opencv_detection_plusminus.py

import cv2
import sys
import os
from datetime import datetime
startTime = datetime.now()
plusminus = 60
#os.system('rm frames/*')
#os.system('ffmpeg -threads 0 -i ihop.mp4 -f image2 frames/%05d.png')
number_of_frames = len(os.listdir('frames'))
print number_of_frames
l = [[] for i in range(number_of_frames)]
j = 0
for i in range(1, number_of_frames+1):
    print j
    
    print i
    iteml = []
    filename = '%05d.png' % (i)
    print filename
    current = os.getcwd()
    # Get user supplied values
    imagePath = os.path.join(current, 'frames/'+filename)

    # Read the image
    image = cv2.imread(imagePath)
    result_image = image.copy()
    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
    for cascPath in [f for f in os.listdir('/home/azureuser/haartrainer/haarcascades') if "HS" in f or 'face' in f]:
        print cascPath
        cascPath = 'haarcascades/'+cascPath
        faceCascade = cv2.CascadeClassifier(cascPath)
        # Detect faces in the image
        faces = faceCascade.detectMultiScale(
            gray,
            scaleFactor=1.1,
            minNeighbors=5,
            minSize=(15, 15),
        )

        print "Found {0} faces!".format(len(faces))

        # Draw a rectangle around the faces
        for (x, y, w, h) in faces:
            iteml.append((x, y, w, h))
            #cv2.rectangle(image, (x, y), (x+w, y+h), (0, 255, 0), 2)
            #sub_face = image[y:y+h, x:x+w]
            # apply a gaussian blur on this new recangle image
            #sub_face = cv2.GaussianBlur(sub_face,(23, 23), 30)
            # merge this blurry rectangle to our final image
            #result_image[y:y+sub_face.shape[0], x:x+sub_face.shape[1]] = sub_face
        l[j].extend(iteml)
        if j < plusminus:
            for k in range(0,j):
                l[k].extend(iteml)
        else:
            for k in range(j-plusminus, j):
                l[k].extend(iteml)
        if j + plusminus < number_of_frames:
            for k in range(j+1, j+plusminus):
                l[k].extend(iteml)
             
        else:
            for k in range(j+1, number_of_frames):
                l[k].extend(iteml)
    #cv2.imwrite("detected.png", result_image)
    j += 1
j = 0
for i in range(1, number_of_frames+1):
    print j
    
    print i
    iteml = []
    filename = '%05d.png' % (i)
    current = os.getcwd()
    # Get user supplied values
    imagePath = os.path.join(current, 'frames/'+filename)

    # Read the image
    image = cv2.imread(imagePath)
    result_image = image.copy()
    
        # Draw a rectangle around the faces
    for (x, y, w, h) in l[j]:
        #iteml.append((x, y, w, h))
        #cv2.rectangle(image, (x, y), (x+w, y+h), (0, 255, 0), 2)
        sub_face = image[y:y+h, x:x+w]
        # apply a gaussian blur on this new recangle image
        sub_face = cv2.GaussianBlur(sub_face,(23, 23), 30)
        # merge this blurry rectangle to our final image
        result_image[y:y+sub_face.shape[0], x:x+sub_face.shape[1]] = sub_face

    cv2.imwrite("output/detected_"+filename, result_image)
    j += 1
os.system('ffmpeg -threads 0 -framerate 30/1 -i output/detected_%05d.png -c:v libx264 -r 30 -pix_fmt yuv420p out.mp4')
print datetime.now() - startTime