def find_best_match(model_images, query_images, dist_type, hist_type,
num_bins):
hist_isgray = histogram_module.is_grayvalue_hist(hist_type)
model_hists = compute_histograms(model_images, hist_type, hist_isgray,
num_bins)
query_hists = compute_histograms(query_images, hist_type, hist_isgray,
num_bins)
D = np.zeros((len(model_images), len(query_images)))
best_match = []
for i, query_hist in enumerate(query_hists):
min_d = np.inf
min_d_index = -1
for j, model_hist in enumerate(model_hists):
d = dist_module.get_dist_by_name(query_hist, model_hist, dist_type)
D[j][i] = d
if d < min_d:
min_d = d
min_d_index = j
best_match.append(min_d_index)
return best_match, D
def find_best_match(model_images, query_images, dist_type, hist_type,
num_bins):
hist_isgray = histogram_module.is_grayvalue_hist(hist_type)
model_hists = compute_histograms(model_images, hist_type, hist_isgray,
num_bins)
query_hists = compute_histograms(query_images, hist_type, hist_isgray,
num_bins)
D = np.zeros((len(model_images), len(query_images)))
#-------------------------------------------------------------
#print(D.shape)
#print(model_hists)
#print(query_hists)
for qe in (range(len(query_hists))):
for mo in (range(len(model_hists))):
D[mo][qe] = dist_module.get_dist_by_name(query_hists[qe],
model_hists[mo],
dist_type)
#index of best match to each querry image
best_match = np.argmin(D, axis=0)
return best_match, D
def find_best_match(model_images, query_images, dist_type, hist_type,
num_bins):
hist_isgray = histogram_module.is_grayvalue_hist(hist_type)
model_hists = compute_histograms(model_images, hist_type, hist_isgray,
num_bins)
query_hists = compute_histograms(query_images, hist_type, hist_isgray,
num_bins)
D = np.zeros((len(model_images), len(query_images)))
# best_match = [99999] * len(query_images)
# your code here
# compute distance matrix
for i in range(len(model_images)):
hist1 = model_hists[i]
for j in range(len(query_images)):
hist2 = query_hists[j]
D[i, j] = dist_module.get_dist_by_name(hist1, hist2, dist_type)
# if D[i,j] < best_match[j]:
# best_match[j] = D[i,j]
# find the best match for each query image
best_match = D.argmin(axis=0)
# print (best_match)
return best_match, D
def find_best_match(model_images, query_images, dist_type, hist_type,
num_bins):
# whether histogram function expects grayvalue or color image
hist_isgray = histogram_module.is_grayvalue_hist(hist_type)
# form list of file images to list of histograms
model_hists = compute_histograms(model_images, hist_type, hist_isgray,
num_bins)
query_hists = compute_histograms(query_images, hist_type, hist_isgray,
num_bins)
D = np.zeros(
(len(model_images), len(query_images))) # container for distances
best_match = [] # container for best match for each image
for i in range(len(query_images)):
Q = query_hists[i]
for j in range(len(model_hists)):
V = model_hists[j]
h1 = Q.copy()
h2 = V.copy()
dist = dist_module.get_dist_by_name(
h1, h2, dist_type) # distance between histogram
D[j, i] = dist # save distance measure in correct position
idx = list(D[:, i]).index(
min(D[:, i])
) # find histogram with lower distance respect to the one in exam
best_match.append(idx)
return np.array(best_match), D
def find_best_match(model_images, query_images, dist_type, hist_type, num_bins):
hist_isgray = histogram_module.is_grayvalue_hist(hist_type)
model_hists = compute_histograms(model_images, hist_type, hist_isgray, num_bins)
query_hists = compute_histograms(query_images, hist_type, hist_isgray, num_bins)
D = np.zeros((len(model_images), len(query_images)))
best_match = np.zeros(len(query_images))
for i in range(len(model_hists)):
for j in range(len(query_hists)):
D[i, j] = dist_module.get_dist_by_name(query_hists[j], model_hists[i], dist_type)
for i in range(len(best_match)):
best_match[i] = np.where(D == np.amin(D[:,i]))[0][0]
return best_match, D
def find_best_match(model_images, query_images, dist_type, hist_type,
num_bins):
hist_isgray = histogram_module.is_grayvalue_hist(hist_type)
model_hists = compute_histograms(model_images, hist_type, hist_isgray,
num_bins)
query_hists = compute_histograms(query_images, hist_type, hist_isgray,
num_bins)
D = np.zeros((len(model_images), len(query_images)))
# ... (your code here)
for i in range(len(model_images)):
for j in range(len(query_images)):
D[i][j] = dist_module.get_dist_by_name(model_hists[i],
query_hists[j], dist_type)
best_match = D.argmin(axis=0)
return best_match, D
def find_best_match(model_images, query_images, dist_type, hist_type, num_bins):
hist_isgray = histogram_module.is_grayvalue_hist(hist_type)
model_hists = compute_histograms(
model_images, hist_type, hist_isgray, num_bins)
query_hists = compute_histograms(
query_images, hist_type, hist_isgray, num_bins)
D = np.zeros((len(model_images), len(query_images)))
# your code here
for model_image_hist_index in range(D.shape[0]):
for query_image_hist_index in range(D.shape[1]):
model_hist = model_hists[model_image_hist_index]
query_hist = query_hists[query_image_hist_index]
D[model_image_hist_index][query_image_hist_index] = dist_module.get_dist_by_name(
model_hist, query_hist, dist_type)
best_match = np.argmin(D, 0)
return best_match, D
def find_best_match(model_images, query_images, dist_type, hist_type, num_bins):
hist_isgray = histogram_module.is_grayvalue_hist(hist_type)
model_hists = compute_histograms(model_images, hist_type, hist_isgray, num_bins)
query_hists = compute_histograms(query_images, hist_type, hist_isgray, num_bins)
D = np.zeros((len(model_images), len(query_images)))
best_match=[]
for i in range(len(query_images)):
for j in range(len(model_images)):
D[j,i]=dist_module.get_dist_by_name(model_hists[j],query_hists[i],dist_type)
minpos = list(D[:,i]).index(min(list(D[:,i])))
best_match.append(int(model_images[minpos].split('/')[1].split('_')[0].split('obj')[1]))
return best_match, D
def find_best_match(model_images, query_images, dist_type, hist_type,
num_bins):
hist_isgray = histogram_module.is_grayvalue_hist(hist_type)
model_hists = compute_histograms(model_images, hist_type, hist_isgray,
num_bins)
query_hists = compute_histograms(query_images, hist_type, hist_isgray,
num_bins)
D = np.zeros((len(model_images), len(query_images)))
# D[i][j] = the best match is j for query i
for i in range(len(model_images)):
histogram_model = model_hists[i]
for j in range(len(query_images)):
histogram_image = query_hists[j]
D[i][j] = dist_module.get_dist_by_name(histogram_image,
histogram_model, dist_type)
best_match = np.argmin(D, axis=0)
return best_match, D
def find_best_match(model_images, query_images, dist_type, hist_type,
num_bins):
hist_isgray = histogram_module.is_grayvalue_hist(hist_type)
model_hists = compute_histograms(model_images, hist_type, hist_isgray,
num_bins)
query_hists = compute_histograms(query_images, hist_type, hist_isgray,
num_bins)
D = np.zeros((len(query_images), len(model_images)))
best_match = np.zeros((len(query_images), len(model_images)))
for i in range(len(query_images)):
for j in range(len(model_images)):
D[i, j] = dist_module.get_dist_by_name(query_hists[i],
model_hists[j], dist_type)
best_match[i] = np.argsort(D[i])
#... (your code here)
best_match = best_match[:, 0]
best_match = best_match.astype(int)
return best_match, D
hist2 = histogram_module.get_hist_by_name(
img2_gray, num_bins_gray, hist_types[hidx])
else:
hist1 = histogram_module.get_hist_by_name(
img1_color, num_bins_color, hist_types[hidx])
hist2 = histogram_module.get_hist_by_name(
img2_color, num_bins_color, hist_types[hidx])
if len(hist1) == 2:
hist1 = hist1[0]
if len(hist2) == 2:
hist2 = hist2[0]
D[didx,
hidx] = dist_module.get_dist_by_name(hist1, hist2,
distance_types[didx])
print('Comparison of images "%s" to "%s":' % (img1_file, img2_file))
print(D)
print('\n')
print('Legend:')
print('%s-%s, %s-%s, %s-%s, %s-%s' %
('l2', 'grayvalue', 'l2', 'rgb', 'l2', 'rg', 'l2', 'dxdy'))
print('%s-%s, %s-%s, %s-%s, %s-%s' %
('intersect', 'grayvalue', 'intersect', 'rgb', 'intersect', 'rg',
'intersect', 'dxdy'))
print('%s-%s, %s-%s, %s-%s, %s-%s' %
('chi2', 'grayvalue', 'chi2', 'rgb', 'chi2', 'rg', 'chi2', 'dxdy'))
# %%