app.py

import numpy as np
import gradio as gr
import spaces
import cv2
from cellpose import models
from matplotlib.colors import hsv_to_rgb
import matplotlib.pyplot as plt
import os, io, base64
from PIL import Image 
from cellpose.io import imread, imsave

# @title Data retrieval
def download_weights():
    import os, requests
    
    fname = ['cpsam']
    
    url = ["https://osf.io/d7c8e/download"]
    
    for j in range(len(url)):
      if not os.path.isfile(fname[j]):
        try:
          r = requests.get(url[j])
        except requests.ConnectionError:
          print("!!! Failed to download data !!!")
        else:
          if r.status_code != requests.codes.ok:
            print("!!! Failed to download data !!!")
          else:
            with open(fname[j], "wb") as fid:
              fid.write(r.content)

try:
    download_weights()
    model = models.CellposeModel(gpu=True, pretrained_model="cpsam")
except Exception as e:
    print(f"Error loading model: {e}")
    exit(1)



            
def plot_flows(y):
    Y = (np.clip(normalize99(y[0][0]),0,1) - 0.5) * 2
    X = (np.clip(normalize99(y[1][0]),0,1) - 0.5) * 2
    H = (np.arctan2(Y, X) + np.pi) / (2*np.pi)
    S = normalize99(y[0][0]**2 + y[1][0]**2)
    HSV = np.concatenate((H[:,:,np.newaxis], S[:,:,np.newaxis], S[:,:,np.newaxis]), axis=-1)
    HSV = np.clip(HSV, 0.0, 1.0)
    flow = (hsv_to_rgb(HSV) * 255).astype(np.uint8)
    return flow

def plot_outlines(img, masks):
    outpix = []
    contours, hierarchy = cv2.findContours(masks.astype(np.int32), mode=cv2.RETR_FLOODFILL, method=cv2.CHAIN_APPROX_SIMPLE)
    for c in range(len(contours)):
        pix = contours[c].astype(int).squeeze()
        if len(pix)>4:
            peri = cv2.arcLength(contours[c], True)
            approx = cv2.approxPolyDP(contours[c], 0.001, True)[:,0,:]
            outpix.append(approx)
    
    figsize = (6,6)
    if img.shape[0]>img.shape[1]:
        figsize = (6*img.shape[1]/img.shape[0], 6)
    else:
        figsize = (6, 6*img.shape[0]/img.shape[1])
    fig = plt.figure(figsize=figsize, facecolor='k')
    ax = fig.add_axes([0.0,0.0,1,1])
    ax.set_xlim([0,img.shape[1]])
    ax.set_ylim([0,img.shape[0]])
    ax.imshow(img[::-1], origin='upper', aspect = 'auto')
    if outpix is not None:
        for o in outpix:
            ax.plot(o[:,0], img.shape[0]-o[:,1], color=[1,0,0], lw=1)
    ax.axis('off')
    
    #bytes_image = io.BytesIO()
    #plt.savefig(bytes_image, format='png', facecolor=fig.get_facecolor(), edgecolor='none')
    #bytes_image.seek(0)
    #img_arr = np.frombuffer(bytes_image.getvalue(), dtype=np.uint8)
    #bytes_image.close()
    #img = cv2.imdecode(img_arr, 1)
    #img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
    #del bytes_image
    #fig.clf()
    #plt.close(fig)

    buf = io.BytesIO()
    fig.savefig(buf, bbox_inches='tight')
    buf.seek(0)
    pil_img = Image.open(buf)

    return pil_img

def plot_overlay(img, masks):
    img = normalize99(img.astype(np.float32).mean(axis=-1))
    img -= img.min()
    img /= img.max()
    HSV = np.zeros((img.shape[0], img.shape[1], 3), np.float32)
    HSV[:,:,2] = np.clip(img*1.5, 0, 1.0)
    for n in range(int(masks.max())):
        ipix = (masks==n+1).nonzero()
        HSV[ipix[0],ipix[1],0] = np.random.rand()
        HSV[ipix[0],ipix[1],1] = 1.0
    RGB = (hsv_to_rgb(HSV) * 255).astype(np.uint8)
    return RGB

def normalize99(img):
    X = img.copy()
    X = (X - np.percentile(X, 1)) / (np.percentile(X, 99) - np.percentile(X, 1))
    return X

def image_resize(img, resize=400):
    ny,nx = img.shape[:2]
    if np.array(img.shape).max() > resize:
        if ny>nx:
            nx = int(nx/ny * resize)
            ny = resize
        else:
            ny = int(ny/nx * resize)
            nx = resize
        shape = (nx,ny)
        img = cv2.resize(img, shape)
    img = img.astype(np.uint8)
    return img

    
@spaces.GPU(duration=10)
def run_model_gpu(img):
    masks, flows, _ = model.eval(img)#, channels = [0,0])
    return masks, flows

@spaces.GPU(duration=60)
def run_model_gpu60(img):
    masks, flows, _ = model.eval(img)#, channels = [0,0])
    return masks, flows

@spaces.GPU(duration=240)
def run_model_gpu240(img):
    masks, flows, _ = model.eval(img)#, channels = [0,0])
    return masks, flows

@spaces.GPU(duration=1000)
def run_model_gpu1000(img):
    masks, flows, _ = model.eval(img)#, channels = [0,0])
    return masks, flows

#@spaces.GPU(duration=10)
def cellpose_segment(img_pil, resize = 400):
    img_input = imread(img_pil)
    #img_input = np.array(img_pil)
    img = image_resize(img_input, resize = resize)

    resize = np.max(img.shape)
    if resize<1000:
        masks, flows = run_model_gpu(img)
    elif resize < 5000:
        masks, flows = run_model_gpu60(img)
    elif resize < 20000:
        masks, flows = run_model_gpu240(img)
    else:
        raise ValueError("Image size must be less than 20,000")
    
        
    #masks, flows, _ = model.eval(img, channels=[0,0])
    flows = flows[0]
    # masks = np.zeros(img.shape[:2])
    # flows = np.zeros_like(img)

    outpix = plot_outlines(img, masks)
    overlay = plot_overlay(img, masks)
    
    target_size = (img_input.shape[1], img_input.shape[0])
    if (target_size[0]!=img.shape[1] or target_size[1]!=img.shape[0]):
        # scale it back to keep the orignal size
        masks = cv2.resize(masks.astype('uint16'), target_size, interpolation=cv2.INTER_NEAREST).astype('uint16')
        #flows = cv2.resize(flows.astype('float32'), target_size).astype('uint8')
    
    
    #crand = .2 + .8 * np.random.rand(np.max(masks.flatten()).astype('int')+1,).astype('float32')
    #crand[0] = 0

    overlay = Image.fromarray(overlay)
    flows = Image.fromarray(flows)

    Ly, Lx = img.shape[:2]
    c = Lx
    outpix = outpix.resize((Lx, Ly), resample  = Image.BICUBIC)
    overlay = overlay.resize((Lx, Ly), resample  = Image.BICUBIC)
    flows = flows.resize((Lx, Ly), resample  = Image.BICUBIC)

    #masks = Image.fromarray(255. * crand[masks])
    #pil_masks = Image.fromarray(masks.astype('int32'))
    #pil_masks.save(fname_mask)

    fname_out  = os.path.splitext(img_pil)[0]+"_outlines.png"
    fname_masks = os.path.splitext(img_pil)[0]+"_masks.tif"

    imsave(fname_masks, masks)
    
    
    outpix.save(fname_out) #"outlines.png")

    b1 = gr.DownloadButton(visible=True, value = fname_masks)
    b2 = gr.DownloadButton(visible=True, value = fname_out) #"outlines.png")
    
    return outpix, overlay, flows, b1, b2

# Gradio Interface
#iface = gr.Interface(
#    fn=cellpose_segment, 
#    inputs="image", 
#    outputs=["image", "image", "image", "image"],
#    title="cellpose segmentation",
#    description="upload an image, then cellpose will segment it at a max size of 400x400 (for full functionality, 'pip install cellpose' locally)"
#)

def download_function(): 
    b1 = gr.DownloadButton("Download masks as TIFF", visible=False)
    b2 = gr.DownloadButton("Download outline image as PNG", visible=False)
    return b1, b2

with gr.Blocks(title = "Hello", 
               css=".gradio-container {background:purple;}") as demo:

    with gr.Row():
        with gr.Column(scale=2):
            gr.HTML("""<div style="font-family:'Times New Roman', 'Serif'; font-size:16pt; font-weight:bold; text-align:center; color:white;">Cellpose-SAM for cellular segmentation</div>""")
            gr.HTML("""<h4 style="color:white;">You may need to refresh/login for 5 minutes of free GPU compute/day. </h4>""")
            gr.HTML("""<h4 style="color:white;">"pip install cellpose" for full functionality. </h4>""")

            input_image = gr.Image(label = "Input image", type = "filepath")

            with gr.Row():
                resize = gr.Number(label = 'max resize', value = 400)
                send_btn = gr.Button("Run Cellpose-SAM")
                
            with gr.Row():
                down_btn = gr.DownloadButton("Download masks (TIF)", visible=False)            
                down_btn2 = gr.DownloadButton("Download outlines (PNG)", visible=False)

            gr.HTML("""<a style="color:white;" href="https://github.com/MouseLand/cellpose" target="_blank">github page for cellpose</a>""")
            gr.HTML("""<a style="color:white;" href="https://github.com/MouseLand/cellpose" target="_blank">Cellpose-SAM paper</a>""")


        with gr.Column(scale=2):            
            img_outlines = gr.Image(label = "Outlines", type = "pil", format = 'png') #, width = "50vw", height = "20vw")
            img_overlay = gr.Image(label = "Overlay", type = "pil", format = 'png') #, width = "50vw", height = "20vw")
            flows = gr.Image(label = "Cellpose flows", type = "pil", format = 'png') #, width = "50vw", height = "20vw")
            #masks = gr.Image(label = "Output image", type = "numpy")
    
    
    send_btn.click(fn=cellpose_segment, inputs=[input_image, resize], outputs=[img_outlines, img_overlay, flows, down_btn, down_btn2])

    #down_btn.click(download_function, None, [down_btn, down_btn2])
        
    
    

demo.launch()