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import numpy as np
import torch
import matplotlib.pyplot as plt
from streamlit_image_coordinates import streamlit_image_coordinates
import streamlit as st
from PIL import Image
from transformers import SamModel, SamProcessor
import cv2
# Define global constants
MAX_WIDTH = 700
# Define helpful functions
def show_anns(anns):
if len(anns) == 0:
return
sorted_anns = sorted(anns, key=(lambda x: x['area']), reverse=True)
ax = plt.gca()
ax.set_autoscale_on(False)
img = np.ones((sorted_anns[0]['segmentation'].shape[0], sorted_anns[0]['segmentation'].shape[1], 4))
img[:,:,3] = 0
for ann in sorted_anns:
m = ann['segmentation']
color_mask = np.concatenate([np.random.random(3), [0.35]])
img[m] = color_mask
ax.imshow(img)
def show_mask(mask, ax, random_color=False):
if random_color:
color = np.concatenate([np.random.random(3), np.array([0.6])], axis=0)
else:
color = np.array([30/255, 144/255, 255/255, 0.6])
h, w = mask.shape[-2:]
mask_image = mask.reshape(h, w, 1) * color.reshape(1, 1, -1)
ax.imshow(mask_image)
def show_points(coords, labels, ax, marker_size=20):
pos_points = coords[labels==1]
ax.scatter(pos_points[:, 0], pos_points[:, 1], color='green', marker='.', s=marker_size, edgecolor='white', linewidth=0.2)
def show_masks_on_image(raw_image, masks, scores):
if len(masks.shape) == 4:
masks = masks.squeeze()
if scores.shape[0] == 1:
scores = scores.squeeze()
nb_predictions = scores.shape[-1]
fig, ax = plt.subplots(1, nb_predictions)
for i, (mask, score) in enumerate(zip(masks, scores)):
mask = mask.cpu().detach()
ax[i].imshow(np.array(raw_image))
show_mask(mask, ax[i])
ax[i].title.set_text(f"Mask {i+1}, Score: {score.item():.3f}")
ax[i].axis("off")
def show_points_on_image(raw_image, input_point, ax, input_labels=None):
ax.imshow(raw_image)
input_point = np.array(input_point)
if input_labels is None:
labels = np.ones_like(input_point[:, 0])
else:
labels = np.array(input_labels)
show_points(input_point, labels, ax)
ax.axis('on')
# Get SAM
if torch.cuda.is_available():
device = 'cuda'
else:
device = 'cpu'
model = SamModel.from_pretrained("facebook/sam-vit-huge").to(device)
processor = SamProcessor.from_pretrained("facebook/sam-vit-huge")
# Get uploaded files from user
scale = st.file_uploader('Upload Scale Image')
image = st.file_uploader('Upload Particle Image')
# Runs when scale image is uploaded
if scale:
scale_np = np.asarray(bytearray(scale.read()), dtype=np.uint8)
scale_np = cv2.imdecode(scale_np, 1)
#inputs = processor(raw_image, return_tensors="pt").to(device)
inputs = processor(scale_np, return_tensors="pt").to(device)
image_embeddings = model.get_image_embeddings(inputs["pixel_values"])
scale_factor = scale_np.shape[1] / MAX_WIDTH # how many times larger scale_np is than the image shown for each dimension
clicked_point = streamlit_image_coordinates(Image.open(scale.name), height=scale_np.shape[0] // scale_factor, width=MAX_WIDTH)
if clicked_point:
input_point_np = np.array([[clicked_point['x'], clicked_point['y']]]) * scale_factor
input_point_list = [input_point_np.astype(int).tolist()]
#inputs = processor(raw_image, input_points=input_point, return_tensors="pt").to(device)
inputs = processor(scale_np, input_points=input_point_list, return_tensors="pt").to(device)
inputs.pop("pixel_values", None)
inputs.update({"image_embeddings": image_embeddings})
with torch.no_grad():
outputs = model(**inputs)
masks = processor.image_processor.post_process_masks(outputs.pred_masks.cpu(), inputs["original_sizes"].cpu(), inputs["reshaped_input_sizes"].cpu())
mask = torch.squeeze(masks[0])[0] # mask.shape: (1,x,y) --> (x,y)
mask = mask.to(torch.int)
input_label = np.array([1])
fig, ax = plt.subplots()
ax.imshow(scale_np)
show_mask(mask, ax)
#show_points_on_image(scale_np, input_point, input_label, ax)
show_points(input_point_np, input_label, ax)
ax.axis('off')
st.pyplot(fig)
# Get pixels per millimeter
pixels_per_unit = torch.sum(mask, axis=1)
pixels_per_unit = pixels_per_unit[pixels_per_unit > 0]
pixels_per_unit = torch.mean(pixels_per_unit, dtype=torch.float).item()
# Runs when image is uploaded
if image:
image_np = np.asarray(bytearray(image.read()), dtype=np.uint8)
image_np = cv2.imdecode(image_np, 1)
#inputs = processor(raw_image, return_tensors="pt").to(device)
inputs = processor(image_np, return_tensors="pt").to(device)
image_embeddings = model.get_image_embeddings(inputs["pixel_values"])
scale_factor = image_np.shape[1] / MAX_WIDTH # how many times larger scale_np is than the image shown for each dimension
clicked_point = streamlit_image_coordinates(Image.open(image.name), height=image_np.shape[0] // scale_factor, width=MAX_WIDTH)
if clicked_point:
input_point_np = np.array([[clicked_point['x'], clicked_point['y']]]) * scale_factor
input_point_list = [input_point_np.astype(int).tolist()]
#inputs = processor(raw_image, input_points=input_point, return_tensors="pt").to(device)
inputs = processor(image_np, input_points=input_point_list, return_tensors="pt").to(device)
inputs.pop("pixel_values", None)
inputs.update({"image_embeddings": image_embeddings})
with torch.no_grad():
outputs = model(**inputs)
masks = processor.image_processor.post_process_masks(outputs.pred_masks.cpu(), inputs["original_sizes"].cpu(), inputs["reshaped_input_sizes"].cpu())
mask = torch.squeeze(masks[0])[0] # mask.shape: (1,x,y) --> (x,y)
mask = mask.to(torch.int)
input_label = np.array([1])
fig, ax = plt.subplots()
ax.imshow(image_np)
show_mask(mask, ax)
#show_points_on_image(scale_np, input_point, input_label, ax)
show_points(input_point_np, input_label, ax)
ax.axis('off')
st.pyplot(fig)
# Get the area in square millimeters
st.write(f'Area: {torch.sum(mask, dtype=torch.float).item() / pixels_per_unit ** 2} mm^2') |