def is_skin_hair_block(self, block, block_type):
'''
Return true if the block (given by the argument 'block' which is the coordinate-tuple for the top-left corner)
is a skin/hair-block - it is if a majority (as per the threshold attribute) of the pixels in the block are
skin/hair colored. 'block_type' says whether we are testing for a skin block ('s') or a hair block ('h).
'''
# Your code
pyimage_object=PyImage(self.filename)
k=pyimage_object.size()
pixel_x=block[0]
pixel_y=block[1]
lst_skin=[]
lst_hair=[]
temp_x=0
while(pixel_x<k[0]):
temp_y=0
while(pixel_y<k[1] and temp_y<self.block_size):
rgb_a=pyimage_object.get_rgba(pixel_x,pixel_y)
pixel_color=Color(rgb_a)
if(self.is_skin(pixel_color)):
lst_skin.append((pixel_x,pixel_y))
elif(self.is_hair(pixel_color)):
lst_hair.append((pixel_x,pixel_y))
pixel_y+=1
temp_y+=1
pixel_x+=1
temp_x+=1
if(len(lst_skin)>self.majority*self.block_size and block_type=='s'):
return True
elif(len(lst_hair)>self.majority*self.block_size and block_type=='h'):
return True
else:
return False
def add_neighbour_blocks(self, block, graph):
'''
Given a block (given by the argument 'block' which is the coordinate-tuple for the top-left corner)
and a graph (could be a hair or a skin graph), add edges from the current block to its neighbours
on the image that are already nodes of the graph
Check blocks to the left, top-left and top of the current block and if any of these blocks is in the
graph (means the neighbour is also of the same type - skin or hair) add an edge from the current block
to the neighbour.
'''
# Your code
pyimage_object=PyImage(self.filename)
k=pyimage_object.size()
if(self.is_skin_hair_block(block, 's')):
if((block[0]-self.block_size)<k[0] and (block[0]-self.block_size)>=0 and self.is_skin_hair_block((block[0]-self.block_size,block[1]), 's')):
graph.add_edge(block, (block[0]-self.block_size,block[1]))
if((block[1]-self.block_size)<k[1] and (block[1]-self.block_size)>=0 and self.is_skin_hair_block((block[0],block[1]-self.block_size), 's')):
graph.add_edge(block, (block[0],block[1]-self.block_size))
if((block[1]-self.block_size)<k[1] and (block[0]-self.block_size)<k[0] and (block[1]-self.block_size)>=0 and (block[0]-self.block_size)>=0 and self.is_skin_hair_block((block[0],block[1]-self.block_size), 's')):
graph.add_edge(block, (block[0]-self.block_size,block[1]-self.block_size))
if((block[1]-self.block_size)<k[1] and (block[0]+self.block_size)<k[0] and (block[1]-self.block_size)>=0 and (block[0]+self.block_size)>=0 and self.is_skin_hair_block((block[0],block[1]-self.block_size), 's')):
graph.add_edge(block, (block[0]+self.block_size,block[1]-self.block_size))
if(self.is_skin_hair_block(block, 'h')):
if((block[0]-self.block_size)<k[0] and (block[0]-self.block_size)>=0 and self.is_skin_hair_block((block[0]-self.block_size,block[1]), 'h')):
graph.add_edge(block, (block[0]-self.block_size,block[1]))
if((block[1]-self.block_size)<k[1] and (block[1]-self.block_size)>=0 and self.is_skin_hair_block((block[0],block[1]-self.block_size), 'h')):
graph.add_edge(block, (block[0],block[1]-self.block_size))
if((block[1]-self.block_size)<k[1] and (block[0]-self.block_size)<k[0] and (block[1]-self.block_size)>=0 and (block[0]-self.block_size)>=0 and self.is_skin_hair_block((block[0],block[1]-self.block_size), 'h')):
graph.add_edge(block, (block[0]-self.block_size,block[1]-self.block_size))
if((block[1]-self.block_size)<k[1] and (block[0]+self.block_size)<k[0] and (block[1]-self.block_size)>=0 and (block[0]+self.block_size)>=0 and self.is_skin_hair_block((block[0],block[1]-self.block_size), 'h')):
graph.add_edge(block, (block[0]+self.block_size,block[1]-self.block_size))
def make_block_graph(self):
'''
Return the skin and hair graphs - nodes are the skin/hair blocks respectively
Initialize skin and hair graphs. For every block if it is a skin(hair) block
add edges to its neighbour skin(hair) blocks in the corresponding graph
For this to work the blocks have to be traversed in the top->bottom, left->right order
'''
# Your code
graph_object_skin=Graph(None)
graph_object_hair=Graph(None)
pyimage_object=PyImage(self.filename)
k=pyimage_object.size()
i=0
while(i<k[0]):
# 3
j=0
while(j<k[1]):
# 4
if(self.is_skin_hair_block((i,j),'s')):
graph_object_skin.add_node((i,j))
self.add_neighbour_blocks((i,j),graph_object_skin)
j+=self.block_size
i+=self.block_size
i=0
while(i<k[0]):
# 5
j=0
while(j<k[1]):
# 6
if(self.is_skin_hair_block((i,j),'h')):
graph_object_hair.add_node((i,j))
self.add_neighbour_blocks((i,j),graph_object_hair)
j+=self.block_size
i+=self.block_size
return graph_object_skin, graph_object_hair
class FaceDetector(object):
'''
classdocs
'''
def __init__(self,
filename,
block_size=11,
min_component_size=11,
majority=0.56):
'''
Constructor - keeps input image filename, image read from the file as a PyImage object, block size (in pixels),
threshold to decide how many skin color pixels are required to declare a block as a skin-block
and min number of blocks required for a component. The majority argument says what fraction of
the block pixels must be skin/hair colored for the block to be a skin/hair block - the default value is
0.5 (half).
'''
self.image = PyImage(filename)
self.block_sz = block_size
self.min_blocks = min_component_size
self.fraction_pixel = majority
def skin_green_limits(self, red):
'''
Return the limits of normalized green given the normalized red component as a tuple (min, max)
'''
return ((-0.776 * red * red + 0.5601 * red + 0.18),
(-1.376 * red * red + 1.0743 * red + 0.2))
def is_skin(self, pixel_color):
'''
Given the pixel color (as a Color object) return True if it represents the skin color
Color is skin if hue in degrees is (> 240 or less than or equal to 20) and
green is in the green limits and it is not white
'''
hue = pixel_color.hue_degrees()
abs_rgb = pixel_color.rgb_abs()
green = pixel_color.g
green_limits = self.skin_green_limits(pixel_color.r)
if ((hue > 240 or hue <= 20)
and (green > green_limits[0] and green < green_limits[1])
and abs_rgb != (255, 255, 255)):
return True
def is_hair(self, pixel_color):
'''
Return True if the pixel color represents hair - it is if intensity < 80 and ((B-G)<15 or (B-R)<15 or
hue is between 20 and 40)
'''
hue = pixel_color.hue_degrees()
intensity = pixel_color.intensity
abs_rgb = pixel_color.rgb_abs()
if (intensity < 80 and (abs_rgb[2] - abs_rgb[1] < 15)
or (abs_rgb[2] - abs_rgb[0] < 15) or (hue > 20 and hue < 40)):
return True
def is_skin_hair_block(self, block, block_type):
'''
Return true if the block (given by the argument 'block' which is the coordinate-tuple for the top-left corner)
is a skin/hair-block - it is if a majority (as per the threshold attribute) of the pixels in the block are
skin/hair colored. 'block_type' says whether we are testing for a skin block ('s') or a hair block ('h).
'''
total_pixels = 0
if block_type == 's':
num_pixels = 0
for width in range(block[0], block[0] + self.block_sz):
for height in range(block[1], block[1] + self.block_sz):
if 0 <= width < self.image.size(
)[0] and 0 <= height < self.image.size()[1]:
total_pixels += 1
pixel_cl = Color(self.image.get_rgba(width, height))
if self.is_skin(pixel_cl):
num_pixels += 1
else:
num_pixels = 0
for width in range(block[0], block[0] + self.block_sz):
for height in range(block[1], block[1] + self.block_sz):
if 0 <= width < self.image.size(
)[0] and 0 <= height < self.image.size()[1]:
total_pixels += 1
pixel_cl = Color(self.image.get_rgba(width, height))
if self.is_hair(pixel_cl):
num_pixels += 1
if (float(num_pixels) / total_pixels >= self.fraction_pixel):
return True
def add_neighbour_blocks(self, block, graph):
'''
Given a block (given by the argument 'block' which is the coordinate-tuple for the top-left corner)
and a graph (could be a hair or a skin graph), add edges from the current block to its neighbours
on the image that are already nodes of the graph
Check blocks to the left, top-left and top of the current block and if any of these blocks is in the
graph (means the neighbour is also of the same type - skin or hair) add an edge from the current block
to the neighbour.
'''
left_x = block[0] - self.block_sz
left_y = block[1]
topleft_x = block[0] - self.block_sz
topleft_y = block[1] - self.block_sz
top_x = block[0]
top_y = block[1] - self.block_sz
topright_x = block[0] + self.block_sz
topright_y = block[1] - self.block_sz
for (block_x, block_y) in ((left_x, left_y), (topleft_x, topleft_y),
(top_x, top_y), (topright_x, topright_y)):
if 0 <= block_x < self.image.size(
)[0] and 0 <= block_y < self.image.size()[1]:
block2 = (block_x, block_y)
graph.add_edge(block, block2)
def make_block_graph(self):
'''
Return the skin and hair graphs - nodes are the skin/hair blocks respectively
Initialize skin and hair graphs. For every block if it is a skin(hair) block
add edges to its neighbour skin(hair) blocks in the corresponding graph
For this to work the blocks have to be traversed in the top->bottom, left->right order
'''
skin_graph = Graph()
hair_graph = Graph()
for x_cordinate in range(0, self.image.size()[0] - 1, self.block_sz):
for y_cordinate in range(0,
self.image.size()[1] - 1, self.block_sz):
if self.is_skin_hair_block((x_cordinate, y_cordinate), 's'):
skin_graph.add_node((x_cordinate, y_cordinate))
self.add_neighbour_blocks((x_cordinate, y_cordinate),
skin_graph)
if self.is_skin_hair_block((x_cordinate, y_cordinate), 'h'):
hair_graph.add_node((x_cordinate, y_cordinate))
self.add_neighbour_blocks((x_cordinate, y_cordinate),
hair_graph)
return skin_graph, hair_graph
def find_bounding_box(self, component):
'''
Return the bounding box - a box is a pair of tuples - ((minx, miny), (maxx, maxy)) for the component
Argument 'component' - is just the list of blocks in that component where each block is represented by the
coordinates of its top-left pixel.
'''
minx = component[0][0]
miny = component[0][1]
maxx = component[0][0]
maxy = component[0][1]
for i in range(0, len(component), 1):
if component[i][0] < minx:
minx = component[i][0]
if component[i][1] < miny:
miny = component[i][1]
if component[i][0] > maxx:
maxx = component[i][0]
if component[i][1] > maxy:
maxy = component[i][1]
return ((minx, miny), (maxx, maxy))
def skin_hair_match(self, skin_box, hair_box):
'''
Return True if the skin-box and hair-box given are matching according to one of the pre-defined patterns
'''
if skin_box[0][0] in range(hair_box[0][0], hair_box[1][0]):
if skin_box[1][0] in range(hair_box[0][0], hair_box[1][0]):
if skin_box[0][1] in range(hair_box[0][1], hair_box[1][1]):
return True #pattern 1,2,3,4,7,8,12,13
if skin_box[0][1] in range(hair_box[0][1], hair_box[1][1]):
if skin_box[0][0] in range(hair_box[0][0], hair_box[1][0]):
return True #pattern 6
elif skin_box[1][0] in range(hair_box[0][0], hair_box[1][0]):
return True #pattern 5
if hair_box[0][1] in range(skin_box[0][1], skin_box[1][1]):
if hair_box[1][1] in range(skin_box[0][1], skin_box[1][1]):
if hair_box[1][0] in range(skin_box[0][0], skin_box[1][0]):
return True
elif hair_box[0][0] in range(skin_box[0][0], skin_box[1][0]):
return True # 9, 10, 11
def detect_faces(self):
'''
Main method - to detect faces in the image that this class was initialized with
Return list of face boxes - a box is a pair of tuples - ((minx, miny), (maxx, maxy))
Algo: (i) Make block graph (ii) get the connected components of the graph (iii) filter the connected components
(iv) find bounding box for each component (v) Look for matches between face and hair bounding boxes
Return the list of face boxes that have matching hair boxes
'''
skin_graph, hair_graph = self.make_block_graph()
skin_comp1 = skin_graph.get_connected_components()
hair_comp1 = hair_graph.get_connected_components()
skin_comp = []
hair_comp = []
list1 = len(skin_comp1)
for i in range(0, list1):
if len(skin_comp1[i]) > self.min_blocks:
skin_comp += [skin_comp1[i]]
list1 -= 1
list1 = len(hair_comp1)
for i in range(0, list1):
if len(skin_comp1[i]) > self.min_blocks:
hair_comp += [hair_comp1[i]]
list1 -= 1
face_list = []
for skin in skin_comp:
for hair in hair_comp:
skin_box = self.find_bounding_box(skin)
hair_box = self.find_bounding_box(hair)
if self.skin_hair_match(skin_box, hair_box):
face_list.append(skin_box)
return face_list
def mark_box(self, box, color):
'''
Mark the box (same as in the above methods) with a given color (given as a raw triple)
This is just a one-pixel wide line showing the box.
'''
for i in range(box[0][0], box[1][0]):
self.image.set(i, box[0][1], color)
self.image.set(i, box[1][1], color)
for i in range(box[0][1], box[1][1]):
self.image.set(box[0][0], i, color)
self.image.set(box[1][0], i, color)
def mark_faces(self, marked_file):
'''
Detect faces and mark each face detected -- mark the bounding box of each face in red
and save the marked image in a new file
'''
face_list = self.detect_faces()
for i in face_list:
self.mark_box(i, (255, 0, 0))
self.image.save(marked_file)
class FaceDetector(object):
'''
classdocs
'''
def __init__(self, filename, block_size = 5,
min_component_size = 10, majority = 0.5):
'''
Constructor - keeps input image filename, image read from the file as a PyImage object, block size (in pixels),
threshold to decide how many skin color pixels are required to declare a block as a skin-block
and min number of blocks required for a component. The majority argument says what fraction of
the block pixels must be skin/hair colored for the block to be a skin/hair block - the default value is
0.5 (half).
'''
self.input_image = PyImage(filename)
self.block_size = block_size
self.threshold = block_size * block_size * majority
self.component_length = min_component_size
self.majority = majority
def skin_green_limits(self, red):
'''
Return the limits of normalized green given the normalized red component as a tuple (min, max)
'''
return ((-0.776*red.norm_r*red.norm_r + 0.5601*red.norm_r + 0.18),
(-1.376*red.norm_r*red.norm_r + 1.0743*red.norm_r + 0.2))
def is_skin(self, pixel_color):
'''
Given the pixel color (as a Color object) return True if it represents the skin color
Color is skin if hue in degrees is (> 240 or less than or equal to 20) and
green is in the green limits and it is not white
'''
if((pixel_color.red > 95) and (pixel_color.green > 40)
and (pixel_color.blue > 20)
and ((max(pixel_color.red, pixel_color.green, pixel_color.blue)-
min(pixel_color.red, pixel_color.green, pixel_color.blue)) > 15 )
and (pixel_color.red-pixel_color.green > 15)
and (pixel_color.red > pixel_color.green)\
and (pixel_color.red > pixel_color.blue)):
return True
def is_hair(self, pixel_color):
'''
Return True if the pixel color represents hair - it is if intensity < 80 and ((B-G)<15 or (B-R)<15 or
hue is between 20 and 40)
'''
if (pixel_color.intensity < 80
and ((pixel_color.norm_b - pixel_color.norm_g < 15)
or (pixel_color.norm_b - pixel_color.norm_r < 15)
or pixel_color.hue() in range (20, 41))):
return True
return False
def is_skin_hair_block(self, block, block_type):
'''
Return true if the block (given by the argument 'block' which is the coordinate-tuple for the top-left corner)
is a skin/hair-block - it is if a majority (as per the threshold attribute) of the pixels in the block are
skin/hair colored. 'block_type' says whether we are testing for a skin block ('s') or a hair block ('h).
'''
block_check = 0
pixelx = block[0]
pixely = block[1]
for pixelx in range (pixelx, pixelx + self.block_size ):
for pixely in range (pixely, pixely + self.block_size):
if self.input_image.get_rgba(pixelx, pixely) == None:
continue
color_block = Color(self.input_image.get_rgba(pixelx, pixely))
if (self.is_skin(color_block) and block_type == 's'):
block_check += 1
elif (self.is_hair(color_block) and block_type == 'h'):
block_check += 1
return block_check >= self.threshold
def add_neighbour_blocks(self, block, graph):
'''
Given a block (given by the argument 'block' which is the coordinate-tuple for the top-left corner)
and a graph (could be a hair or a skin graph), add edges from the current block to its neighbours
on the image that are already nodes of the graph
Check blocks to the left, top-left and top of the current block and if any of these blocks is in the
graph (means the neighbour is also of the same type - skin or hair) add an edge from the current block
to the neighbour.
'''
for direction in ((-self.block_size, 0),
(-self.block_size, -self.block_size),
(0, -self.block_size),
(self.block_size, -self.block_size)):
if (graph.is_node((block[0] + direction[0],
block[1] + direction[1]))):
graph.add_directed_edge(block, (block[0] + direction[0],
block[1] + direction[1]))
def make_block_graph(self):
'''
Return the skin and hair graphs - nodes are the skin/hair blocks respectively
Initialize skin and hair graphs. For every block if it is a skin(hair) block
add edges to its neighbour skin(hair) blocks in the corresponding graph
For this to work the blocks have to be traversed in the top->bottom, left->right order
'''
comparison = lambda x, y : (1 if (x[0] > y[0])
else (0 if x[0]==y[0] else -1))
skin_graph = Graph(comparison)
hair_graph = Graph(comparison)
pixelx = 0
pixely = 0
while pixely <= (self.input_image.size()[1] - self.block_size):
while pixelx <= (self.input_image.size()[0] - self.block_size):
if self.is_skin_hair_block((pixelx, pixely), 's'):
skin_graph.add_node((pixelx, pixely))
self.add_neighbour_blocks((pixelx, pixely), skin_graph)
elif self.is_skin_hair_block((pixelx, pixely), 'h'):
hair_graph.add_node((pixelx, pixely))
self.add_neighbour_blocks((pixelx, pixely), hair_graph)
pixelx += self.block_size
pixelx = 0
pixely += self.block_size
return skin_graph, hair_graph
def find_bounding_box(self, component):
'''
Return the bounding box - a box is a pair of tuples - ((minx, miny), (maxx, maxy)) for the component
Argument 'component' - is just the list of blocks in that component where each block is represented by the
coordinates of its top-left pixel.
'''
pixelx_list = []
pixely_list = []
for nodes in component:
pixelx_list.append(nodes[0])
pixely_list.append(nodes[1])
return (((min(pixelx_list), min(pixely_list)),
(max(pixelx_list), max(pixely_list))))
def inside(self, corners, box):
'''
Check if a given coordinate lies inside the given box
'''
#print corners
for corner in corners:
if ((corner[0] >= box[0][0] and corner[1] >= box[0][1])
and (corner[0] <= box[1][0] and corner[1] <= box[1][1])):
return True
return False
def skin_hair_match(self, skin_box, hair_box):
'''
Return True if the skin-box and hair-box given are matching according to one of the pre-defined patterns
'''
corner = [(skin_box[0][0], skin_box[0][1]),
(skin_box[0][0], skin_box[1][1]),
(skin_box[1][0], skin_box[0][1]),
(skin_box[1][0], skin_box[1][1])]
if (self.inside(corner, hair_box)):
return True
elif skin_box[0][1] == hair_box[1][1]:
return True
else:
return False
def detect_faces(self):
'''
Main method - to detect faces in the image that this class was initialized with
Return list of face boxes - a box is a pair of tuples - ((minx, miny), (maxx, maxy))
Algo: (i) Make block graph (ii) get the connected components of the graph (iii) filter the connected components
(iv) find bounding box for each component (v) Look for matches between face and hair bounding boxes
Return the list of face boxes that have matching hair boxes
'''
skin_graph, hair_graph = self.make_block_graph()
skin_components = skin_graph.get_connected_components()
hair_components = hair_graph.get_connected_components()
filtered_skin = []
filtered_hair = []
for component in skin_components:
if len(component) >= self.component_length:
filtered_skin.append(component)
for component in hair_components:
if len(component) >= self.component_length:
filtered_hair.append(component)
bound_face = []
bound_hair = []
for block in filtered_skin:
bound_face.append(self.find_bounding_box(block))
for block in filtered_hair:
bound_hair.append(self.find_bounding_box(block))
del filtered_skin, filtered_hair
face_box = []
for component in bound_face:
for match in bound_hair:
if (self.skin_hair_match(component, match)
== True):
face_box.append(component)
return face_box
def mark_box(self, box, color):
'''
Mark the box (same as in the above methods) with a given color (given as a raw triple)
This is just a one-pixel wide line showing the box.
'''
for pixelx in range(box[0][0], box[1][0]):
self.input_image.set(pixelx, box[0][1], color)
for pixelx in range(box[0][0], box[1][0]):
self.input_image.set(pixelx, box[1][1], color)
for pixely in range(box[0][1], box[1][1]):
self.input_image.set(box[0][0], pixely, color)
for pixely in range(box[0][1], box[1][1]):
self.input_image.set(box[1][0], pixely, color)
def mark_faces(self, marked_file):
'''
Detect faces and mark each face detected -- mark the bounding box of each face in red
and save the marked image in a new file
'''
face_box = self.detect_faces()
for box in face_box:
self.mark_box(box, (255, 0, 0))
self.input_image.save(marked_file)
class FaceDetector(object):
'''
classdocs
'''
def __init__(self,
filename,
block_size=5,
min_component_size=15,
majority=0.5):
'''
Constructor - keeps input image filename, image read from the file as a PyImage object, block size (in pixels),
threshold to decide how many skin color pixels are required to declare a block as a skin-block
and min number of blocks required for a component. The majority argument says what fraction of
the block pixels must be skin/hair colored for the block to be a skin/hair block - the default value is
0.5 (half).
'''
# Your code
self.filename = filename
self.block_size = block_size
self.min_component_size = min_component_size
self.majority = majority
self.skin = Graph()
self.hair = Graph()
self.pyim = PyImage(filename)
def skin_green_limits(self, red):
'''
Return the limits of normalized green given the normalized red component as a tuple (min, max)
'''
return ((-0.776 * red * red + 0.5601 * red + 0.1766),
(-1.376 * red * red + 1.0743 * red + 0.1452))
def is_skin(self, pixel_color):
'''
Given the pixel color (as a Color object) return True if it represents the skin color
Color is skin if hue in degrees is (> 240 or less than or equal to 20) and
green is in the green limits and it is not white
'''
# Your code
color = Color(pixel_color)
hue = color.hue_degrees()
W = (color.R - 0.33) * (color.R - 0.33) + (color.G - 0.33) * (color.G -
0.33)
green_limits = self.skin_green_limits(color.R)
cond1 = (hue > 240 or hue <= 20)
cond2 = green_limits[0] < color.G and color.G < green_limits[1]
cond3 = color.rgb_abs()[0] < 230 and color.rgb_abs(
)[1] < 230 and color.rgb_abs()[2] < 230
cond4 = 0.6 > color.R and color.R > 0.2
if cond1 and cond2 and cond3 and cond4 and W > 0.0004:
return True
return False
def is_hair(self, pixel_color):
'''
Return True if the pixel color represents hair - it is if intensity < 80 and ((B-G)<15 or (B-R)<15 or
hue is between 20 and 40)
'''
# Your code
color = Color(pixel_color)
hue = color.hue_degrees()
R = pixel_color[0]
G = pixel_color[1]
B = pixel_color[2]
if ((20 <= hue and hue <= 40) or (B - G) < 15 or
(B - R) < 15) and color.intensity < 80:
return True
return False
def is_skin_hair_block(self, block, block_type):
'''
Return true if the block (given by the argument 'block' which is the coordinate-tuple for the top-left corner)
is a skin/hair-block - it is if a majority (as per the threshold attribute) of the pixels in the block are
skin/hair colored. 'block_type' says whether we are testing for a skin block ('s') or a hair block ('h).
'''
# Your code
count_pixels = 0
for pixely in range(block[1], block[1] + self.block_size):
for pixelx in range(block[0], block[0] + self.block_size):
if pixelx < self.pyim.size()[0] and pixely < self.pyim.size(
)[1]:
color = self.pyim.get_rgba(pixelx, pixely)
if block_type == 's' and self.is_skin(color):
count_pixels += 1
elif block_type == 'h' and self.is_hair(color):
count_pixels += 1
if ((count_pixels * 1.0) /
(self.block_size * self.block_size * 1.0)) >= self.majority:
return True
return False
def add_neighbour_blocks(self, block, graph):
'''
Given a block (given by the argument 'block' which is the coordinate-tuple for the top-left corner)
and a graph (could be a hair or a skin graph), add edges from the current block to its neighbours
on the image that are already nodes of the graph
Check blocks to the left, top-left and top of the current block and if any of these blocks is in the
graph (means the neighbour is also of the same type - skin or hair) add an edge from the current block
to the neighbour.
'''
# Your code
valid_rangeX = range(self.pyim.size()[0])
valid_rangeY = range(self.pyim.size()[1])
length = self.block_size
if self.is_skin_hair_block(block, 's'):
if (block[0] - length) in valid_rangeX and self.is_skin_hair_block(
(block[0] - length, block[1]), 's'):
self.skin.add_edge((block[0] - length, block[1]), block)
if (block[0] - length) in valid_rangeX and (
block[1] -
length) in valid_rangeY and self.is_skin_hair_block(
(block[0] - length, block[1] - length), 's'):
self.skin.add_edge((block[0] - length, block[1] - length),
block)
if (block[1] - length) in valid_rangeY and self.is_skin_hair_block(
(block[0], block[1] - length), 's'):
self.skin.add_edge((block[0], block[1] - length), block)
if (block[0] + length) in valid_rangeX and (
block[1] -
length) in valid_rangeY and self.is_skin_hair_block(
(block[0] + length, block[1] - length), 's'):
self.skin.add_edge((block[0] + length, block[1] - length),
block)
elif self.is_skin_hair_block(block, 'h'):
#self.hair.add_node(block)
if (block[0] - length) in valid_rangeX and self.is_skin_hair_block(
(block[0] - length, block[1]), 'h'):
self.hair.add_edge((block[0] - length, block[1]), block)
if (block[0] - length) in valid_rangeX and (
block[1] -
length) in valid_rangeY and self.is_skin_hair_block(
(block[0] - length, block[1] - length), 'h'):
self.hair.add_edge((block[0] - length, block[1] - length),
block)
if (block[1] - length) in valid_rangeY and self.is_skin_hair_block(
(block[0], block[1] - length), 'h'):
self.hair.add_edge((block[0], block[1] - length), block)
if (block[0] + length) in valid_rangeX and (
block[1] -
length) in valid_rangeY and self.is_skin_hair_block(
(block[0] + length, block[1] - length), 'h'):
self.hair.add_edge((block[0] + length, block[1] - length),
block)
def make_block_graph(self):
'''
Return the skin and hair graphs - nodes are the skin/hair blocks respectively
Initialize skin and hair graphs. For every block if it is a skin(hair) block
add edges to its neighbour skin(hair) blocks in the corresponding graph
For this to work the blocks have to be traversed in the top->bottom, left->right order
'''
# Your code
for col in range(0, self.pyim.size()[1], self.block_size):
for row in range(0, self.pyim.size()[0], self.block_size):
if self.is_skin_hair_block((row, col), 's'):
self.skin.add_node((row, col))
self.add_neighbour_blocks((row, col), self.skin.adjacency)
elif self.is_skin_hair_block((row, col), 'h'):
self.hair.add_node((row, col))
self.add_neighbour_blocks((row, col), self.hair.adjacency)
def find_bounding_box(self, component):
'''
Return the bounding box - a box is a pair of tuples - ((minx, miny), (maxx, maxy)) for the component
Argument 'component' - is just the list of blocks in that component where each block is represented by the
coordinates of its top-left pixel.
'''
# Your code
minx = miny = maxx = maxy = component[0][0]
for tup in component:
if tup[0] > maxx:
maxx = tup[0]
if tup[0] < minx:
minx = tup[0]
if tup[1] > maxy:
maxy = tup[1]
if tup[1] < miny:
miny = tup[1]
return ((minx, miny), (maxx, maxy))
def skin_hair_match(self, skin_box, hair_box):
'''
Return True if the skin-box and hair-box given are matching according to one of the pre-defined patterns
'''
# Your code
hair_minx = hair_box[0][0]
hair_miny = hair_box[0][1]
hair_maxx = hair_box[1][0]
hair_maxy = hair_box[1][1]
skin_minx = skin_box[0][0]
skin_miny = skin_box[0][1]
skin_maxx = skin_box[1][0]
skin_maxy = skin_box[1][1]
'''if skin_miny == hair_maxy :
return True
if skin_miny > hair_miny and skin_minx > hair_minx and skin_maxy < hair_maxy and skin_maxx > hair_maxx:
return True
if hair_miny < skin_miny and skin_miny < hair_maxy:
return True
if hair_maxy == skin_maxy :
return True
if skin_miny <= hair_miny and hair_maxy <= skin_maxy:
return True'''
return True
def detect_faces(self):
'''
Main method - to detect faces in the image that this class was initialized with
Return list of face boxes - a box is a pair of tuples - ((minx, miny), (maxx, maxy))
Algo: (i) Make block graph (ii) get the connected components of the graph (iii) filter the connected components
(iv) find bounding box for each component (v) Look for matches between face and hair bounding boxes
Return the list of face boxes that have matching hair boxes
'''
# Your code
index = 0
self.make_block_graph()
skin_components = self.skin.get_connected_components()
hair_components = self.hair.get_connected_components()
skin_components = skin_components[::-1]
hair_components = hair_components[::-1]
for comp in skin_components:
if len(comp) >= self.min_component_size:
break
index += 1
skin_components = skin_components[index:]
index = 0
for comp in hair_components:
if len(comp) >= self.min_component_size:
break
index += 1
hair_components = hair_components[index:]
skin_boxes = []
hair_boxes = []
for component in skin_components:
skin_boxes.append(self.find_bounding_box(component))
for component in hair_components:
hair_boxes.append(self.find_bounding_box(component))
faces = []
for skin_box in skin_boxes:
for hair_box in hair_boxes:
if self.skin_hair_match(skin_box, hair_box):
faces.append(skin_box)
return faces, hair_boxes
def mark_box(self, box, color):
'''
Mark the box (same as in the above methods) with a given color (given as a raw triple)
This is just a one-pixel wide line showing the box.
'''
# Your code
minx = box[0][0]
miny = box[0][1]
maxx = box[1][0]
maxy = box[1][1]
if minx > self.pyim.size()[0]:
minx = self.pyim.size()[0] - 1
if maxx > self.pyim.size()[0]:
maxx = self.pyim.size()[0] - 1
if miny > self.pyim.size()[1]:
miny = self.pyim.size()[1] - 1
if maxy > self.pyim.size()[1]:
maxy = self.pyim.size()[1] - 1
for pixel in range(minx, maxx + 1):
self.pyim.set(pixel, miny, color)
for pixel in range(minx, maxx + 1):
self.pyim.set(pixel, maxy, color)
for pixel in range(miny, maxy + 1):
self.pyim.set(minx, pixel, color)
for pixel in range(miny, maxy + 1):
self.pyim.set(maxx, pixel, color)
def mark_faces(self, marked_file):
'''
Detect faces and mark each face detected -- mark the bounding box of each face in red
and save the marked image in a new file
'''
# Your code
faces, hair = self.detect_faces()
for box in faces:
self.mark_box(box, (255, 0, 0))
'''for box in hair:
self.mark_box(box, (0, 255, 0))'''
self.pyim.save(marked_file)