After-practise

Section #2 - Advanced/bitwise.py → Advanced/bitwise.py

@@ -3,27 +3,27 @@
 import cv2 as cv
 import numpy as np
 
-blank = np.zeros((400,400), dtype='uint8')
+blank = np.zeros((400,400), dtype='uint8')#dtype='uint8' is a grayscale image
 
 rectangle = cv.rectangle(blank.copy(), (30,30), (370,370), 255, -1)
 circle = cv.circle(blank.copy(), (200,200), 200, 255, -1)
 
 cv.imshow('Rectangle', rectangle)
 cv.imshow('Circle', circle)
 
-# bitwise AND --> intersecting regions
+# bitwise AND --> intersecting regions       (Appartient au cercle ET au rectangle)
 bitwise_and = cv.bitwise_and(rectangle, circle)
 cv.imshow('Bitwise AND', bitwise_and)
 
 # bitwise OR --> non-intersecting and intersecting regions
-bitwise_or = cv.bitwise_or(rectangle, circle)
+bitwise_or = cv.bitwise_or(circle, rectangle)
 cv.imshow('Bitwise OR', bitwise_or)
 
 # bitwise XOR --> non-intersecting regions
 bitwise_xor = cv.bitwise_xor(rectangle, circle)
 cv.imshow('Bitwise XOR', bitwise_xor)
 
-# bitwise NOT
+# # bitwise NOT
 bitwise_not = cv.bitwise_not(circle)
 cv.imshow('Circle NOT', bitwise_not)
 

Section #2 - Advanced/colour_spaces.py → Advanced/colour_spaces.py

@@ -6,8 +6,8 @@
 img = cv.imread('../Resources/Photos/park.jpg')
 cv.imshow('Park', img)
 
-# plt.imshow(img)
-# plt.show()
+plt.imshow(img)
+plt.show()
 
 # BGR to Grayscale
 gray = cv.cvtColor(img, cv.COLOR_BGR2GRAY)

Section #2 - Advanced/histogram.py → Advanced/histogram.py

@@ -9,8 +9,8 @@
 
 blank = np.zeros(img.shape[:2], dtype='uint8')
 
-# gray = cv.cvtColor(img, cv.COLOR_BGR2GRAY)
-# cv.imshow('Gray', gray)
+gray = cv.cvtColor(img, cv.COLOR_BGR2GRAY)
+cv.imshow('Gray', gray)
 
 mask = cv.circle(blank, (img.shape[1]//2,img.shape[0]//2), 100, 255, -1)
 
@@ -30,16 +30,16 @@
 
 # Colour Histogram
 
-plt.figure()
-plt.title('Colour Histogram')
-plt.xlabel('Bins')
-plt.ylabel('# of pixels')
-colors = ('b', 'g', 'r')
-for i,col in enumerate(colors):
-    hist = cv.calcHist([img], [i], mask, [256], [0,256])
-    plt.plot(hist, color=col)
-    plt.xlim([0,256])
-
-plt.show()
+# plt.figure()
+# plt.title('Colour Histogram')
+# plt.xlabel('Bins')
+# plt.ylabel('# of pixels')
+# colors = ('b', 'g', 'r')
+# for i,col in enumerate(colors):
+#     hist = cv.calcHist([img], [i], mask, [256], [0,256])
+#     plt.plot(hist, color=col)
+#     plt.xlim([0,256])
+
+# plt.show()
 
 cv.waitKey(0)

Section #2 - Advanced/splitmerge.py → Advanced/splitmerge.py

@@ -2,14 +2,20 @@
 
 import cv2 as cv
 import numpy as np
+import matplotlib.pyplot as plt
+
 
 img = cv.imread('../Resources/Photos/park.jpg')
 cv.imshow('Park', img)
 
-blank = np.zeros(img.shape[:2], dtype='uint8')
+# plt.imshow(img)
+# plt.show()
 
+blank = np.zeros(img.shape[:2], dtype='uint8')
 b,g,r = cv.split(img)
 
+# cv.imshow('Blue', b)
+
 blue = cv.merge([b,blank,blank])
 green = cv.merge([blank,g,blank])
 red = cv.merge([blank,blank,r])

Section #1 - Basics/basic_functions.py → Basics/basic_functions.py

@@ -2,34 +2,38 @@
 
 import cv2 as cv
 
-# Read in an image
+# # Read in an image
 img = cv.imread('../Resources/Photos/park.jpg')
 cv.imshow('Park', img)
 
 # Converting to grayscale
 gray = cv.cvtColor(img, cv.COLOR_BGR2GRAY)
 cv.imshow('Gray', gray)
 
-# Blur 
+
+
+# Blur  an image (Gaussian Blur)
 blur = cv.GaussianBlur(img, (7,7), cv.BORDER_DEFAULT)
 cv.imshow('Blur', blur)
 
-# Edge Cascade
+# Edge Cascade  #Just like a filter in old phone cameras XD
 canny = cv.Canny(blur, 125, 175)
 cv.imshow('Canny Edges', canny)
 
-# Dilating the image
-dilated = cv.dilate(canny, (7,7), iterations=3)
+# Dilating the image 
+dilated = cv.dilate(canny, (5,5), iterations=3)
 cv.imshow('Dilated', dilated)
 
 # Eroding
 eroded = cv.erode(dilated, (7,7), iterations=3)
 cv.imshow('Eroded', eroded)
 
-# Resize
+# # Resize
 resized = cv.resize(img, (500,500), interpolation=cv.INTER_CUBIC)
 cv.imshow('Resized', resized)
 
-# Cropping
-cropped = img[50:200, 200:400]
-cv.imshow('Cropped', cropped)
+# # Cropping
+cropped = resized[100:250,100:150] 
+cv.imshow('Cropped', cropped)
+
+cv.waitKey(0)

Section #1 - Basics/contours.py → Basics/contours.py

@@ -18,13 +18,15 @@
 canny = cv.Canny(blur, 125, 175)
 cv.imshow('Canny Edges', canny)
 
-# ret, thresh = cv.threshold(gray, 125, 255, cv.THRESH_BINARY)
-# cv.imshow('Thresh', thresh)
+# # ret, thresh = cv.threshold(gray, 125, 255, cv.THRESH_BINARY)
+# # cv.imshow('Thresh', thresh)
 
 contours, hierarchies = cv.findContours(canny, cv.RETR_LIST, cv.CHAIN_APPROX_SIMPLE)
 print(f'{len(contours)} contour(s) found!')
 
-cv.drawContours(blank, contours, -1, (0,0,255), 1)
+
+# (Blue, Green, Red)
+cv.drawContours(blank, contours, -1, (100,0,0), 1)
 cv.imshow('Contours Drawn', blank)
 
 cv.waitKey(0)

Section #1 - Basics/draw.py → Basics/draw.py

@@ -7,19 +7,21 @@
 cv.imshow('Blank', blank)
 
 # 1. Paint the image a certain colour
-blank[200:300, 300:400] = 0,0,255
+blank[200:300, 300:400]  = 0,0,255
 cv.imshow('Green', blank)
 
+#Blank.shape = (500,500,3)
+
 # 2. Draw a Rectangle
-cv.rectangle(blank, (0,0), (blank.shape[1]//2, blank.shape[0]//2), (0,255,0), thickness=-1)
-cv.imshow('Rectangle', blank)
+cv.rectangle(blank, (0,0), (blank.shape[1]//2, blank.shape[0]//2), (0,255,0), thickness=-1)#thickness=-1 will fill the rectangle
+cv.imshow('Rectangle', blank)#(0,0) is the top left corner, (blank.shape[1]//2, blank.shape[0]//2) is the bottom right corner
 
 # 3. Draw A circle
-cv.circle(blank, (blank.shape[1]//2, blank.shape[0]//2), 40, (0,0,255), thickness=-1)
+cv.circle(blank, (blank.shape[1]//2, blank.shape[0]//2), 40 , (0,0,255), thickness=-1)
 cv.imshow('Circle', blank)
 
 # 4. Draw a line
-cv.line(blank, (100,250), (300,400), (255,255,255), thickness=3)
+cv.line(blank, (100,250), (300,400), (255,255,255), thickness=1)
 cv.imshow('Line', blank)
 
 # 5. Write text

Section #1 - Basics/transformations.py → Basics/transformations.py

@@ -8,7 +8,7 @@
 
 # Translation
 def translate(img, x, y):
-    transMat = np.float32([[1,0,x],[0,1,y]])
+    transMat = np.float32([[1,0,x],[0,1,y]]) 
     dimensions = (img.shape[1], img.shape[0])
     return cv.warpAffine(img, transMat, dimensions)
 
@@ -17,22 +17,22 @@ def translate(img, x, y):
 # x --> Right
 # y --> Down
 
-translated = translate(img, -100, 100)
+translated = translate(img, 150, 100)
 cv.imshow('Translated', translated)
 
 # Rotation
-def rotate(img, angle, rotPoint=None):
+def rotate(img, angle, rotPoint=None): #rotPoint is the point around which the image will be rotated
     (height,width) = img.shape[:2]
 
     if rotPoint is None:
         rotPoint = (width//2,height//2)
 
-    rotMat = cv.getRotationMatrix2D(rotPoint, angle, 1.0)
+    rotMat = cv.getRotationMatrix2D(rotPoint, angle, 1.0) #1.0 is the scale
     dimensions = (width,height)
 
     return cv.warpAffine(img, rotMat, dimensions)
 
-rotated = rotate(img, -45)
+rotated = rotate(img, 180)
 cv.imshow('Rotated', rotated)
 
 rotated_rotated = rotate(img, -90)
@@ -43,7 +43,7 @@ def rotate(img, angle, rotPoint=None):
 cv.imshow('Resized', resized)
 
 # Flipping
-flip = cv.flip(img, -1)
+flip = cv.flip(img, -1)#0 is vertical flip, 1 is horizontal flip, -1 is both
 cv.imshow('Flip', flip)
 
 # Cropping

Section #3 - Faces/face_detect.py → Faces/face_detect.py

@@ -3,20 +3,22 @@
 import cv2 as cv
 
 img = cv.imread('../Resources/Photos/group 1.jpg')
-cv.imshow('Group of 5 people', img)
+#cv.imshow('Group of 5 people', img)
 
-gray = cv.cvtColor(img, cv.COLOR_BGR2GRAY)
-cv.imshow('Gray People', gray)
+# gray = cv.cvtColor(img, cv.COLOR_BGR2GRAY)
+# cv.imshow('Gray People', gray)
 
 haar_cascade = cv.CascadeClassifier('haar_face.xml')
 
-faces_rect = haar_cascade.detectMultiScale(gray, scaleFactor=1.1, minNeighbors=1)
+faces_rect = haar_cascade.detectMultiScale(img, scaleFactor=1.1, minNeighbors=1)
 
 print(f'Number of faces found = {len(faces_rect)}')
 
 for (x,y,w,h) in faces_rect:
     cv.rectangle(img, (x,y), (x+w,y+h), (0,255,0), thickness=2)
 
+img = cv.resize(img, (int(img.shape[1]/2), int(img.shape[0]/2)))
+
 cv.imshow('Detected Faces', img)