qurator-spk/eynollah
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Robert Sachunsky 2023-02-11 11:58:40 +00:00 • committed by cneud
parent a078a18530
commit 73057d57d1
1 changed files with 12 additions and 7 deletions
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qurator/eynollah/eynollah.py
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@ -356,7 +356,8 @@ class Eynollah:
index_y_d = img_h - img_height_model index_y_d = img_h - img_height_model
img_patch = img[index_y_d:index_y_u, index_x_d:index_x_u, :] img_patch = img[index_y_d:index_y_u, index_x_d:index_x_u, :]
label_p_pred = model_enhancement.predict(img_patch.reshape(1, img_patch.shape[0], img_patch.shape[1], img_patch.shape[2])) label_p_pred = model_enhancement.predict(img_patch.reshape(1, img_patch.shape[0], img_patch.shape[1], img_patch.shape[2]),
verbose=0)
seg = label_p_pred[0, :, :, :] seg = label_p_pred[0, :, :, :]
seg = seg * 255 seg = seg * 255
@ -491,10 +492,11 @@ class Eynollah:
img_in[0, :, :, 0] = img_1ch[:, :] img_in[0, :, :, 0] = img_1ch[:, :]
img_in[0, :, :, 1] = img_1ch[:, :] img_in[0, :, :, 1] = img_1ch[:, :]
img_in[0, :, :, 2] = img_1ch[:, :] img_in[0, :, :, 2] = img_1ch[:, :]
if not self.dir_in: if not self.dir_in:
label_p_pred = model_num_classifier.predict(img_in) label_p_pred = model_num_classifier.predict(img_in, verbose=0)
else: else:
label_p_pred = self.model_classifier.predict(img_in) label_p_pred = self.model_classifier.predict(img_in, verbose=0)
num_col = np.argmax(label_p_pred[0]) + 1 num_col = np.argmax(label_p_pred[0]) + 1
self.logger.info("Found %s columns (%s)", num_col, label_p_pred) self.logger.info("Found %s columns (%s)", num_col, label_p_pred)
@ -572,10 +574,11 @@ class Eynollah:
if self.dir_in: if self.dir_in:
label_p_pred = self.model_classifier.predict(img_in) label_p_pred = self.model_classifier.predict(img_in, verbose=0)
else: else:
label_p_pred = model_num_classifier.predict(img_in) label_p_pred = model_num_classifier.predict(img_in, verbose=0)
num_col = np.argmax(label_p_pred[0]) + 1 num_col = np.argmax(label_p_pred[0]) + 1
self.logger.info("Found %s columns (%s)", num_col, label_p_pred) self.logger.info("Found %s columns (%s)", num_col, label_p_pred)
if not self.dir_in: if not self.dir_in:
session_col_classifier.close() session_col_classifier.close()
@ -684,7 +687,8 @@ class Eynollah:
img = img / float(255.0) img = img / float(255.0)
img = resize_image(img, img_height_model, img_width_model) img = resize_image(img, img_height_model, img_width_model)
label_p_pred = model.predict(img.reshape(1, img.shape[0], img.shape[1], img.shape[2])) label_p_pred = model.predict(img.reshape(1, img.shape[0], img.shape[1], img.shape[2]),
verbose=0)
seg = np.argmax(label_p_pred, axis=3)[0] seg = np.argmax(label_p_pred, axis=3)[0]
seg_color = np.repeat(seg[:, :, np.newaxis], 3, axis=2) seg_color = np.repeat(seg[:, :, np.newaxis], 3, axis=2)
@ -736,7 +740,8 @@ class Eynollah:
index_y_d = img_h - img_height_model index_y_d = img_h - img_height_model
img_patch = img[index_y_d:index_y_u, index_x_d:index_x_u, :] img_patch = img[index_y_d:index_y_u, index_x_d:index_x_u, :]
label_p_pred = model.predict(img_patch.reshape(1, img_patch.shape[0], img_patch.shape[1], img_patch.shape[2])) label_p_pred = model.predict(img_patch.reshape(1, img_patch.shape[0], img_patch.shape[1], img_patch.shape[2]),
verbose=0)
seg = np.argmax(label_p_pred, axis=3)[0] seg = np.argmax(label_p_pred, axis=3)[0]
seg_color = np.repeat(seg[:, :, np.newaxis], 3, axis=2) seg_color = np.repeat(seg[:, :, np.newaxis], 3, axis=2)