def __init__(self, labels=CRAFTED_LABELS):
"""
Detects fish from the OverFeat network Localizer
Reference:
P Sermanet, D. Eigen, X. Zhang, M. Mathieu, R. Fergus, Y. LeCun.
OverFeat: Integrated Recognition, Localization, and Detection
using Convolutional Networks
International Conference on Learning Representations (ICLR 2014), April 2014.
"""
self.localizer = OverfeatLocalizer(match_strings=labels, top_n=2)
class FishLocalizer(object):
def __init__(self, labels=CRAFTED_LABELS):
"""
Detects fish from the OverFeat network Localizer
Reference:
P Sermanet, D. Eigen, X. Zhang, M. Mathieu, R. Fergus, Y. LeCun.
OverFeat: Integrated Recognition, Localization, and Detection
using Convolutional Networks
International Conference on Learning Representations (ICLR 2014), April 2014.
"""
self.localizer = OverfeatLocalizer(match_strings=labels, top_n=2)
def find(self, image, around=15):
"""
Find potential fish locations in an image
@params
image: the image to extract fish from
around: the pixes around the detected fish position to extract from
@returns
list of images containing extracted fish
"""
points = self.localizer.predict(image)
extracted_fish = []
for p, point in enumerate(points):
gmm = GMM()
try:
# use a GMM to find the fish positions
gmm.fit(point)
ulp_x, ulp_y, w, h = convert_gmm_to_box(gmm)
ulp_x = int(ulp_x) - around if ulp_x > around else 0
ulp_y = int(ulp_y) - around if ulp_y > around else 0
w, h = int(w), int(h)
if w == 0 or h == 0:
continue
# extract the fish
extracted_fish.append(image[ulp_y:ulp_y + h + around,
ulp_x:ulp_x + w + around, :])
except Exception as e:
# the gmm could not be fit, possibly due to not enough points
# @TODO: check if the points are the reason for skipping here
print str(e)
#end try
#endfor
return extracted_fish
def convert_gmm_to_box(gmm, color, alpha):
midpoint = gmm.means_
std = 3 * np.sqrt(clf.covars_)
width = std[:, 0]
height = std[:, 1]
upper_left_point = (midpoint[:, 0] - width // 2,
midpoint[:, 1] - height // 2)
return Rectangle(upper_left_point, width, height, ec=color,
fc=color, alpha=alpha)
X = load_sample_image("cat_and_dog.jpg")
dog_label = 'dog.n.01'
cat_label = 'cat.n.01'
clf = OverfeatLocalizer(top_n=1,
match_strings=[dog_label, cat_label])
points = clf.predict(X)
dog_points = points[0]
cat_points = points[1]
plt.imshow(X)
ax = plt.gca()
ax.autoscale(enable=False)
clf = GMM()
clf.fit(dog_points)
dog_box = convert_gmm_to_box(clf, "darkred", .6)
clf.fit(cat_points)
cat_box = convert_gmm_to_box(clf, "steelblue", .6)
ax.add_patch(dog_box)
ax.add_patch(cat_box)
ax.get_xaxis().set_ticks([])
# Show all the boxes being processed
axarr[1, 0].imshow(X / 255.)
axarr[1, 0].axis('off')
# Hard code box size to speed up processing
x_points = np.linspace(0, X.shape[1] - 231, 13)
y_points = np.linspace(0, X.shape[0] - 231, 10)
xx, yy = np.meshgrid(x_points, y_points)
for x, y in zip(xx.flat, yy.flat):
axarr[1, 0].add_patch(Rectangle((x, y), 231, 231, fc='yellow', ec='black',
alpha=.4))
print("Starting localization")
# Get all points with sloth in the top 5 labels
sloth_label = "three-toed sloth, ai, Bradypus tridactylus"
clf = OverfeatLocalizer(match_strings=[sloth_label])
sloth_points = clf.predict(X)[0]
axarr[1, 1].imshow(X / 255.)
axarr[1, 1].axis('off')
axarr[1, 1].autoscale(enable=False)
axarr[1, 1].scatter(sloth_points[:, 0], sloth_points[:, 1], color='orange',
s=50)
print("Localization complete!")
def convert_gmm_to_box(gmm, color, alpha):
midpoint = gmm.means_
std = 3 * np.sqrt(clf.covars_)
width = std[:, 0]
height = std[:, 1]
upper_left_point = (midpoint[:, 0] - width // 2,
# Show all the boxes being processed
axarr[0, 2].imshow(X)
axarr[0, 2].axis('off')
clf = OverfeatTransformer(force_reshape=False)
X_tf = clf.transform(X)
x_points = np.linspace(0, X.shape[1] - 231, X_tf[0].shape[3])
y_points = np.linspace(0, X.shape[0] - 231, X_tf[0].shape[2])
xx, yy = np.meshgrid(x_points, y_points)
for x, y in zip(xx.flat, yy.flat):
axarr[0, 2].add_patch(
Rectangle((x, y), 231, 231, fc='yellow', ec='black', alpha=.4))
# Get all points with sloth in the top 5 labels
sloth_label = "three-toed sloth, ai, Bradypus tridactylus"
clf = OverfeatLocalizer(match_strings=[sloth_label])
sloth_points = clf.predict(X)[0]
axarr[1, 0].imshow(X)
axarr[1, 0].axis('off')
axarr[1, 0].autoscale(enable=False)
axarr[1, 0].scatter(sloth_points[:, 0],
sloth_points[:, 1],
color='orange',
s=50)
# Final localization box
sloth_box = convert_points_to_box(sloth_points, 'orange', .6)
axarr[1, 1].imshow(X)
axarr[1, 1].autoscale(enable=False)
axarr[1, 1].add_patch(sloth_box)
axarr[1, 1].axis('off')
def convert_gmm_to_box(gmm, color, alpha):
midpoint = gmm.means_
std = 3 * np.sqrt(clf.covars_)
width = std[:, 0]
height = std[:, 1]
upper_left_point = (midpoint[:, 0] - width // 2,
midpoint[:, 1] - height // 2)
return Rectangle(upper_left_point, width, height, ec=color,
fc=color, alpha=alpha)
X = load_sample_image("cat_and_dog.jpg")
dog_label = 'dog.n.01'
cat_label = 'cat.n.01'
clf = OverfeatLocalizer(top_n=1,
match_strings=[dog_label, cat_label])
points = clf.predict(X.astype('float32'))
dog_points = points[0]
cat_points = points[1]
plt.imshow(X)
ax = plt.gca()
ax.autoscale(enable=False)
clf = GMM()
clf.fit(dog_points)
dog_box = convert_gmm_to_box(clf, "darkred", .6)
clf.fit(cat_points)
cat_box = convert_gmm_to_box(clf, "steelblue", .6)
ax.add_patch(dog_box)
ax.add_patch(cat_box)
ax.get_xaxis().set_ticks([])
print(__doc__)
import matplotlib.pyplot as plt
from matplotlib.patches import Rectangle
from sklearn_theano.datasets import load_sample_image
from sklearn_theano.feature_extraction import OverfeatLocalizer
from sklearn_theano.feature_extraction import get_all_overfeat_labels
def convert_points_to_box(points, color, alpha):
upper_left_point = (points[:, 0].min(), points[:, 1].min())
width = points[:, 0].max() - points[:, 0].min()
height = points[:, 1].max() - points[:, 1].min()
return Rectangle(upper_left_point, width, height, ec=color,
fc=color, alpha=alpha)
X = load_sample_image("sloth.jpg")
sloth_label = [label for label in get_all_overfeat_labels()
if 'three-toed sloth' in label][0]
clf = OverfeatLocalizer(match_strings=[sloth_label])
sloth_points = clf.predict(X.astype('float32'))[0]
sloth_box = convert_points_to_box(sloth_points, 'orange', .4)
plt.imshow(X)
ax = plt.gca()
ax.autoscale(enable=False)
ax.add_patch(sloth_box)
ax.get_xaxis().set_ticks([])
ax.get_yaxis().set_ticks([])
plt.show()
print(__doc__)
import matplotlib.pyplot as plt
from matplotlib.patches import Rectangle
from sklearn_theano.datasets import load_sample_image
from sklearn_theano.feature_extraction import OverfeatLocalizer
from sklearn_theano.feature_extraction import get_all_overfeat_labels
def convert_points_to_box(points, color, alpha):
upper_left_point = (points[:, 0].min(), points[:, 1].min())
width = points[:, 0].max() - points[:, 0].min()
height = points[:, 1].max() - points[:, 1].min()
return Rectangle(upper_left_point, width, height, ec=color,
fc=color, alpha=alpha)
X = load_sample_image("sloth.jpg")
sloth_label = [label for label in get_all_overfeat_labels()
if 'three-toed sloth' in label][0]
clf = OverfeatLocalizer(match_strings=[sloth_label])
sloth_points = clf.predict(X)[0]
sloth_box = convert_points_to_box(sloth_points, 'orange', .4)
plt.imshow(X)
ax = plt.gca()
ax.autoscale(enable=False)
ax.add_patch(sloth_box)
ax.get_xaxis().set_ticks([])
ax.get_yaxis().set_ticks([])
plt.show()
