Henry Scheible
decrease number of crops
29c7e10
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)
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 = 2
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')
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=(10, 10))
# 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()