adding other paper code

cellpose/denoise.py

@@ -23,7 +23,7 @@
 
 MODEL_NAMES = []
 for ctype in ["cyto3", "cyto2", "nuclei"]:
-    for ntype in ["denoise", "deblur", "upsample"]:
+    for ntype in ["denoise", "deblur", "upsample", "oneclick"]:
         MODEL_NAMES.append(f"{ntype}_{ctype}")
         if ctype != "cyto3":
             for ltype in ["per", "seg", "rec"]:

paper/3.0/analysis.py

@@ -9,6 +9,7 @@
 from pathlib import Path
 import torch
 from torch import nn
+from tqdm import trange
 
 # in same folder
 try:
@@ -299,6 +300,106 @@ def real_examples(folder):
         dat2["ap_n2v"] = ap_n2v
         np.save(root / "n2v_masks.npy", dat2)
 
+def real_examples_ribo(root):
+    navgs = [1, 2, 4, 8, 16, 32, 64]
+    noisy = [[], [], [], [], [], [], []]
+    clean = []
+    for i in [1, 3, 6, 4, 5]:
+        imgs = io.imread(Path(root) / f"denoise_{i:05d}_00001.tif")[:300]
+        imgs = [imgs[:, :512, :512], imgs[:, 512:, :512], imgs[:, :512, 512:], imgs[:, 512:, 512:]]
+        clean.extend([img.mean(axis=0) for img in imgs])
+        for n, navg in enumerate(navgs):
+            iavg = np.linspace(0, len(imgs[0])-1, navg+2).astype(int)[1:-1]
+            noisy[n].extend(np.array([img[iavg].mean(axis=0) for img in imgs]))
+        print(len(clean), len(noisy[0]))
+
+    thresholds = np.arange(0.5, 1.05, 0.05)
+    diameter = 17
+    normalize = True # {"tile_norm_blocksize": 80}
+    seg_model = models.Cellpose(gpu=True, model_type="cyto2")
+    model = denoise.DenoiseModel(gpu=True, model_type="denoise_cyto2")
+    masks = seg_model.eval(clean, diameter=diameter, channels=[0,0], 
+                        normalize=normalize)[0]
+    ap_noisy = np.zeros((len(noisy), len(noisy[0]), len(thresholds)))
+    ap_dn = np.zeros((len(noisy), len(noisy[0]), len(thresholds)))
+    dat = {}
+    dat["navgs"] = navgs
+    dat["imgs_dn"] = []
+    dat["masks_dn"] = []
+    dat["masks_noisy"] = []
+    dat["masks_clean"] = masks
+    dat["noisy"] = noisy
+    dat["clean"] = clean
+    for n, imgs in enumerate(noisy):
+        masks_noisy = seg_model.eval(imgs, diameter=diameter, channels=[0,0],
+                                    normalize=normalize)[0]
+        img_dn = model.eval(imgs, diameter=diameter, channels=[0,0],
+                            normalize=normalize)
+        ap, tp, fp, fn = metrics.average_precision(masks, masks_noisy, threshold=thresholds)
+        ap_noisy[n] = ap
+        masks_dn = seg_model.eval(img_dn, diameter=diameter, channels=[0,0],
+                                normalize=normalize)[0]
+        ap, tp, fp, fn = metrics.average_precision(masks, masks_dn, threshold=thresholds)
+        ap_dn[n] = ap
+        dat["imgs_dn"].append(img_dn)
+        dat["masks_dn"].append(masks_dn)
+        dat["masks_noisy"].append(masks_noisy)
+        print(ap_noisy[n,:,0].mean(axis=0), ap_dn[n,:,0].mean(axis=0))
+    dat["ap_noisy"] = ap_noisy
+    dat["ap_dn"] = ap_dn
+    np.save(Path(root) / "ribo_denoise.npy", dat)
+
+    dat = {}
+    dat["navgs"] = navgs
+    dat["imgs_n2s"] = []
+    dat["masks_n2s"] = []
+    dat["masks_clean"] = masks
+    dat["noisy"] = noisy
+    dat["clean"] = clean
+    dat["ap_n2s"] = np.zeros((len(noisy), len(noisy[0]), len(thresholds)))
+
+    for n, imgs in enumerate(noisy):
+        imgs_n2s = []
+        for i in trange(len(imgs)):
+            out = noise2self.train_per_image(imgs[i][np.newaxis,...].astype("float32"))
+            imgs_n2s.append(out)
+        imgs_n2s = np.array(imgs_n2s)
+        masks_n2s = seg_model.eval(imgs_n2s, diameter=diameter, channels=[0,0])[0]
+        ap, tp, fp, fn = metrics.average_precision(masks, masks_n2s, threshold=thresholds)
+        dat["ap_n2s"][n] = ap
+        dat["imgs_n2s"].append(imgs_n2s)
+        dat["masks_n2s"].append(masks_n2s)
+        print(n, ap.mean(axis=0)[[0, 5, 8]])
+
+    np.save(Path(root) / "ribo_denoise_n2s.npy", dat)
+
+    dat = {}
+    dat["navgs"] = navgs
+    dat["imgs_n2v"] = []
+    dat["masks_n2v"] = []
+    dat["masks_clean"] = masks
+    dat["noisy"] = noisy
+    dat["clean"] = clean
+    dat["ap_n2v"] = np.zeros((len(noisy), len(noisy[0]), len(thresholds)))
+
+    for n, imgs in enumerate(noisy):
+        imgs_n2v = []
+        for i in trange(len(imgs)):
+            out = noise2void.train_per_image(imgs[i].astype("float32"))
+            imgs_n2v.append(out)
+        imgs_n2v = np.array(imgs_n2v)
+        masks_n2v = seg_model.eval(imgs_n2v, diameter=diameter, channels=[0,0],
+                                normalize=normalize)[0]
+        ap, tp, fp, fn = metrics.average_precision(masks, masks_n2v, threshold=thresholds)
+        #print(ap[:,0])
+        dat["ap_n2v"][n] = ap
+        dat["imgs_n2v"].append(imgs_n2v)
+        dat["masks_n2v"].append(masks_n2v)
+        print(n, ap.mean(axis=0)[[0, 5, 8]])
+
+    np.save(Path(root) / "ribo_denoise_n2v.npy", dat)
+
+
 
 def specialist_training(root):
     """ root is path to specialist images (first 89 images of cyto2 and first 11 test images) """

paper/3.0/figures.py

@@ -1204,9 +1204,9 @@ def suppfig_specialist(folder, save_fig=True):
 
     il = 0
 
-    fig = plt.figure(figsize=(9, 5), dpi=100)
+    fig = plt.figure(figsize=(9, 9), dpi=100)
     yratio = 9 / 5
-    grid = plt.GridSpec(2, 4, figure=fig, left=0.02, right=0.96, top=0.96, bottom=0.1,
+    grid = plt.GridSpec(3, 4, figure=fig, left=0.02, right=0.96, top=0.96, bottom=0.1,
                         wspace=0.15, hspace=0.2)
 
     titles = ["train - clean", "train - noisy", "test - noisy"]
@@ -1265,32 +1265,46 @@ def suppfig_specialist(folder, save_fig=True):
     ax.set_xticks(np.arange(0.5, 1.05, 0.1))
     ax.set_xlim([0.5, 1.0])
 
-    transl = mtransforms.ScaledTranslation(-10 / 72, 20 / 72, fig.dpi_scale_trans)
+    grid1 = matplotlib.gridspec.GridSpecFromSubplotSpec(2, 5, subplot_spec=grid[1:, :], wspace=0.05,
+                                                        hspace=0.1)
 
-    kk = [2, 3, 4, 10]
+    transl = mtransforms.ScaledTranslation(-10 / 72, 25 / 72, fig.dpi_scale_trans)
+
+    kk = [2, 3, 4, 6, 10]
     iex = 8
-    ylim = [10, 310]
-    xlim = [100, 500]
+    ylim = [125, 512] # [0, 350]
+    xlim = [50, 325] # [100, 500]
     legstr0[-1] = u"\u2013 Cellpose3 (per. + seg.)"
     for j, k in enumerate(kk):
-        ax = plt.subplot(grid[1, j])
-        pos = ax.get_position().bounds
-        ax.set_position([pos[0], pos[1] - 0.07, pos[2], pos[3]])
-        img0 = imgs_all[k][iex].squeeze()
-        img0 *= 1.1
-        img0 = np.clip(img0, 0, 1)
+        outlines_gt = utils.outlines_list(masks_all[0][iex].T.copy(), multiprocessing=False)
+        for ii in range(2):
+            ax = plt.subplot(grid1[ii, j])
+            pos = ax.get_position().bounds
+            ax.set_position([pos[0], pos[1] - 0.07 + ii*0.03, pos[2], pos[3]])
+            img0 = imgs_all[k][iex].squeeze().T
+            masks0 = masks_all[k][iex].squeeze().T
+            img0 *= 1.
+            img0 = np.clip(img0, 0, 1)
 
-        ax.imshow(img0, cmap="gray", vmin=0, vmax=1)
-        ax.axis("off")
-        ax.set_ylim(ylim)
-        ax.set_xlim(xlim)
-        ax.set_title(legstr0[k][2:], color=cols0[k], fontsize="medium")
-        ax.text(1, -0.04, f"[email protected] = {aps[k,iex,0] : 0.2f}", va="top", ha="right",
-                transform=ax.transAxes)
-        if j == 0:
-            il = plot_label(ltr, il, ax, transl, fs_title)
-            ax.text(0.02, 1.2, "Denoised test image", fontsize="large",
-                    fontstyle="italic", transform=ax.transAxes)
+            ax.imshow(img0, cmap="gray", vmin=0, vmax=1)
+            if ii==1:
+                outlines = utils.outlines_list(masks0, multiprocessing=False)
+                for o in outlines_gt:
+                    ax.plot(o[:, 0], o[:, 1], color=[0.7,0.4,1], lw=2)
+                for o in outlines:
+                    ax.plot(o[:, 0], o[:, 1], color=[1, 1, 0.3], lw=1.5, ls="--")
+            ax.axis("off")
+            ax.set_ylim(ylim)
+            ax.set_xlim(xlim)
+            if ii==0:
+                ax.set_title(legstr0[k][2:], color=cols0[k], fontsize="medium")
+            else:
+                ax.text(1, -0.04, f"[email protected] = {aps[k,iex,0] : 0.2f}", va="top", ha="right",
+                    transform=ax.transAxes)
+            if j == 0 and ii==0:
+                il = plot_label(ltr, il, ax, transl, fs_title)
+                ax.text(0.02, 1.15, "Denoised test image", fontsize="large",
+                        fontstyle="italic", transform=ax.transAxes)
 
     print(aps.mean(axis=1)[:, [0, 5, 8]])
 
@@ -1493,9 +1507,9 @@ def fig6(folder, save_fig=True):
 
     diams = [utils.diameters(lbl)[0] for lbl in lbls]
 
-    gen_model = "/home/carsen/dm11_string/datasets_cellpose/models/per_1.00_seg_1.50_rec_0.00_poisson_blur_downsample_2024_08_20_11_46_25.557039"
+    gen_model = "oneclick_cyto3" #"/home/carsen/dm11_string/datasets_cellpose/models/per_1.00_seg_1.50_rec_0.00_poisson_blur_downsample_2024_08_20_11_46_25.557039"
     model = denoise.DenoiseModel(gpu=True, nchan=1, diam_mean=diam_mean,
-                                 pretrained_model=gen_model)
+                                 model_type=gen_model)
     seg_model = models.CellposeModel(gpu=True, model_type="cyto3")
     pscales = [1.5, 20., 1.5, 1., 5., 40., 3.]
     denoise.deterministic()
@@ -1561,6 +1575,7 @@ def fig6(folder, save_fig=True):
     legstr0 = ["", u"\u2013 noisy image", u"\u2013 original", 
                 u"\u2013 noise-specific", "\u2013 data-specific", u"-- one-click"]
     theight = [0, 0,4,3,2,1]
+    cstr = ["noisy\nimage", "blurry\nimage", "bilinear\nupsampled"]
     for i in range(6):
         ctype = "cellpose test set" if i < 3 else "nuclei test set"
         noise_type = ["denoising", "deblurring", "upsampling"][i % 3]
@@ -1580,7 +1595,7 @@ def fig6(folder, save_fig=True):
         if i == 1 or i == 4:
             ax.text(0.5, 1.18, ctype, transform=ax.transAxes, ha="center",
                     fontsize="large")
-
+        ax.text(0.03, 0.03, cstr[i%3], transform=ax.transAxes, fontsize="small")
         ax.set_ylim([0, 0.72])
         ax.set_xticks(np.arange(0.5, 1.05, 0.25))
         ax.set_xlim([0.5, 1.0])
@@ -1593,9 +1608,98 @@ def fig6(folder, save_fig=True):
     ]
     colsj = cols0[[0, 1, -1]]
 
-    ly0 = 250
+    generalist_restoration_panels(fig, grid, imgs, lbls, masks, diams, api,
+                                  titlesj, colsj, titlesi, j0=0, il=il)
+
+    if save_fig:
+        os.makedirs("figs/", exist_ok=True)
+        fig.savefig("figs/fig6.pdf", dpi=150)
+
+def suppfig_generalist_examples(folder, save_fig=True):
+    cols0 = np.array([[0, 0, 0], [0, 0, 0], [0, 128, 0], [180, 229, 162], 
+                    [246, 198, 173], [192, 71, 29], ])
+    cols0 = cols0 / 255
+    titlesi = [
+        "Tissuenet", "Livecell", "Yeaz bright-field", "YeaZ phase-contrast",
+        "Omnipose phase-contrast", "Omnipose fluorescent", "DeepBacs"
+    ]
+    colsj = cols0[[0, 1, -1]]
+    folders = [
+        "cyto2", "nuclei", "tissuenet", "livecell", "yeast_BF", "yeast_PhC",
+        "bact_phase", "bact_fluor", "deepbacs"
+    ]
+    diam_mean = 30.
+
+    #iexs = [340, 50, 10, 5, 70, 2, 33]
+    iexs = [305, 1071, 0, 3, 70, 9, 31]
+    imgs, lbls = [[], [], []], []
+    masks = [[], [], []]
+    for f, iex in zip(folders[2:], iexs):
+        dat = np.load(Path(folder) / f"{f}_generalist_masks.npy",
+                      allow_pickle=True).item()
+        img = dat["imgs"][iex].copy()
+        img = img[:1] if img.ndim > 2 else img
+        img = np.maximum(0, transforms.normalize99(img))
+        imgs[0].append(img)
+        masks[0].append(dat["masks_pred"][iex])
+        lbls.append(dat["masks"][iex].astype("uint16"))
+
+    diams = [utils.diameters(lbl)[0] for lbl in lbls]
 
-    transl = mtransforms.ScaledTranslation(-15 / 72, 30 / 72, fig.dpi_scale_trans)
+    gen_model = "oneclick_cyto3" 
+    model = denoise.DenoiseModel(gpu=True, nchan=1, diam_mean=diam_mean,
+                                    model_type=gen_model)
+    seg_model = models.CellposeModel(gpu=True, model_type="cyto3")
+
+    fig = plt.figure(figsize=(14, 8), dpi=100)
+    grid = plt.GridSpec(4, 14, figure=fig, left=0.02, right=0.97, top=0.97, bottom=0.03)
+
+    for ii in range(2):
+        if ii==0:
+            titlesj = ["clean", "blurry", "deblurred (one-click)"]
+        else:
+            titlesj = ["clean", "downsampled", "upsampled (one-click)"]
+        masks[1] = []
+        masks[2] = []
+        imgs[1] = []
+        imgs[2] = []
+        sigmas = [5., 3., 7., 12., 5., 5., 3.]
+        ds = [6,4,8,8,6,6,6]
+        denoise.deterministic()
+        for i, img in tqdm(enumerate(imgs[0])):
+            img0 = torch.from_numpy(img.copy()).squeeze().unsqueeze(0).unsqueeze(0)
+            img0 = img0.float()
+            noisy0 = denoise.add_noise(img0, poisson=0., downsample=1. if ii==1 else 0, 
+                                       blur=1., ds=ds[i] if ii==1 else 0,
+                                    sigma0 = sigmas[i] if ii==0 else sigmas[i]/2,
+                                    sigma1 = sigmas[i] if ii==0 else sigmas[i]/2, 
+                                        pscale=120.).numpy().squeeze()
+            denoised0 = model.eval(noisy0, diameter=diams[i], normalize=True)
+
+            imgs[1].append(noisy0)
+            imgs[2].append(denoised0)
+            for j in range(1, 3):
+                masks[j].append(
+                    seg_model.eval(
+                        imgs[j][i], diameter=diams[i], channels=[0, 0], tile_overlap=0.5,
+                        flow_threshold=0.4, augment=True, bsize=224,
+                        niter=2000 if folders[i - 2] == "bact_phase" else None)[0])
+        api = np.array(
+            [metrics.average_precision(lbls, masks[i])[0][:, 0] for i in range(3)])
+
+        generalist_restoration_panels(fig, grid, imgs, lbls, masks, diams, api,
+                                    titlesj, colsj, titlesi, j0=-1 + 2*ii, letter=True)
+    if save_fig:
+        os.makedirs("figs/", exist_ok=True)
+        fig.savefig("figs/suppfig_genex.pdf", dpi=150)
+
+def generalist_restoration_panels(fig, grid, imgs, lbls, masks, diams, api,
+                                  titlesj, colsj, titlesi, j0=0, ly0=250, letter=False, il=0):
+    if letter:
+        il = j0>0
+        transl = mtransforms.ScaledTranslation(-20 / 72, 15 / 72, fig.dpi_scale_trans)
+    else:
+        transl = mtransforms.ScaledTranslation(-20 / 72, 5 / 72, fig.dpi_scale_trans)
     for i in range(len(imgs[0])):
         ratio = diams[i] / 30.
         d = utils.diameters(lbls[i])[0]
@@ -1608,20 +1712,18 @@ def fig6(folder, save_fig=True):
         for j in range(1, 3):
             img = np.clip(transforms.normalize99(imgs[j][i].copy().squeeze()), 0, 1)
             for k in range(2):
-                ax = plt.subplot(grid[j, 2 * i + k])
+                ax = plt.subplot(grid[j+j0, 2 * i + k])
                 pos = ax.get_position().bounds
                 ax.set_position([
-                    pos[0] + 0.003 * i - 0.00 * k, pos[1] - (2 - j) * 0.025 - 0.07,
+                    pos[0] + 0.003 * i - 0.00 * k, pos[1] - (2 - j) * 0.025 - 0.08*(j0==0),
                     pos[2], pos[3]
                 ])
                 if 1:
                     ax.imshow(img, cmap="gray", vmin=0,
-                              vmax=0.35 if j == 1 and i == 2 else 1.0)
+                              vmax=0.35 if j == 1 and i == 2 and j0==0 else 1.0)
                     if k == 1:
                         outlines = utils.outlines_list(masks[j][i],
                                                        multiprocessing=False)
-                        #for o in outlines_gt:
-                        #    ax.plot(o[:,0], o[:,1], color=[0.7,0.4,1], lw=1, ls="-")
                         for o in outlines:
                             ax.plot(o[:, 0], o[:, 1], color=[1, 1, 0.3], lw=1.5,
                                     ls="--")
@@ -1638,17 +1740,19 @@ def fig6(folder, save_fig=True):
                 if k == 0 and i == 0:
                     ax.text(-0.22, 0.5, titlesj[j], transform=ax.transAxes, va="center",
                             rotation=90, color=colsj[j], fontsize="medium")
-                    if j == 0:
+                    if j==1:
                         il = plot_label(ltr, il, ax, transl, fs_title)
-                        ax.text(-0.0, 1.22, "Denoising examples from other datasets",
+                        ax.text(-0.02, 1.05, "Denoising examples from other datasets",
                                 fontstyle="italic", transform=ax.transAxes,
                                 fontsize="large")
-                if k == 0 and j == 0:
-                    ax.text(0.0, 1.05, titlesi[i], transform=ax.transAxes,
-                            fontsize="medium")
-    if save_fig:
-        os.makedirs("figs/", exist_ok=True)
-        fig.savefig("figs/fig6.pdf", dpi=150)
+                    if j==1 and letter:
+                        ax.text(-0.0, 1.11, "Deblurring examples from other datasets" if j0==-1 else "Upsampling examples from other datasets",
+                                fontstyle="italic", transform=ax.transAxes,
+                                fontsize="large")
+                        il = plot_label(ltr, il, ax, transl, fs_title)
+                #if k == 0 and (j == 0 or (j==1 and j0==0)):
+                    #ax.text(0.0, 1.05, titlesi[i], transform=ax.transAxes,
+                     #       fontsize="medium")
 
 def load_seg_generalist(folder):
     folders = [