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panel_disconnet

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This commit is contained in:
Daniel Saavedra
2020-03-25 09:43:11 -03:00
parent dce4a1a2c3
commit 7010af8a58
2 changed files with 43 additions and 36 deletions
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16
panel_disconnect.py
View File

@@ -7,7 +7,7 @@ Created on Tue Mar 17 13:55:42 2020
"""
import numpy as np
def disconnect(image, boxes, obj_thresh = 0.5, area_min = 400, merge = 0):
def disconnect(image, boxes, obj_thresh = 0.5, area_min = 400, merge = 0, z_thresh = 1.8):
new_boxes = []
for num, box in enumerate(boxes):
@@ -17,25 +17,25 @@ def disconnect(image, boxes, obj_thresh = 0.5, area_min = 400, merge = 0):
ymin = box.ymin + merge
ymax = box.ymax - merge
if xmin > 0 and ymin > 0 and xmax < image.shape[1] and ymax < image.shape[0] and box.classes[0] > obj_thresh:
if xmin > 0 and ymin > 0 and xmax < image.shape[1] and ymax < image.shape[0] and box.get_score() > obj_thresh:
area = (ymax - ymin)*(xmax - xmin)
z_score = np.sum(image[np.int(ymin):np.int(ymax), np.int(xmin):np.int(xmax)]) / area
if area > area_min:
box.score = z_score
box.z_score = z_score
new_boxes.append(box)
#boxes_area_score[str(num)] = {'xmin': xmin, 'xmax': xmax, 'ymin': ymin, 'ymax': ymax, 'score' : score, 'area' : area}
mean_score = np.mean([box.score for box in new_boxes])
sd_score = np.std([box.score for box in new_boxes])
mean_score = np.mean([box.z_score for box in new_boxes])
sd_score = np.std([box.z_score for box in new_boxes])
new_boxes = [box for box in new_boxes if (box.score - mean_score)/sd_score > 2]
new_boxes = [box for box in new_boxes if (box.z_score - mean_score)/sd_score > z_thresh]
for box in new_boxes:
z_score = (box.score - mean_score)/sd_score
box.classes[0] = min((z_score-2)*0.5+ 0.5, 1)
z_score = (box.z_score - mean_score)/sd_score
box.classes[0] = min((z_score-z_thresh)*0.5/(3-z_thresh)+ 0.5, 1)
return new_boxes

63
predict_yolo3_disconnect.py
View File

@@ -15,7 +15,7 @@ from tqdm import tqdm
import numpy as np
def disconnect(image, boxes, obj_thresh = 0.5, area_min = 400, merge = 0):
def disconnect(image, boxes, obj_thresh = 0.5, area_min = 400, merge = 0, z_thresh = 1.8):
new_boxes = []
for num, box in enumerate(boxes):
@@ -25,31 +25,31 @@ def disconnect(image, boxes, obj_thresh = 0.5, area_min = 400, merge = 0):
ymin = box.ymin + merge
ymax = box.ymax - merge
if xmin > 0 and ymin > 0 and xmax < image.shape[1] and ymax < image.shape[0] and box.classes[0] > obj_thresh:
if xmin > 0 and ymin > 0 and xmax < image.shape[1] and ymax < image.shape[0] and box.get_score() > obj_thresh:
area = (ymax - ymin)*(xmax - xmin)
z_score = np.sum(image[np.int(ymin):np.int(ymax), np.int(xmin):np.int(xmax)]) / area
if area > area_min:
box.score = z_score
box.z_score = z_score
new_boxes.append(box)
#boxes_area_score[str(num)] = {'xmin': xmin, 'xmax': xmax, 'ymin': ymin, 'ymax': ymax, 'score' : score, 'area' : area}
mean_score = np.mean([box.score for box in new_boxes])
sd_score = np.std([box.score for box in new_boxes])
mean_score = np.mean([box.z_score for box in new_boxes])
sd_score = np.std([box.z_score for box in new_boxes])
new_boxes = [box for box in new_boxes if (box.score - mean_score)/sd_score > 2]
new_boxes = [box for box in new_boxes if (box.z_score - mean_score)/sd_score > z_thresh]
for box in new_boxes:
z_score = (box.score - mean_score)/sd_score
box.classes[0] = min((z_score-2)*0.5+ 0.5, 1)
z_score = (box.z_score - mean_score)/sd_score
box.classes[0] = min((z_score-z_thresh)*0.5/(3-z_thresh)+ 0.5, 1)
return new_boxes
def disconnect_plot(image, boxes, obj_thresh = 0.5, area_min = 400, merge = 0):
def disconnect_plot(image, boxes, obj_thresh = 0.5, area_min = 400, merge = 0, z_thresh = 1.8):
new_boxes = []
for num, box in enumerate(boxes):
@@ -59,21 +59,21 @@ def disconnect_plot(image, boxes, obj_thresh = 0.5, area_min = 400, merge = 0):
ymin = box.ymin + merge
ymax = box.ymax - merge
if xmin > 0 and ymin > 0 and xmax < image.shape[1] and ymax < image.shape[0] and box.classes[0] > obj_thresh:
if xmin > 0 and ymin > 0 and xmax < image.shape[1] and ymax < image.shape[0] and box.get_score() > obj_thresh:
area = (ymax - ymin)*(xmax - xmin)
z_score = np.sum(image[np.int(ymin):np.int(ymax), np.int(xmin):np.int(xmax)]) / area
if area > area_min:
box.score = z_score
box.z_score = z_score
new_boxes.append(box)
#boxes_area_score[str(num)] = {'xmin': xmin, 'xmax': xmax, 'ymin': ymin, 'ymax': ymax, 'score' : score, 'area' : area}
mean_score = np.mean([box.score for box in new_boxes])
sd_score = np.std([box.score for box in new_boxes])
mean_score = np.mean([box.z_score for box in new_boxes])
sd_score = np.std([box.z_score for box in new_boxes])
normal_score = ([box.score for box in new_boxes] - mean_score)/sd_score
normal_score = ([box.z_score for box in new_boxes] - mean_score)/sd_score
# plt.figure()
# _ = plt.hist(normal_score, bins='auto') # arguments are passed to np.histogram
# plt.title("Histogram with 'auto' bins")
@@ -87,12 +87,12 @@ def disconnect_plot(image, boxes, obj_thresh = 0.5, area_min = 400, merge = 0):
# plt.title("Histogram with 'auto' bins")
# plt.show()
new_boxes = [box for box in new_boxes if (box.score - mean_score)/sd_score > 2]
new_boxes = [box for box in new_boxes if (box.z_score - mean_score)/sd_score > z_thresh]
for box in new_boxes:
z_score = (box.score - mean_score)/sd_score
box.classes[0] = min((z_score-2)*0.5+ 0.5, 1)
z_score = (box.z_score - mean_score)/sd_score
box.classes[0] = min((z_score-z_thresh)*0.5/(3-z_thresh)+ 0.5, 1)
@@ -224,20 +224,27 @@ def _main_(args):
# the main loop
times = []
for image_path in image_paths:
images = [cv2.imread(image_path) for image_path in image_paths]
print(images)
start = time.time()
# predict the bounding boxes
boxes = get_yolo_boxes(infer_model, images, net_h, net_w, config['model']['anchors'], obj_thresh, nms_thresh)
boxes = [[box for box in boxes_image if box.get_score() > obj_thresh] for boxes_image in boxes]
print('Elapsed time = {}'.format(time.time() - start))
times.append(time.time() - start)
boxes_disc = [disconnect(image, boxes_image, z_thresh = 1.8) for image, boxes_image in zip(images, boxes)]
for image, boxes_image in zip(images, boxes_disc):
image = cv2.imread(image_path)
print(image_path)
start = time.time()
# predict the bounding boxes
boxes = get_yolo_boxes(infer_model, [image], net_h, net_w, config['model']['anchors'], obj_thresh, nms_thresh)[0]
print('Elapsed time = {}'.format(time.time() - start))
times.append(time.time() - start)
# draw bounding boxes on the image using labels
I = image.copy()
draw_boxes(I, boxes, config['model']['labels'], obj_thresh)
draw_boxes(I, boxes_image, config['model']['labels'], obj_thresh)
plt.figure(figsize = (10,12))
plt.imshow(I)
# write the image with bounding boxes to file
cv2.imwrite(output_path + image_path.split('/')[-1], np.uint8(image))