created solutions to img_avg.py

Author: abhatia1205

1_beginner/chapter5/practice/prime.py

@@ -14,15 +14,14 @@
 numer = int(input("Enter number here: "))
 
 """
-See if you can do the above problem without looping up until the number.
-For example, if the number you are checking is 101, don't write the code
-for i in range(101), or even for i in range(100). You may loop until 
-half of the number.
+Given a number less than or equal to 10 billion, see if you can check if it is
+prime in UNDER 2 SECONDS. The code you wrote above probably wont do that, so
+you will have to figure out a clever solution.
 
-NOTE: THE ABOVE IS A CHALLENGE PROBLEM, AND IS EXTRREEMMLY HARD TO DO
-OPTIMALLY. I COULDN'T DO IT OPTIMALY UNTIL I LEARNED HOW TO. If you can't
+NOTE: THE ABOVE IS A CHALLENGE PROBLEM, AND IS EXTRREEMMLY HARD TO DO.
+I COULDN'T DO IT OPTIMALY UNTIL I LEARNED HOW TO. If you can't
 figure out the solution, don't feel discouraged, its seriously a really
-hard problem.
+hard problem (I have seen this asked to college students :O)
 
 """
 

2_intermediate/chapter10/practice/img_avg.py

@@ -0,0 +1,46 @@
+""" Here is the challenge problem for 2d loops>
+Images are often represented as 3d arrays,
+where the rows and columns are the pixels in the image,
+and each pixel has an r, g, and b value.
+
+The interesting thing is that we can iterate over images.
+The challenge is, given an image, create a program that
+will return a different image where each pixel is the average
+of the pixels surrounding it in the original image.
+
+The neighbors of an image are all the pixels that surroun it,
+1 on each side, and 4 on the diagonals, for 8 in total. 
+Each pixel doesn't necessarily have 8 neighbors, though (think about why)
+
+The code to grab an image from the internet and make it
+into an array is given to you. The code also displays the new image
+you create in the end.
+
+NOTE: The image is 3 dimensional because each pixel has rgb values.
+To find the average value of all of a pixels neighbors, you must
+change the average of the red value to the red value, blue to blue, etc.
+For example, if the neighbors of a pixel with value [1,2,3]
+were [20,30,40] and [10,120,30], the new pixel that would replace the original one would be
+[15,75,35]
+"""
+
+from PIL import Image
+import requests
+import numpy
+import matplotlib.pyplot as plt
+
+url = "https://images.dog.ceo/breeds/waterdog-spanish/20180723_185544.jpg"
+img = numpy.array(Image.open(requests.get(url, stream=True).raw)).tolist()
+newimg = img
+transpose = numpy.transpose(img)
+
+plt.imshow(img)
+plt.show()
+
+#write code to create newimg here
+
+plt.imshow(newimg)
+plt.show()
+
+plt.imshow(transpose)
+plt.show()

2_intermediate/chapter10/solutions/img_avg.py

@@ -0,0 +1,90 @@
+""" Here is the challenge problem for 2d loops>
+Images are often represented as 3d arrays,
+where the rows and columns are the pixels in the image,
+and each pixel has an r, g, and b value.
+
+The interesting thing is that we can iterate over images.
+The challenge is, given an image, create a program that
+will return a different image where each pixel is the average
+of the pixels surrounding it in the original image.
+
+The neighbors of an image are all the pixels that surroun it,
+1 on each side, and 4 on the diagonals, for 8 in total. 
+Each pixel doesn't necessarily have 8 neighbors, though (think about why)
+
+The code to grab an image from the internet and make it
+into an array is given to you. The code also displays the new image
+you create in the end.
+
+NOTE: The image is 3 dimensional because each pixel has rgb values.
+To find the average value of all of a pixels neighbors, you must
+change the average of the red value to the red value, blue to blue, etc.
+For example, if the neighbors of a pixel with value [1,2,3]
+were [20,30,40] and [10,120,30], the new pixel that would replace the original one would be
+[15,75,35]
+"""
+
+from PIL import Image
+import requests
+import numpy
+import matplotlib.pyplot as plt
+
+url = "https://images.dog.ceo/breeds/waterdog-spanish/20180723_185544.jpg"
+img = numpy.array(Image.open(requests.get(url, stream=True).raw))
+newimg = img
+transpose = numpy.transpose(img)
+
+
+#write code to create newimg here
+def solution1():
+	"""Iterating over the image here. i is a variable from 0 to the width of the image.
+	j is a variable that ranges from 0 to the height of the image. i is associated with
+	values"""
+	for i in range(len(img)):
+		for j in range(len(img[0])):
+			x_n = [0]
+			y_n = [0]
+
+			if(i == 0):
+				x_n.append(1)
+			elif(i ==  len(img)-1):
+				x_n.append(-1)
+			else:
+				x_n.append(1)
+				x_n.append(-1)
+
+			if(j == 0):
+				y_n.append(1)
+			elif(j ==  len(img[0])-1):
+				y_n.append(-1)
+			else:
+				y_n.append(1)
+				y_n.append(-1)
+
+			r_avg = -1*img[i][j][0]
+			g_avg = -1*img[i][j][1]
+			b_avg = -1*img[i][j][2]
+			c = -1
+
+			for x in x_n:
+				for y in y_n:
+					r_avg += img[i+x][j+y][0]
+					g_avg += img[i+x][j+y][1]
+					b_avg += img[i+x][j+y][2]
+					c+=1
+			r_avg = r_avg/c
+			g_avg = g_avg/c
+			b_avg = b_avg/c
+
+			newimg[i][j] = [r_avg, g_avg, b_avg]
+
+
+
+
+solution1()
+
+plt.imshow(newimg)
+plt.show()
+
+plt.imshow(transpose)
+plt.show()