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--- face_detection:torch3vision [2009/05/26 10:55] – gerald
+++ face_detection:torch3vision [2024/02/29 13:36] (aktuell) – Externe Bearbeitung 127.0.0.1
@@ Zeile 2: / Zeile 2: @@
 {{tag>face_detecton gesichtererkennung}}
+jpegtopnm is obsolete under debian. Use djpeg instead
+<code>apt-get install libjpeg-progs</code>
 [[http://torch3vision.idiap.ch/]]
-Quelle((http://ultrahigh.org/2008/10/14/command-line-face-detection/))
+Quelle (grossteils) ((http://ultrahigh.org/2008/10/14/command-line-face-detection/))
-You will need the C++ Compiler 'g++' to make
+You will need the C++ Compiler 'g++' to make. When not compiling enter:
+<code>apt-get install g++</code>
   * Download Torch3Vision and un-tar it: tar -zxf Torch3vision2.1.tgz
@@ Zeile 15: / Zeile 19: @@
   * Build the vison examples for face detection: cd vision2.1/examples/facedetect/
  ../../../torch3make *.cc
+Jetzt 3 Programme in neuen Unterverzeichnis ''Linux_i686_dbg_float'':
+  * haarscan -  Schnelle, zuverlässige Erkennung
+  * mlpcascadescan - Sehr zuverlössige Erkennung, bei ganz kleinen Bildern geht es nicht richtig, grosse (>3MPixel) dauert lange.
+  * mlpscan - Seh keinen rechten Unterschied zum vorherigen.
+Jedes dieser 3 Programme braucht eine Modeldatei zur Gesichtererkennung. Diese 3 Dateien liegen unter ''vision2.1/examples/facedetect/models/''. Die jeweils passende Datei muss (mit Pfad) bei jeden Aufruf angegeben werden.
@@ Zeile 25: / Zeile 37: @@
 </code>
 This command takes about 20s to run on my creaky old laptop, and creates two files.
+[[http://torch3vision.idiap.ch/examples2.0/|Source code of examples]]
+Parmaters (as far as i found out):
+  * -minWsize n: min size n  in pixel to recognize a face
+  * -nbest n : save the best n results only
+  * -dir <string>                -> directory to store ouput files [.]
+  * -savepos                     -> save pos file
+  * -draw                        -> draw ppm image with detections
+  * -savejpg                     -> save in jpeg instead of jpeg (jpeg ist wrong, should be ppm, but not working)
+  * -verbose: tell a little bit more what you doing...
+The other file ‘andy.pos’ contains the results of face detection. Line one is the number of detections, then each line has format x y w h, very easy to parse.
+<code>
+ FACE_POS=`head -n 2 “andy.pos | tail -n 1`
+ FACE_X=`echo $FACE_POS | awk ‘{print $1}’`
+ FACE_W=`echo $FACE_POS | awk ‘{print $3}’`
+ FACE_CENTER=`echo $FACE_X + $FACE_W/2 | bc`
+</code>
+I played around with the step-factors in the x and y directions to shave a second or so off the face detection routine, the values I chose were 0.1 and 0.2 respectively (I don’t need any accuracy in the y direction really, since my use is to cut the face down the middle).
+Then, since these are portrait photographs, I can speed up face detection by setting a minimum size for the face. I experimented and one sixth of the total image width gave good results - any larger and the face detection would fail with a crash. Adding this constraint provides better than 10X speed up, since the algorithm doesn’t waste time searching for small faces.
+<code>
+WIDTH=`identify -format “%w” “andy.jpg”`
+ MIN_FACE_WIDTH=`echo $WIDTH / 6 | bc`
+</code>
+So now here’s the final face detection command
+<code>
+mlpcascadescan “$ppm” -dir /tmp/ -savepos -model $MODEL \
+ -minWsize $MIN_FACE_WIDTH -stepxfactor $STEPX -stepyfactor $STEPY
+</code>
+And finally, as promised, I’ll tell you how to blank-out one side of the face: of course, using Image Magick. Using the ‘chop’ or ‘crop’ commands didn’t work for this purpose, where I wanted the image to keep it’s dimensions but have one half just be white. So I decided to draw a white rectangle over half of the picture.  I apply the image manipulation to the original JPEG file, not the temporary PPM file that I used to detect the face position.
+<code>
+convert -fill white \
+ -draw “rectangle $FACE_CENTER,0 $WIDTH,$HEIGHT” \
+ “andy.jpg” “andy_half.jpg”
+</code>
+Whole script:
+<code bash>
+#!/bin/bash
+. ~/local/bin/die
+DRAW="-draw"
+STEPX="0.05"
+STEPY="0.25"
+MODEL="$HOME/local/models/mlp-cascade19x19-20-2-110"
+# Find the center of the face in the image. Crop the image along the center of
+# the face.
+# Requirements: 'torch3vision' http://torch3vision.idiap.ch/downloads.php
+which mlpcascadescan > /dev/null || \
+	die "Please install mlpcascadescan from torch3vision"
+[ -e "$MODEL" ] || die "Model file $MODEL not found, get it from torch3vision"
+FACE_CENTER=""
+# Find the center of the face. Takes a jpeg and finds the face.
+function find_center() {
+	jpeg="$1"
+	temp="/tmp/crop_center_face"
+	ppm="$temp.ppm"
+	pos="$temp.pos"
+	echo "Converting to ppm"
+	jpegtopnm $jpeg > $ppm
+	if [ -e "$pos" ]; then
+		rm "$pos"
+	fi
+    HEIGHT=`identify -format "%h" "$jpeg"`;
+    WIDTH=`identify -format "%w" "$jpeg"`;
+	MIN_FACE_WIDTH=`echo $WIDTH / 6 | bc`
+	echo "Beginning face detection, min-width=$MIN_FACE_WIDTH"
+	echo "mlpcascadescan "$ppm" -dir /tmp/ -savepos $DRAW -model $MODEL \
+		-minWsize $MIN_FACE_WIDTH -stepxfactor $STEPX -stepyfactor $STEPY"
+	mlpcascadescan "$ppm" -dir /tmp/ -savepos $DRAW -model $MODEL \
+		-minWsize $MIN_FACE_WIDTH -stepxfactor $STEPX -stepyfactor $STEPY
+	[ -e "$pos" ] || die "Face detection failed: $jpeg"
+	FACE_POS=`head -n 2 "$pos" | tail -n 1`
+	FACE_X=`echo $FACE_POS | awk '{print $1}'`
+	FACE_W=`echo $FACE_POS | awk '{print $3}'`
+	FACE_CENTER=`echo $FACE_X + $FACE_W/2 | bc`
+	echo "FACE_X: $FACE_X   FACE_W: $FACE_W   FACE_CENTER: $FACE_CENTER"
+}
+function crop_image() {
+	jpeg="$1"
+	ext=${jpeg##*.}
+    base=`basename "$jpeg" .$ext`
+    crop="$base""_crop.jpg"
+    HEIGHT=`identify -format "%h" "$jpeg"`;
+    WIDTH=`identify -format "%w" "$jpeg"`;
+	echo "Cropping $jpeg at $FACE_CENTER, saving to $crop"
+	convert -fill white -draw "rectangle $FACE_CENTER,0 $WIDTH,$HEIGHT" "$jpeg" "$crop"
+}
+for file in $@; do
+	echo "Processing file: $file"
+	find_center $file
+	crop_image $file
+done
+</code>

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face_detection/torch3vision.1243335324.txt.gz · Zuletzt geändert: 2024/02/29 13:36 (Externe Bearbeitung)

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