def vis_on_batch(self, batch, savedir_image):
self.eval()
images = batch["images"].to(self.device)
points = batch["points"].long().to(self.device)
logits = self.model_base.forward(images)
probs = logits.sigmoid().cpu().numpy()
blobs = lcfcn_loss.get_blobs(probs=probs)
pred_counts = (np.unique(blobs)!=0).sum()
pred_blobs = blobs
pred_probs = probs.squeeze()
# loc
pred_count = pred_counts.ravel()[0]
pred_blobs = pred_blobs.squeeze()
pred_points = lcfcn_loss.blobs2points(pred_blobs).squeeze()
img_org = hu.get_image(batch["images"],denorm="rgb")
i1 = convert(np.array(img_org), batch['points'][0], enlarge=20)
i2 = convert(np.array(img_org), pred_blobs, enlarge=0)
i3 = convert(np.array(img_org), pred_points, enlarge=20)
hu.save_image(savedir_image, np.hstack([i1, i2, i3]))
def val_on_batch(self, batch):
self.eval()
images = batch["images"].to(self.device)
points = batch["points"].long().to(self.device)
logits = self.model_base.forward(images)
probs = logits.sigmoid().cpu().numpy()
blobs = lcfcn_loss.get_blobs(probs=probs)
pred_points = lcfcn_loss.blobs2points(blobs).squeeze()
mae = abs(float((np.unique(blobs)!=0).sum() - (points!=0).sum()))
game = lcfcn_loss.compute_game(pred_points=pred_points.squeeze(), gt_points=points.squeeze().cpu().numpy(), L=3)
return {'mae':mae, 'game':game }
def vis_on_batch(self, batch, savedir_image):
self.eval()
images = batch["images"].to('cpu') #helen changed this from .cuda()
#print(images)
#print(images.shape)
points = batch["points"].long().to(
'cpu') #helen changed this from .cuda()
logits = self.model.forward(images)
probs = logits.sigmoid().cpu().numpy()
blobs = lcfcn_loss.get_blobs(probs=probs)
pred_counts = (np.unique(blobs) != 0).sum()
pred_blobs = blobs
pred_probs = probs.squeeze()
# loc
pred_count = pred_counts.ravel()[0]
pred_blobs = pred_blobs.squeeze()
img_org = hu.get_image(batch["images"], denorm="rgb")
# true points
y_list, x_list = np.where(batch["points"][0].long().numpy().squeeze())
img_peaks = hi.points_on_image(y_list, x_list, img_org, radius=11)
text = "%s ground truth" % (batch["points"].sum().item())
hi.text_on_image(text=text, image=img_peaks)
# pred points
pred_points = lcfcn_loss.blobs2points(pred_blobs).squeeze()
y_list, x_list = np.where(pred_points.squeeze())
img_pred = hi.mask_on_image(img_org, pred_blobs)
#img_pred = hi.points_on_image(y_list, x_list, img_org)
text = "%s predicted" % (len(y_list))
hi.text_on_image(text=text, image=img_pred)
# *************** helen added this code
plt.imshow(
img_pred
) #these lines of code display the image with the model's predications on it
plt.show()
# *************** end of helen's code
# heatmap
heatmap = hi.gray2cmap(pred_probs)
heatmap = hu.f2l(heatmap)
hi.text_on_image(text="lcfcn heatmap", image=heatmap)
img_mask = np.hstack([img_peaks, img_pred, heatmap])
hu.save_image(savedir_image, img_mask)
def vis_on_batch_helen(self, batch, savedir_image):
self.eval()
images = batch.to('cpu')
#print(images) #print statement to check what images is for debugging
#print(images.shape) #print statement to check images has the correct shape
logits = self.model.forward(images)
probs = logits.sigmoid().cpu().numpy()
blobs = lcfcn_loss.get_blobs(probs=probs)
pred_counts = (np.unique(blobs) != 0).sum()
pred_blobs = blobs
pred_probs = probs.squeeze()
# loc
pred_count = pred_counts.ravel()[0]
pred_blobs = pred_blobs.squeeze()
img_org = hu.get_image(images, denorm="rgb")
#this is what was originally written: img_org = hu.get_image(batch["images"],denorm="rgb")
#the following lines are commented out because my own data does not have labels and will thus throw errors
# true points
#y_list, x_list = np.where(batch["points"][0].long().numpy().squeeze())
#img_peaks = hi.points_on_image(y_list, x_list, img_org, radius=11)
#text = "%s ground truth" % (batch["points"].sum().item())
#hi.text_on_image(text=text, image=img_peaks)
# pred points
pred_points = lcfcn_loss.blobs2points(pred_blobs).squeeze()
y_list, x_list = np.where(pred_points.squeeze())
img_pred = hi.mask_on_image(img_org, pred_blobs)
# img_pred = hi.points_on_image(y_list, x_list, img_org)
text = "%s predicted" % (len(y_list))
hi.text_on_image(text=text, image=img_pred)
# these lines of code display the image with the model's predications on it
plt.imshow(img_pred)
plt.show()
# heatmap
heatmap = hi.gray2cmap(pred_probs)
heatmap = hu.f2l(heatmap)
hi.text_on_image(text="lcfcn heatmap", image=heatmap)
img_mask = np.hstack([img_pred, heatmap]) #helen took out im_peaks
#this is what was originally written: img_mask = np.hstack([img_peaks, img_pred, heatmap])
hu.save_image(savedir_image, img_mask)
def vis_on_batch(self, batch, savedir_image):
self.eval()
images = batch["images"].cuda()
points = batch["points"].long().cuda()
logits = self.model_base.forward(images)
probs = logits.sigmoid().cpu().numpy()
blobs = lcfcn_loss.get_blobs(probs=probs)
pred_counts = (np.unique(blobs)!=0).sum()
pred_blobs = blobs
pred_probs = probs.squeeze()
# loc
pred_count = pred_counts.ravel()[0]
pred_blobs = pred_blobs.squeeze()
img_org = hu.get_image(batch["images"],denorm="rgb")
# true points
y_list, x_list = np.where(batch["points"][0].long().numpy().squeeze())
img_peaks = haven_img.points_on_image(y_list, x_list, img_org)
text = "%s ground truth" % (batch["points"].sum().item())
haven_img.text_on_image(text=text, image=img_peaks)
# pred points
pred_points = lcfcn_loss.blobs2points(pred_blobs).squeeze()
y_list, x_list = np.where(pred_points.squeeze())
img_pred = hi.mask_on_image(img_org, pred_blobs)
# img_pred = haven_img.points_on_image(y_list, x_list, img_org)
text = "%s predicted" % (len(y_list))
haven_img.text_on_image(text=text, image=img_pred)
# heatmap
heatmap = hi.gray2cmap(pred_probs)
heatmap = hu.f2l(heatmap)
haven_img.text_on_image(text="lcfcn heatmap", image=heatmap)
img_mask = np.hstack([img_peaks, img_pred, heatmap])
hu.save_image(savedir_image, img_mask)