Initial commit

livepopeize.py

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+import cv2
+import sys
+from popeize import popeize
+
+# Get user supplied values
+try:
+    cascPath = sys.argv[1]
+except IndexError:
+    cascPath = 'features/haarcascade_frontalface_default.xml'
+
+video_capture = cv2.VideoCapture(0)
+
+# Create the haar cascade
+faceCascade = cv2.CascadeClassifier(cascPath)
+
+# Read the image
+papaj = cv2.imread("zoltamorda.png", -1)
+
+while True:
+    ret, image = video_capture.read()
+
+    popeize(faceCascade, image, papaj, 1.3)
+
+    cv2.imshow("Video", image)
+
+    if cv2.waitKey(1) & 0xFF == ord('q'):
+        break
+
+video_capture.release()
+cv2.destroyAllWindows()

popeize.py

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+import cv2
+import sys
+
+def blit(l_img, s_img, x_offset, y_offset):
+    for c in range(0, 3):
+        l_img[y_offset:y_offset+s_img.shape[0], x_offset:x_offset+s_img.shape[1], c] = s_img[:,:,c] * (s_img[:,:,3]/255.0) + l_img[y_offset:y_offset+s_img.shape[0], x_offset:x_offset+s_img.shape[1], c] * (1.0 - s_img[:,:,3]/255.0)
+
+def downscale(img, width, height):
+    factor = min(1.0*height/img.shape[0], 1.0*width/img.shape[1])
+    return cv2.resize(img, None, None, factor, factor)
+
+def popeize(faceCascade, image, papaj, faceScale=1.5):
+    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
+
+    # Detect faces in the image
+    faces = faceCascade.detectMultiScale(gray, faceScale, 5)
+
+    print "Found {0} faces!".format(len(faces))
+
+    # Draw a rectangle around the faces
+    for (x, y, w, h) in faces:
+        factor = min(1.0*h/papaj.shape[0], 1.0*w/papaj.shape[1]) * 1.3
+        scaledpapaj = cv2.resize(papaj, None, None, factor, factor)
+        dx = x + (w/2) - scaledpapaj.shape[1]/2
+        dy = y + (h/2) - scaledpapaj.shape[0]/2
+
+        print dx, dy, factor
+
+        try:
+            blit(image, scaledpapaj, dx, dy)
+        except Exception as e:
+            print e, dx, dy, factor
+        #cv2.rectangle(image, (x, y), (x+w, y+h), (0, 255, 0), 2)
+
+if __name__ == '__main__':
+    imagePath = sys.argv[1]
+    cascPath = sys.argv[2]
+
+    # Create the haar cascade
+    faceCascade = cv2.CascadeClassifier(cascPath)
+
+    # Read the image
+    image = cv2.imread(imagePath)
+    papaj = cv2.imread("zoltamorda.png", -1)
+
+    cv2.namedWindow("image")
+
+    def trackbarChanged(value):
+        image = cv2.imread(imagePath)
+        popeize(faceCascade, image, papaj, 1.1 + value/100.0)
+        cv2.imshow("image", downscale(image, 1024, 768))
+        print 'showed'
+
+    cv2.createTrackbar("scale", "image", 0, 300, trackbarChanged)
+
+    popeize(faceCascade, image, papaj, 1.1)
+    cv2.imwrite("output.jpg", image)
+    cv2.imshow("image", downscale(image, 1024, 768))
+
+    while cv2.waitKey(0) & 0xff != ord('q'):
+        pass

popeproxy.py

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+"""
+usage: mitmdump -T --anticache --host -s popeproxy.py
+"""
+
+from libmproxy.protocol.http import decoded
+import cv2
+import sys
+import os
+import numpy as np
+
+sys.path.insert(0, os.getcwd())
+
+import popeize
+
+papaj = cv2.imread("zoltamorda.png", -1)
+faceCascade = cv2.CascadeClassifier('features/haarcascade_frontalface_default.xml')
+
+types = {
+    'image/jpeg': '.jpg',
+    'image/png': '.png',
+    }
+
+
+def response(context, flow):
+    if 'content-type' not in flow.response.headers:
+        return
+
+    ctype = flow.response.headers['content-type'][0]
+
+    if ctype not in types:
+        return
+
+    with decoded(flow.response):
+        image = cv2.imdecode(np.fromstring(flow.response.content,
+                                           dtype=np.uint8), -1)
+        popeize.popeize(faceCascade, image, papaj, 1.1)
+        success, encimg = cv2.imencode(types[ctype], image)
+
+        if success:
+            flow.response.content = encimg.tostring()
+
+        print '*'*20, 'length', len(flow.reply.obj.response.content)
+        print '*'*20, 'type', ctype