee741acb7c26a1365d103f7b0f6ec412b893853 \
+    --hash=sha256:709fe01086a55cf79d20f741f39325018f4df051ef39fe921b1ebe780a66184c \
+    --hash=sha256:8c0ce1e99116d5ab21355d8ebe53d9460366704ea38ae4d9f6933188f327b456 \
+    --hash=sha256:e3fb1677c720409d5f671e39bac6c9e0e422584e5f518bfd50aa4cbbea02433f \
+    --hash=sha256:642c2e7a804fcf18c222e1060df25fc210b9c58db7c91416fb055897fc27e8cc \
+    --hash=sha256:7b7c050ae976e286906dd3f26009e117eb000fb2cf3533398c5ad9ccc86867b1 \
+    --hash=sha256:ef3f72c9666bba2bab70d2a8b79f2c6d2c1a42a7f7e2b0ec83bb2f9e383950af \
+    --hash=sha256:01c205616a89d09827986bc4e859bcabd64f5a0662a7fe95e0d359424e0e071b \
+    --hash=sha256:5a0f54ce2c092aaf439813735584b9537cad479575a09892b8352fea5e988dc0 \
+    --hash=sha256:2cf71233a0ed05ccdabe209c606fe0bac7379fdcf687f39b944420d2a09fdb57 \
+    --hash=sha256:aa31fdcc33fef9eb2552cbcbfee7773d5a6792c137b359e82879c101e98584c5 \
+    --hash=sha256:d1967f46ea8f2db647c786e78d8cc7e4313dbd1b0aca360592d8027b8508e24d \
+    --hash=sha256:3232822c7d98d23895ccc443bbdf57c7412c5a65996c30442ebe6ed3df335383 \
+    --hash=sha256:988635d122aaf2bdcef9e795435662bcd65b02f4f4c1ae37fbee7401c440b3a7 \
+    --hash=sha256:9cca3c2cdadb362116235fdbd411735de4328c61425b0aa9f872fd76d02c4e86 \
+    --hash=sha256:d52a25136894c63de15a35bc0bdc5adb4b0e173b9c0d07a2be9d3ca64a332735 \
+    --hash=sha256:40e7bc81c9e2b2734ea4bc1aceb8a8f0ceaac7c5299bc5d69e37c44d9081d43b \
+    --hash=sha256:b9b7a708dd92306328117d8c4b62e2194d00c365f18eff11a9b53c6f923b01e3 \
+    --hash=sha256:6a9a25751acb379b466ff6be78a315e2b439d4c94c1e99cb7266d40a537995d3 \
+    --hash=sha256:34aa51c45f28ba7f12accd624225e2b1e5a3a45206aa191f6f9aac931d9d56fe \
+    --hash=sha256:dee0ce50c6a2dd9056c20db781e9c1cfd33e77d2d569f5d1d9321c641bb903d5 \
+    --hash=sha256:dee60e1de1898bde3b238f18340eec6148986da0455d8ba7848d50470a7a32fb \
+    --hash=sha256:380a85cf89e0e69b7cfbe2ea9f765f004ff419f34194018a6827ac0e3edfed4d
+zipp==3.10.0; python_version >= "3.7" \
+    --hash=sha256:4fcb6f278987a6605757302a6e40e896257570d11c51628968ccb2a47e80c6c1 \
+    --hash=sha256:7a7262fd930bd3e36c50b9a64897aec3fafff3dfdeec9623ae22b40e93f99bb8