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import cv2
import numpy as np
import math
import torch
import random
from PIL import Image
from torch.utils.data import DataLoader
from torchvision.transforms import Resize
torch.manual_seed(12345)
random.seed(12345)
np.random.seed(12345)
device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
class WireframeExtractor:
def __call__(self, image: np.ndarray):
"""
Extract corners of wireframe from a barnacle image
:param image: Numpy RGB image of shape (W, H, 3)
:return [x1, y1, x2, y2]
"""
h, w = image.shape[:2]
imghsv = cv2.cvtColor(image, cv2.COLOR_RGB2HSV)
hsvblur = cv2.GaussianBlur(imghsv, (9, 9), 0)
lower = np.array([70, 20, 20])
upper = np.array([130, 255, 255])
color_mask = cv2.inRange(hsvblur, lower, upper)
invert = cv2.bitwise_not(color_mask)
contours, _ = cv2.findContours(invert, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)
max_contour = contours[0]
largest_area = 0
for index, contour in enumerate(contours):
area = cv2.contourArea(contour)
if area > largest_area:
if cv2.pointPolygonTest(contour, (w / 2, h / 2), False) == 1:
largest_area = area
max_contour = contour
x, y, w, h = cv2.boundingRect(max_contour)
# return [x, y, x + w, y + h]
return x,y,w,h
wireframe_extractor = WireframeExtractor()
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)
polygons = []
color = []
for ann in sorted_anns:
m = ann['segmentation']
img = np.ones((m.shape[0], m.shape[1], 3))
color_mask = np.random.random((1, 3)).tolist()[0]
for i in range(3):
img[:,:,i] = color_mask[i]
ax.imshow(np.dstack((img, m*0.35)))
from segment_anything import sam_model_registry, SamAutomaticMaskGenerator, SamPredictor
model = sam_model_registry["default"](checkpoint="./sam_vit_h_4b8939.pth")
model.to(device)
predictor = SamPredictor(model)
mask_generator = SamAutomaticMaskGenerator(model)
import gradio as gr
import matplotlib.pyplot as plt
import io
def check_circularity(segmentation):
img_u8 = segmentation.astype(np.uint8)
im_gauss = cv2.GaussianBlur(img_u8, (5, 5), 0)
ret, thresh = cv2.threshold(im_gauss, 0, 255, cv2.THRESH_BINARY)
contours, hierarchy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
con = contours[0]
perimeter = cv2.arcLength(con, True)
area = cv2.contourArea(con)
if perimeter != 0:
circularity = 4*math.pi*(area/(perimeter*perimeter))
if 0.8 < circularity < 1.2:
return True
else:
return circularity
def count_barnacles(image_raw, split_num, progress=gr.Progress()):
progress(0, desc="Finding bounding wire")
corners = wireframe_extractor(image_raw)
print(corners) # (0, 0, 1254, 1152)
cropped_image = image_raw[corners[1]:corners[3]+corners[1], corners[0]:corners[2]+corners[0], :]
print(cropped_image.shape)
split_num = 3
x_inc = int(cropped_image.shape[0]/split_num)
y_inc = int(cropped_image.shape[1]/split_num)
startx = -x_inc
mask_counter = 0
good_masks = []
centers = []
for r in range(0, split_num):
startx += x_inc
starty = -y_inc
for c in range(0, split_num):
starty += y_inc
small_image = cropped_image[starty:starty+y_inc, startx:startx+x_inc, :]
# plt.figure()
# plt.imshow(small_image)
# plt.axis('on')
mask_generator.predictor.set_image(small_image)
progress(0, desc=f"Generating masks for crop {r*split_num + c}/{split_num ** 2}")
masks = mask_generator.generate(small_image)
num_masks = len(masks)
for idx, mask in enumerate(masks):
progress(float(idx)/float(num_masks), desc=f"Processing masks for crop {r*split_num + c}/{split_num ** 2}")
circular = check_circularity(mask['segmentation'])
if circular and mask['area']>500 and mask['area'] < 10000:
mask_counter += 1
good_masks.append(mask)
box = mask['bbox']
centers.append((box[0] + box[2]/2 + corners[0] + startx, box[1] + box[3]/2 + corners[1] + starty))
progress(0, desc="Generating Plot")
# Create a figure with a size of 10 inches by 10 inches
fig = plt.figure(figsize=(40, 40))
# Display the image using the imshow() function
# plt.imshow(cropped_image)
plt.imshow(image_raw)
# Call the custom function show_anns() to plot annotations on top of the image
# show_anns(good_masks)
for coord in centers:
plt.scatter(coord[0], coord[1], marker="x", color="red", s=32)
# Turn off the axis
plt.axis('off')
# Get the plot as a numpy array
# buf = io.BytesIO()
# plt.savefig(buf, format='png', bbox_inches='tight', pad_inches=0)
# buf.seek(0)
# img_arr = np.frombuffer(buf.getvalue(), dtype=np.uint8)
# buf.close()
# # Decode the numpy array to an image
# annotated = cv2.imdecode(img_arr, 1)
# annotated = cv2.cvtColor(annotated, cv2.COLOR_BGR2RGB)
# # Close the figure
# plt.close(fig)
# return annotated, mask_counter, centers
return fig, mask_counter, centers
demo = gr.Interface(count_barnacles,
inputs=[
gr.Image(type="numpy", label="Input Image"),
],
outputs=[
# gr.Image(type="numpy", label="Annotated Image"),
gr.Plot(label="Annotated Image"),
gr.Number(label="Predicted Number of Barnacles"),
gr.Dataframe(type="array", headers=["x", "y"], label="Mask centers")
# gr.Number(label="Actual Number of Barnacles"),
# gr.Number(label="Custom Metric")
])
# examples="examples")
demo.queue(concurrency_count=1).launch() |