Testing Mods to PythonForEngineers Book Source

README

I uploaded files modified from Shantnu Tiwari's Python For Engineers (PyEng) respository.
Specifically, the Image_Video subdirectory has sample programs, which I modified to use matplotlib.pyplot's imshow() insead of OpenCV's imshow().
This is not ideal, but got the programs further along in the absense of a working OpenCV on Ubuntu.
The next step was to avoid the GUI altogether and use OpenCV's imwrite() to save images to a file.  The pyplot examples are left in, but commented out for reference.

blur2.py

import cv2
import sys
import numpy as np
import matplotlib.pyplot as plt

# The first argument is the image
image = cv2.imread(sys.argv[1])

#conver to grayscale
gray_image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)

#blur it
blurred_image = cv2.GaussianBlur(image, (7,7), 0)

#
# Show all 3 images
#
# Here are some alternatives if displaying with cv2 isn't working
#
#cv2.imshow("Original Image", image)
#cv2.imshow("Gray Image", gray_image)
#cv2.imshow("Blurred Image", blurred_image)
#
# 1) Instead of showing the image with cv2, we will save it

cv2.imwrite('blur2_original.png', image)
cv2.imwrite('blur2_gray.png', gray_image)
cv2.imwrite('blur2_blurred.png', blurred_image)

# 2) Use pyplot to show the image.  Just uncomment these lines:
#
#plt.imshow(image)   # "Original Image" )
#plt.show()
#plt.imshow(gray_image)   # "Gray Image" )
#plt.show()
#plt.imshow(blurred_image)   # "Blurred Image" )
#plt.show()

# Uncomment this if showing via GUI
#cv2.waitKey(0)

count_cards2.py

import cv2
import sys
import numpy as np
import matplotlib.pyplot as plt

# Read the image
image = cv2.imread("cards.jpg")

#convert to grayscale
gray_image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)

#blur it
blurred_image = cv2.GaussianBlur(gray_image, (7,7), 0)

# Show both our images
#
# Here are some alternatives if displaying with cv2 isn't working
#
#cv2.imshow("Original image", image)
#cv2.imshow("Blurred image", blurred_image)
#
# 1) Instead of showing the image with cv2, we will save it

cv2.imwrite('count_cards2_original.png', image)
cv2.imwrite('count_cards2_blurred.png', blurred_image)

# 2) Use pyplot to show the image.  Just uncomment these lines:
#
#plt.imshow(image)
#plt.show()
#plt.imshow(blurred_image)
#plt.show()

# Run the Canny edge detector
canny = cv2.Canny(blurred_image, 30, 100)

#
#cv2.imshow("Canny", canny)
#
cv2.imwrite('count_cards2_canny.png', canny)
#
# OR
#
#plt.imshow(canny)
#plt.show()

im, contours, hierarchy= cv2.findContours(canny, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)

print("Number of objects found = ", len(contours))

cv2.drawContours(image, contours, -1, (0,255,0), 2)
#
#cv2.imshow("objects Found", image)
#
cv2.imwrite('count_cards2_objects.png', image)
#
# OR
#
#plt.imshow(image)
#plt.show()

# Uncomment this if showing via GUI
#cv2.waitKey(0)
#plt.clf()

display2.py

import cv2
import sys
import numpy as np
import matplotlib.pyplot as plt

# Read the image. The first command line argument is the image
image = cv2.imread(sys.argv[1])

# Here are some alternatives if displaying with cv2 isn't working
#
#cv2.imshow("Image", image)
#
# 1) Instead of showing the image with cv2, we will save it

cv2.imwrite('display2.png', image)

# 2) Use pyplot to show the image.  Just uncomment these lines:
#
#plt.imshow(image)
#plt.show()


# Uncomment this if showing via GUI
#cv2.waitKey(0)

edge_detect2.py

import cv2
import sys
import numpy as np
import matplotlib.pyplot as plt

# The first argument is the image
image = cv2.imread(sys.argv[1])

#convert to grayscale
gray_image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)

#blur it
blurred_image = cv2.GaussianBlur(gray_image, (7,7), 0)

# Here are some alternatives if displaying with cv2 isn't working
#
#cv2.imshow("Original Image", image)
#
# 1) Instead of showing the image with cv2, we will save it

cv2.imwrite('edge_detect2_original.png', image)

# 2) Use pyplot to show the image.  Just uncomment these lines:
#
#plt.imshow(image)
#plt.show()

# Use low thresholds
canny = cv2.Canny(blurred_image, 10, 30)
#
#cv2.imshow("Canny with low thresholds", canny)
#
cv2.imwrite('edge_detect2_canny.png', canny)
#
# OR
#
#plt.imshow(canny)
#plt.show()

# Use high thresholds
canny2 = cv2.Canny(blurred_image, 50, 150)
#
#cv2.imshow("Canny with high thresholds", canny2)
#
cv2.imwrite('edge_detect2_canny2.png', canny2)
#
# OR
#
#plt.imshow(canny2)
#plt.show()

# Uncomment this if showing via GUI
#cv2.waitKey(0)

face_detect2.py

#!/usr/bin/python

import sys
import cv2
import numpy as np
import matplotlib.pyplot as plt


imgpath = sys.argv[1]
cascasdepath = "haarcascade_frontalface_default.xml"

image = cv2.imread(imgpath)
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)

face_cascade = cv2.CascadeClassifier(cascasdepath)

faces = face_cascade.detectMultiScale(
    gray,
    scaleFactor = 1.2,
    minNeighbors = 5,
    minSize = (30,30)

    )

print("The number of faces found = ", len(faces))

for (x,y,w,h) in faces:
    cv2.rectangle(image, (x,y), (x+h, y+h), (0, 255, 0), 2)

# Here are some alternatives if displaying with cv2 isn't working
#
#cv2.imshow("Faces found", image)    
#
# 1) Instead of showing the image with cv2, we will save it

cv2.imwrite('face_detect2.png', image)

# 2) Use pyplot to show the image.  Just uncomment these lines:
#
#plt.imshow(image)
#plt.show()

# Uncomment this if showing via GUI
#cv2.waitKey(0)

webcam_face_detect2.py

#!/usr/bin/python

import sys
import cv2
import numpy as np
import matplotlib.pyplot as plt


cascasdepath = "haarcascade_frontalface_default.xml"

if len(sys.argv) < 2:
    video_capture = cv2.VideoCapture(0)
else:
    video_capture = cv2.VideoCapture(sys.argv[1])

while True:
    ret, image = video_capture.read()

    if not ret:
        break

    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)

    face_cascade = cv2.CascadeClassifier(cascasdepath)

    faces = face_cascade.detectMultiScale(
        gray,
        scaleFactor = 1.2,
        minNeighbors = 5,
        minSize = (30,30)

        )

    #print("The number of faces found = ", len(faces))

    for (x,y,w,h) in faces:
        cv2.rectangle(image, (x,y), (x+h, y+h), (0, 255, 0), 2)

    # Here are some alternatives if displaying with cv2 isn't working
    #
    #cv2.imshow("Faces found", image)    
    #
    # 1) Instead of showing the image with cv2, we will save it

    cv2.imwrite('webcam_face_detect2.png', image)

    # 2) Use pyplot to show the image.  Just uncomment these lines:
    #
    #plt.imshow(image)
    #plt.show()

    if cv2.waitKey(1) & 0xFF == ord('q'):
        break

video_capture.release()
# cv2.destroyAllWindows()