??? ??, ?? ?? ??? ???? ???? ?? ?? ???? ?? ? ?? ??? ??? ?? ?????. ?? ?? ??, ??? ??, ??? ??? ??? ?? ?? ???? ???? ??? ???. ??? ??? ????? ???? ??? ? ????? ?????? OpenCV(cv2)? ????? ???? ??? ?? ??? ?????.

? ??????? ? ?? ?? ?? ??? ??? ???????.

• ?? ?? ?? – ??? ??? ?? ? ?????.
• ?? ???? – ??? ??? ???? ? ?????.
• K-?? ?? ?? – ????? ??? ??? ??????? ? ??????.

? ????? ???? ????? ??? ?? ?? ?? ??? ???? ?? ???? ?? ? ?? ??? ???????. ????? GitHub ????? ??? ???? ?? ?? ???? ????? ? ????.

?? ??? ?? ?? ?? ??

?? ?? ??? ???? ??? ???? ?????? ??? ?????. ?? ?? ??? ??? ??? ?? ?? ??? ???? ?????. ? ??? ?? ???? ???? "?? ????" ???? ?????. OpenCV? ???? ??? ???????.

?: ?? ????? ???? ??
???? ???? ????, ?? ?? ??? ??? ??? ?????.

import cv2 
import numpy as np
import matplotlib.pyplot as plt
files = sorted(glob("SAT*.png")) #Get png files 
print(len(files))
img=cv2.imread(files[0])
use_image= img[0:600,700:1300]
gray = cv2.cvtColor(use_image, cv2.COLOR_BGR2GRAY)

#Stadard values 
min_val = 100
max_val = 200
# Apply Canny Edge Detection
edges = cv2.Canny(gray, min_val, max_val)
#edges = cv2.Canny(gray, min_val, max_val,apertureSize=5,L2gradient = True )
False
# Show the result
plt.figure(figsize=(15, 5))
plt.subplot(131), plt.imshow(cv2.cvtColor(use_image, cv2.COLOR_BGR2RGB))
plt.title('Original Image'), plt.axis('off')
plt.subplot(132), plt.imshow(gray, cmap='gray')
plt.title('Grayscale Image'), plt.axis('off')
plt.subplot(133), plt.imshow(edges, cmap='gray')
plt.title('Canny Edges'), plt.axis('off')
plt.show()

?? ?????? ?? ??? ?? ?? ??? ??? ???? ?????. ??? ???? ????? ?? ?? ??? ???????. ??? min_val ? max_val? ??? ??? ??? ?? ?? ?????.

???? ?? ??? ????? ?? ???? ????? ?? ??? ????? ???? ?? ? ????. ?? ????? ???(cv2.equalizeHist()) ? ??? ??(cv2.GaussianBlur())? ???? ??? ? ????.

use_image= img[0:600,700:1300]
gray = cv2.cvtColor(use_image, cv2.COLOR_BGR2GRAY)
gray_og = gray.copy()
gray = cv2.equalizeHist(gray)
gray = cv2.GaussianBlur(gray, (9, 9),1)

plt.figure(figsize=(15, 5))
plt.subplot(121), plt.imshow(gray, cmap='gray')
plt.title('Grayscale Image')
plt.subplot(122)
_= plt.hist(gray.ravel(), 256, [0,256],label="Equalized") 
_ = plt.hist(gray_og.ravel(), 256, [0,256],label="Original",histtype='step')
plt.legend()
plt.title('Grayscale Histogram')

??? ???? ?? ??? ??? ?? ???? ???? ?? Canny Edge ?? ????? ?? ?? ?? ????? ???? ? ??? ???.

????? ????? ??? ?????. ?? ??? ???? ?? ????? ????? ???? ??????.

# Edges to contours 
contours, hierarchy = cv2.findContours(edges, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
# Calculate contour areas
areas = [cv2.contourArea(contour) for contour in contours]

# Normalize areas for the colormap
normalized_areas = np.array(areas)
if normalized_areas.max() > 0:
    normalized_areas = normalized_areas / normalized_areas.max()

# Create a colormap
cmap = plt.cm.jet

# Plot the contours with the color map
plt.figure(figsize=(10, 10))
plt.subplot(1,2,1)
plt.imshow(gray, cmap='gray', alpha=0.5)  # Display the grayscale image in the background
mask = np.zeros_like(use_image)
for contour, norm_area in zip(contours, normalized_areas):
    color = cmap(norm_area)  # Map the normalized area to a color
    color = [int(c*255) for c in color[:3]]
    cv2.drawContours(mask, [contour], -1, color,-1 )  # Draw contours on the image

plt.subplot(1,2,2)

? ??? ??? ??? ?? ??? ???? ???? ?? ?????. ? ???? ??? ?? ??? ?????, ??? ??? ?? ????? ???? ? ??? ???. ??? ? ???? ?? ???? ??? ?? ????? ?? ????. ?? ??? ?? ???? ??? ?? ??? ?? ??? ??? ? ????.

???? ?? ??? ??? Canny Edge ??? ????? ?? ??? ???? ??? ??? ???. ??? ??? ? ???? ???? ???? ? ?? ? ?????. ???? K-?? ?????? ???? ???? ???? ???? ??? ???? ?? ?? ??? ???????.

KMean ?????

K-?? ?????? ??? ??? ????? ????? ??? ???? ?? ???? ????, ?? ???? ???? ???? ???? ? ?? ??????. OpenCV? cv2.kmeans ??? ? ????? ????? ?? ??, ?? ?? ?? ??? ??? ?? ??? ???? ? ?? ????.

? ????? K-Means Clustering? ???? ?? ?? ???? ??? ??? ???? ?????.

????? ???? RGB ?? K-?? ?????? ???? ? ??? ??? ???? ?????.

import cv2 
import numpy as np
import matplotlib.pyplot as plt
files = sorted(glob("SAT*.png")) #Get png files 
print(len(files))
img=cv2.imread(files[0])
use_image= img[0:600,700:1300]
gray = cv2.cvtColor(use_image, cv2.COLOR_BGR2GRAY)

#Stadard values 
min_val = 100
max_val = 200
# Apply Canny Edge Detection
edges = cv2.Canny(gray, min_val, max_val)
#edges = cv2.Canny(gray, min_val, max_val,apertureSize=5,L2gradient = True )
False
# Show the result
plt.figure(figsize=(15, 5))
plt.subplot(131), plt.imshow(cv2.cvtColor(use_image, cv2.COLOR_BGR2RGB))
plt.title('Original Image'), plt.axis('off')
plt.subplot(132), plt.imshow(gray, cmap='gray')
plt.title('Grayscale Image'), plt.axis('off')
plt.subplot(133), plt.imshow(edges, cmap='gray')
plt.title('Canny Edges'), plt.axis('off')
plt.show()

??? ?????? ??? ?? ??? ??? ???? ???? ????. ??? ???? ??? ?? ??? ?? ????? ????? ??? ??? ? ????.

???? ??? ? ???? ????? ??? ?? K-??? ???? ?? ??? ??? ??? ? ????.

use_image= img[0:600,700:1300]
gray = cv2.cvtColor(use_image, cv2.COLOR_BGR2GRAY)
gray_og = gray.copy()
gray = cv2.equalizeHist(gray)
gray = cv2.GaussianBlur(gray, (9, 9),1)

plt.figure(figsize=(15, 5))
plt.subplot(121), plt.imshow(gray, cmap='gray')
plt.title('Grayscale Image')
plt.subplot(122)
_= plt.hist(gray.ravel(), 256, [0,256],label="Equalized") 
_ = plt.hist(gray_og.ravel(), 256, [0,256],label="Original",histtype='step')
plt.legend()
plt.title('Grayscale Histogram')

??? ???? ?? ????? ? ?????? ???? ???? ??? ??? ????? ? ??? ?? ???.

?? ??? ? ? ???? ?? matplotlib plt.fill_between;? ???? ??? ???? ??? ?? ?? ?? ? ????.

# Edges to contours 
contours, hierarchy = cv2.findContours(edges, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
# Calculate contour areas
areas = [cv2.contourArea(contour) for contour in contours]

# Normalize areas for the colormap
normalized_areas = np.array(areas)
if normalized_areas.max() > 0:
    normalized_areas = normalized_areas / normalized_areas.max()

# Create a colormap
cmap = plt.cm.jet

# Plot the contours with the color map
plt.figure(figsize=(10, 10))
plt.subplot(1,2,1)
plt.imshow(gray, cmap='gray', alpha=0.5)  # Display the grayscale image in the background
mask = np.zeros_like(use_image)
for contour, norm_area in zip(contours, normalized_areas):
    color = cmap(norm_area)  # Map the normalized area to a color
    color = [int(c*255) for c in color[:3]]
    cv2.drawContours(mask, [contour], -1, color,-1 )  # Draw contours on the image

plt.subplot(1,2,2)

? ???? ???? ?? ??? ?? RGB ?? ?? ???? ????? ?? ??? ??? ? ????. ?? ? ?? ??? ????? ??? ??? ? ????.

???? ?(K)? ??? ? ??? ????. K? ??? ?? ???? ??? ????, ?? ???? ? ?? ???? ?????. ??? ?? ?? K ?? ??? ? ????.

import cv2 
import numpy as np
import matplotlib.pyplot as plt
files = sorted(glob("SAT*.png")) #Get png files 
print(len(files))
img=cv2.imread(files[0])
use_image= img[0:600,700:1300]
gray = cv2.cvtColor(use_image, cv2.COLOR_BGR2GRAY)

#Stadard values 
min_val = 100
max_val = 200
# Apply Canny Edge Detection
edges = cv2.Canny(gray, min_val, max_val)
#edges = cv2.Canny(gray, min_val, max_val,apertureSize=5,L2gradient = True )
False
# Show the result
plt.figure(figsize=(15, 5))
plt.subplot(131), plt.imshow(cv2.cvtColor(use_image, cv2.COLOR_BGR2RGB))
plt.title('Original Image'), plt.axis('off')
plt.subplot(132), plt.imshow(gray, cmap='gray')
plt.title('Grayscale Image'), plt.axis('off')
plt.subplot(133), plt.imshow(edges, cmap='gray')
plt.title('Canny Edges'), plt.axis('off')
plt.show()

??? K ?? ?? ????? ??? ????? ??? ??? ??? ?????.

??? K ?(?: 2-3): ??? ??? ?? ???? ????? ?? ?? ??? ?????.
?K ?? ????(?: 12-15): ? ????? ?????? ???? ???? ???? ???? ???? ????.

K-?? ?????? ?? ???? ???? ???? ???? ??? ?????. ??? ??? ??? ?? ???? ?? ?? ??? ?????. ??? ??? K? ??, ?? ???? ??, ??? ???? ?? ?????. ???? ??? ??? ???? ??? ??? ???? ???? ?? ????? ???????.

?? ??

?? ????? ??? ?? ??? ??? ????? ??? ?????. ? ??? ??? ?? ?? ??? ???? "???"? "???"? ????? ?????. ?? ??? ?????? ????? ??? ??? ??? ??? ? ????. ?? ??? ??? ???? ? ?? ????? ? ??, ?? ??, ??? ?? ??? ?? ??? ????? ??? ?????.

? ?? ??? ???? ????? ??? ??? ?? ?? ????? ???? ????.

use_image= img[0:600,700:1300]
gray = cv2.cvtColor(use_image, cv2.COLOR_BGR2GRAY)
gray_og = gray.copy()
gray = cv2.equalizeHist(gray)
gray = cv2.GaussianBlur(gray, (9, 9),1)

plt.figure(figsize=(15, 5))
plt.subplot(121), plt.imshow(gray, cmap='gray')
plt.title('Grayscale Image')
plt.subplot(122)
_= plt.hist(gray.ravel(), 256, [0,256],label="Equalized") 
_ = plt.hist(gray_og.ravel(), 256, [0,256],label="Original",histtype='step')
plt.legend()
plt.title('Grayscale Histogram')

??? ??? ??? ?? ?? ??? ?? ???? ? ????.

# Edges to contours 
contours, hierarchy = cv2.findContours(edges, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
# Calculate contour areas
areas = [cv2.contourArea(contour) for contour in contours]

# Normalize areas for the colormap
normalized_areas = np.array(areas)
if normalized_areas.max() > 0:
    normalized_areas = normalized_areas / normalized_areas.max()

# Create a colormap
cmap = plt.cm.jet

# Plot the contours with the color map
plt.figure(figsize=(10, 10))
plt.subplot(1,2,1)
plt.imshow(gray, cmap='gray', alpha=0.5)  # Display the grayscale image in the background
mask = np.zeros_like(use_image)
for contour, norm_area in zip(contours, normalized_areas):
    color = cmap(norm_area)  # Map the normalized area to a color
    color = [int(c*255) for c in color[:3]]
    cv2.drawContours(mask, [contour], -1, color,-1 )  # Draw contours on the image

plt.subplot(1,2,2)

????? ??? ??? ????? ???? ?? ??? ??? ??? ????. ? ???? ?? ??? ???? ??????. ??? ????? ??? ??? ??, ??? ??, ???? ??? ?? ??? ??? ?? ?????.

????? ??? ?? ??? ?? ??? ?? ?? ???? ???? ??? ?? ??? ? ????. ?? ?? ??? ????.

# Kmean color segmentation
use_image= img[0:600,700:1300]
#use_image = cv2.medianBlur(use_image, 15)


 # Reshape image for k-means
pixel_values = use_image.reshape((-1, 3)) if len(use_image.shape) == 3 else use_image.reshape((-1, 1))
pixel_values = np.float32(pixel_values)

criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 10, 1.0)
K = 3
attempts=10
ret,label,center=cv2.kmeans(pixel_values,K,None,criteria,attempts,cv2.KMEANS_PP_CENTERS)

centers = np.uint8(center)
segmented_data = centers[label.flatten()]
segmented_image = segmented_data.reshape(use_image.shape)

plt.figure(figsize=(10, 6))
plt.subplot(1,2,1),plt.imshow(use_image[:,:,::-1])
plt.title("RGB View")
plt.subplot(1,2,2),plt.imshow(segmented_image[:,:,[2,1,0]])
plt.title(f"Kmean Segmented Image K={K}")

??? ??? ?? ? ?? ??? ???? ???? ??? ????? ???? ? ????.

?? ????? ??? ?? ??? ??? ??? ??? ??? ????? ?????. ??? ??? ???? ?? ?? ??? ?? ??? ???? ????? ??? ? ????. ??? ?? ??? ??? ???? ?? ? ???? ?? ????.

??

???? ??? ??? ?? ???, ??? ?? ??? ??? ???? ??? ? ?? ??? ?????. ? ??????? ?? ???? ??, K-?? ?????, ?? ??????? ? ?? ??? ?? ??? ????? ?? ?? ??????? ?? ???????. ???? ?? ?? ?? ???? ?? ?? ????? ? ?? ?? ??? ????? ??? ??? ?? ??? ???? OpenCV? ???? ?????.

?? ??? ??????? ? ?? ??? ???? ??? ?? ??? ??? ???. ?? ?? ??? ?? ??? ?? ???? ??????

? ??? [Python-CV??? ?? : Canny Edge, Watershed ? K-Means ??? ?? ?????. ??? ??? PHP ??? ????? ?? ?? ??? ?????!