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from transformers import SegformerForSemanticSegmentation
from transformers import SegformerImageProcessor
from PIL import Image
import gradio as gr
import numpy as np
import random
import cv2
import torch
from imutils import perspective
def midpoint(ptA, ptB):
return ((ptA[0] + ptB[0]) * 0.5, (ptA[1] + ptB[1]) * 0.5)
# Load in image, convert to gray scale, and Otsu's threshold
kernel1 =( np.ones((5,5), dtype=np.float32))
blur_radius=0.5
kernel_sharpening = np.array([[-1,-1,-1],
[-1,9,-1],
[-1,-1,-1]])*(1/9)
def cca_analysis(image,predicted_mask):
image2=np.asarray(image)
print(image.shape)
image = cv2.resize(predicted_mask, (image2.shape[1],image2.shape[1]), interpolation = cv2.INTER_AREA)
image=cv2.morphologyEx(image, cv2.MORPH_OPEN, kernel1,iterations=1 )
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
thresh = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)[1]
labels=cv2.connectedComponents(thresh,connectivity=8)[1]
a=np.unique(labels)
count2=0
for label in a:
if label == 0:
continue
# Create a mask
mask = np.zeros(thresh.shape, dtype="uint8")
mask[labels == label] = 255
# Find contours and determine contour area
cnts,hieararch = cv2.findContours(mask.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
cnts = cnts[0]
c_area = cv2.contourArea(cnts)
# threshhold for tooth count
if c_area>100:
count2+=1
rect = cv2.minAreaRect(cnts)
box = cv2.boxPoints(rect)
box = np.array(box, dtype="int")
box = perspective.order_points(box)
color1 = (list(np.random.choice(range(150), size=3)))
color =[int(color1[0]), int(color1[1]), int(color1[2])]
cv2.drawContours(image2,[box.astype("int")],0,color,2)
(tl,tr,br,bl)=box
(tltrX,tltrY)=midpoint(tl,tr)
(blbrX,blbrY)=midpoint(bl,br)
# compute the midpoint between the top-left and top-right points,
# followed by the midpoint between the top-righ and bottom-right
(tlblX,tlblY)=midpoint(tl,bl)
(trbrX,trbrY)=midpoint(tr,br)
# draw the midpoints on the image
cv2.circle(image2, (int(tltrX), int(tltrY)), 5, (255, 0, 0), -1)
cv2.circle(image2, (int(blbrX), int(blbrY)), 5, (255, 0, 0), -1)
cv2.circle(image2, (int(tlblX), int(tlblY)), 5, (255, 0, 0), -1)
cv2.circle(image2, (int(trbrX), int(trbrY)), 5, (255, 0, 0), -1)
cv2.line(image2, (int(tltrX), int(tltrY)), (int(blbrX), int(blbrY)),color, 2)
cv2.line(image2, (int(tlblX), int(tlblY)), (int(trbrX), int(trbrY)),color, 2)
return image2
def to_rgb(img):
result_new=np.zeros((img.shape[1],img.shape[0],3))
result_new[:,:,0]=img
result_new[:,:,1]=img
result_new[:,:,2]=img
result_new=np.uint8(result_new*255)
return result_new
image_list = [
"data/1.png",
"data/2.png",
"data/3.png",
"data/4.png",
]
model_path = ['deprem-ml/deprem_satellite_semantic_whu']
def visualize_instance_seg_mask(mask):
# Initialize image with zeros with the image resolution
# of the segmentation mask and 3 channels
image = np.zeros((mask.shape[0], mask.shape[1], 3))
# Create labels
labels = np.unique(mask)
label2color = {
label: (
random.randint(0, 255),
random.randint(0, 255),
random.randint(0, 255),
)
for label in labels
}
for height in range(image.shape[0]):
for width in range(image.shape[1]):
image[height, width, :] = label2color[mask[height, width]]
image = image / 255
return image
def Segformer_Segmentation(image_path, model_id,postpro):
output_save = "output.png"
test_image = cv2.imread(image_path)
model = SegformerForSemanticSegmentation.from_pretrained(model_id)
proccessor = SegformerImageProcessor(model_id)
inputs = proccessor(images=test_image, return_tensors="pt")
with torch.no_grad():
outputs = model(**inputs)
result = proccessor.post_process_semantic_segmentation(outputs)[0]
result = np.array(result)
if postpro=="Connected Components Labelling":
result=to_rgb(result)
result=cca_analysis(test_image,result)
else:
result = visualize_instance_seg_mask(result)
result=result*255
cv2.imwrite(output_save, result)
return image_path, output_save
examples = [[image_list[0], "deprem-ml/deprem_satellite_semantic_whu"],
[image_list[1], "deprem-ml/deprem_satellite_semantic_whu"],
[image_list[2], "deprem-ml/deprem_satellite_semantic_whu"],
[image_list[3], "deprem-ml/deprem_satellite_semantic_whu"]]
title = "Deprem ML - Segformer Semantic Segmentation"
app = gr.Blocks()
with app:
gr.HTML("<h1 style='text-align: center'>{}</h1>".format(title))
with gr.Row():
with gr.Column():
input_video = gr.Image(type='filepath')
model_id = gr.Dropdown(value=model_path[0], choices=model_path,label="Model Name")
cca = gr.Dropdown(value="Connected Components Labelling", choices=["Connected Components Labelling","No Post Process"],label="Post Process")
input_video_button = gr.Button(value="Predict")
with gr.Column():
output_orijinal_image = gr.Image(type='filepath')
with gr.Column():
output_mask_image = gr.Image(type='filepath')
gr.Examples(examples, inputs=[input_video, model_id,cca], outputs=[output_orijinal_image, output_mask_image], fn=Segformer_Segmentation, cache_examples=True)
input_video_button.click(Segformer_Segmentation, inputs=[input_video, model_id,cca], outputs=[output_orijinal_image, output_mask_image])
app.launch(debug=True)
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