def pick_point_from_image1(event, x, y, flags, param):
global img1
if event == cv2.EVENT_LBUTTONDOWN:
u = x
v = y
draw_reticle(img1, u, v, (0, 255, 0))
print u, v
print res_a[v, u, :]
print "is your descriptor"
save_descriptor_to_yaml(res_a[v, u, :])
def find_best_match(self, event, u, v, flags, param):
"""
For each network, find the best match in the target image to point highlighted
with reticle in the source image. Displays the result
:return:
:rtype:
"""
img_1_with_reticle = np.copy(self.img1)
draw_reticle(img_1_with_reticle, u, v, self._reticle_color)
cv2.imshow("source", img_1_with_reticle)
alpha = self._config["blend_weight_original_image"]
beta = 1 - alpha
img_2_with_reticle = np.copy(self.img2)
print "\n\n"
self._res_uv = dict()
# self._res_a_uv = dict()
# self._res_b_uv = dict()
for network_name in self._dcn_dict:
res_a = self._res_a[network_name]
res_b = self._res_b[network_name]
best_match_uv, best_match_diff, norm_diffs = \
DenseCorrespondenceNetwork.find_best_match((u, v), res_a, res_b)
print "\n\n"
print "network_name:", network_name
self._res_uv[network_name] = dict()
self._res_uv[network_name]['source'] = res_a[v, u, :].tolist()
self._res_uv[network_name]['target'] = res_b[v, u, :].tolist()
# print "res_a[v, u, :]:", res_a[v, u, :]
# print "res_b[v, u, :]:", res_b[v, u, :]
print "%s best match diff: %.3f" % (network_name, best_match_diff)
threshold = self._config["norm_diff_threshold"]
if network_name in self._config["norm_diff_threshold_dict"]:
threshold = self._config["norm_diff_threshold_dict"][
network_name]
heatmap = self.scale_norm_diffs_to_make_heatmap(
norm_diffs, threshold)
reticle_color = self._network_reticle_color[network_name]
draw_reticle(heatmap, best_match_uv[0], best_match_uv[1],
reticle_color)
draw_reticle(img_2_with_reticle, best_match_uv[0],
best_match_uv[1], reticle_color)
blended = cv2.addWeighted(self.img2_gray, alpha, heatmap, beta, 0)
cv2.imshow(network_name, blended)
cv2.imshow("target", img_2_with_reticle)
if event == cv2.EVENT_LBUTTONDOWN:
utils.saveToYaml(self._res_uv, 'clicked_point.yaml')
def find_best_match(self, event,u,v,flags,param):
"""
For each network, find the best match in the target image to point highlighted
with reticle in the source image. Displays the result
:return:
:rtype:
"""
img_1_with_reticle = np.copy(self.img1)
draw_reticle(img_1_with_reticle, u, v, self._reticle_color)
cv2.imshow("source", img_1_with_reticle)
alpha = self._config["blend_weight_original_image"]
beta = 1 - alpha
img_2_with_reticle = np.copy(self.img2)
print "\n\n"
self._res_uv = dict()
# self._res_a_uv = dict()
# self._res_b_uv = dict()
for network_name in self._dcn_dict:
res_a = self._res_a[network_name]
res_b = self._res_b[network_name]
best_match_uv, best_match_diff, norm_diffs = \
DenseCorrespondenceNetwork.find_best_match((u, v), res_a, res_b)
print "\n\n"
print "network_name:", network_name
self._res_uv[network_name] = dict()
self._res_uv[network_name]['source'] = res_a[v, u, :].tolist()
self._res_uv[network_name]['target'] = res_b[v, u, :].tolist()
# print "res_a[v, u, :]:", res_a[v, u, :]
# print "res_b[v, u, :]:", res_b[v, u, :]
print "%s best match diff: %.3f" %(network_name, best_match_diff)
threshold = self._config["norm_diff_threshold"]
if network_name in self._config["norm_diff_threshold_dict"]:
threshold = self._config["norm_diff_threshold_dict"][network_name]
heatmap = self.scale_norm_diffs_to_make_heatmap(norm_diffs, threshold)
reticle_color = self._network_reticle_color[network_name]
draw_reticle(heatmap, best_match_uv[0], best_match_uv[1], reticle_color)
draw_reticle(img_2_with_reticle, best_match_uv[0], best_match_uv[1], reticle_color)
blended = cv2.addWeighted(self.img2_gray, alpha, heatmap, beta, 0)
cv2.imshow(network_name, blended)
cv2.imshow("target", img_2_with_reticle)
if event == cv2.EVENT_LBUTTONDOWN:
utils.saveToYaml(self._res_uv, 'clicked_point.yaml')
def find_best_match(self, event, u, v, flags, param):
"""
For each network, find the best match in the target image to point highlighted
with reticle in the source image. Displays the result
:return:
:rtype:
"""
img_1_with_reticle = np.copy(self.img1)
draw_reticle(img_1_with_reticle, u, v, self._reticle_color)
source = img_1_with_reticle
alpha = self._config["blend_weight_original_image"]
beta = 1 - alpha
img_2_with_reticle = np.copy(self.img2)
self._res_uv = dict()
for network_name in self._dcn_dict:
res_a = self._res_a[network_name]
res_b = self._res_b[network_name]
best_match_uv, best_match_diff, norm_diffs = \
DenseCorrespondenceNetwork.find_best_match((u, v), res_a, res_b)
print "network_name:", network_name
self._res_uv[network_name] = dict()
self._res_uv[network_name]['source'] = res_a[v, u, :].tolist()
self._res_uv[network_name]['target'] = res_b[v, u, :].tolist()
print "%s best match diff: %.3f" % (network_name, best_match_diff)
threshold = self._config["norm_diff_threshold"]
if network_name in self._config["norm_diff_threshold_dict"]:
threshold = self._config["norm_diff_threshold_dict"][
network_name]
heatmap = self.scale_norm_diffs_to_make_heatmap(
norm_diffs, threshold)
reticle_color = self._network_reticle_color[network_name]
draw_reticle(heatmap, best_match_uv[0], best_match_uv[1],
reticle_color)
draw_reticle(img_2_with_reticle, best_match_uv[0],
best_match_uv[1], reticle_color)
blended = cv2.addWeighted(self.img2_gray, alpha, heatmap, beta, 0)
target = img_2_with_reticle
return (source, blended, target, [best_match_uv[0], best_match_uv[1]])
def draw_points(img, img_points_picked):
for index, img_point in enumerate(img_points_picked):
color = label_colors[index % len(label_colors)]
draw_reticle(img, int(img_point["u"]), int(img_point["v"]), color)
def draw_points(img, img_points_picked):
for index, img_point in enumerate(img_points_picked):
draw_reticle(img, int(img_point["u"]), int(img_point["v"]),
label_colors[index])