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change panel disconnect

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This commit is contained in:
Daniel Saavedra
2020-04-03 13:15:34 -03:00
parent 7cf0c577a1
commit 4351f33609
7 changed files with 118 additions and 212 deletions
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3
Result_yolo3_panel/.gitignore vendored
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@@ -4,7 +4,8 @@ Result_Prueba/
Prueba/
Otros_2/
Otros/
Result_Prueba_disconnect
*.jpg
*.h5
*.pkl

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panel_disconnect.py
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@@ -8,34 +8,107 @@ 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, z_thresh = 1.8):
new_boxes = []
for num, box in enumerate(boxes):
xmin = box.xmin + merge
xmax = box.xmax - merge
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.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.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.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.z_score - mean_score)/sd_score > z_thresh]
for box in new_boxes:
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
box.score = -1
return new_boxes
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):
xmin = box.xmin + merge
xmax = box.xmax - merge
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.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.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.z_score for box in new_boxes])
sd_score = np.std([box.z_score for box in new_boxes])
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")
# plt.show()
#
# plt.figure()
# mean = np.mean([boxes_area_score[i]['area'] for i in boxes_area_score])
# sd = np.std([boxes_area_score[i]['area'] for i in boxes_area_score])
# normal = ([boxes_area_score[i]['area'] for i in boxes_area_score] - mean)/sd
# _ = plt.hist(normal, bins='auto') # arguments are passed to np.histogram
# plt.title("Histogram with 'auto' bins")
# plt.show()
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.z_score - mean_score)/sd_score
box.classes[0] = min((z_score-z_thresh)*0.5/(3-z_thresh)+ 0.5, 1)
colors = plt.cm.brg(np.linspace(0, 1, 21)).tolist()
plt.figure(figsize=(10,6))
plt.imshow(I,cmap = 'gray')
current_axis = plt.gca()
for box in new_boxes:
color = colors[2]
#boxes_area_score[key]['score_norm'] = (boxes_area_score[key]['score'] - mean) / sd
#z_score = (box.score - mean_score) / sd_score
#z_score = (boxes_area_score[key]['area'] )
### Escribe el z-score
#if z_score > 1:
current_axis.text((box.xmin + box.xmax)/2,
(box.ymin+ box.ymax)/2,
'%.2f' % box.classes[0], size='x-large',
color='white', bbox={'facecolor':color, 'alpha':1.0})
return new_boxes

230
predict_yolo3_disconnect.py
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@@ -13,111 +13,9 @@ from utils.bbox import draw_boxes
from tensorflow.keras.models import load_model
from tqdm import tqdm
import numpy as np
from panel_disconnect import disconnect
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):
xmin = box.xmin + merge
xmax = box.xmax - merge
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.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.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.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.z_score - mean_score)/sd_score > z_thresh]
for box in new_boxes:
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, z_thresh = 1.8):
new_boxes = []
for num, box in enumerate(boxes):
xmin = box.xmin + merge
xmax = box.xmax - merge
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.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.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.z_score for box in new_boxes])
sd_score = np.std([box.z_score for box in new_boxes])
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")
# plt.show()
#
# plt.figure()
# mean = np.mean([boxes_area_score[i]['area'] for i in boxes_area_score])
# sd = np.std([boxes_area_score[i]['area'] for i in boxes_area_score])
# normal = ([boxes_area_score[i]['area'] for i in boxes_area_score] - mean)/sd
# _ = plt.hist(normal, bins='auto') # arguments are passed to np.histogram
# plt.title("Histogram with 'auto' bins")
# plt.show()
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.z_score - mean_score)/sd_score
box.classes[0] = min((z_score-z_thresh)*0.5/(3-z_thresh)+ 0.5, 1)
colors = plt.cm.brg(np.linspace(0, 1, 21)).tolist()
plt.figure(figsize=(10,6))
plt.imshow(I,cmap = 'gray')
current_axis = plt.gca()
for box in new_boxes:
color = colors[2]
#boxes_area_score[key]['score_norm'] = (boxes_area_score[key]['score'] - mean) / sd
#z_score = (box.score - mean_score) / sd_score
#z_score = (boxes_area_score[key]['area'] )
### Escribe el z-score
#if z_score > 1:
current_axis.text((box.xmin + box.xmax)/2,
(box.ymin+ box.ymax)/2,
'%.2f' % box.classes[0], size='x-large',
color='white', bbox={'facecolor':color, 'alpha':1.0})
return new_boxes
def _main_(args):
@@ -134,7 +32,7 @@ def _main_(args):
# Set some parameter
###############################
net_h, net_w = 416, 416 # a multiple of 32, the smaller the faster
obj_thresh, nms_thresh = 0.8, 0.3
obj_thresh, nms_thresh = 0.5, 0.3
###############################
# Load the model
@@ -145,112 +43,46 @@ def _main_(args):
###############################
# Predict bounding boxes
###############################
if 'webcam' in input_path: # do detection on the first webcam
video_reader = cv2.VideoCapture(0)
# the main loop
batch_size = 1
images = []
while True:
ret_val, image = video_reader.read()
if ret_val == True: images += [image]
image_paths = []
if (len(images)==batch_size) or (ret_val==False and len(images)>0):
batch_boxes = get_yolo_boxes(infer_model, images, net_h, net_w, config['model']['anchors'], obj_thresh, nms_thresh)
if os.path.isdir(input_path):
for inp_file in os.listdir(input_path):
image_paths += [input_path + inp_file]
else:
image_paths += [input_path]
for i in range(len(images)):
draw_boxes(images[i], batch_boxes[i], config['model']['labels'], obj_thresh)
cv2.imshow('video with bboxes', images[i])
images = []
if cv2.waitKey(1) == 27:
break # esc to quit
cv2.destroyAllWindows()
elif input_path[-4:] == '.mp4': # do detection on a video
video_out = output_path + input_path.split('/')[-1]
video_reader = cv2.VideoCapture(input_path)
image_paths = [inp_file for inp_file in image_paths if (inp_file[-4:] in ['.jpg', '.png', 'JPEG'])]
nb_frames = int(video_reader.get(cv2.CAP_PROP_FRAME_COUNT))
frame_h = int(video_reader.get(cv2.CAP_PROP_FRAME_HEIGHT))
frame_w = int(video_reader.get(cv2.CAP_PROP_FRAME_WIDTH))
# the main loop
times = []
images = [cv2.imread(image_path) for image_path in image_paths]
video_writer = cv2.VideoWriter(video_out,
cv2.VideoWriter_fourcc(*'MPEG'),
50.0,
(frame_w, frame_h))
# the main loop
batch_size = 1
images = []
start_point = 0 #%
show_window = False
for i in tqdm(range(nb_frames)):
_, image = video_reader.read()
#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]
if (float(i+1)/nb_frames) > start_point/100.:
images += [image]
print('Elapsed time = {}'.format(time.time() - start))
times.append(time.time() - start)
if (i%batch_size == 0) or (i == (nb_frames-1) and len(images) > 0):
# predict the bounding boxes
batch_boxes = get_yolo_boxes(infer_model, images, net_h, net_w, config['model']['anchors'], obj_thresh, nms_thresh)
boxes_disc = [disconnect(image, boxes_image, z_thresh = 1.8) for image, boxes_image in zip(images, boxes)]
for i in range(len(images)):
# draw bounding boxes on the image using labels
draw_boxes(images[i], batch_boxes[i], config['model']['labels'], obj_thresh)
for image_path, image, boxes_image in zip(image_paths, images, boxes_disc):
# show the video with detection bounding boxes
if show_window: cv2.imshow('video with bboxes', images[i])
#print(boxes_image[0].score)
# draw bounding boxes on the image using labels
# write result to the output video
video_writer.write(images[i])
images = []
draw_boxes(image, boxes_image, ["disconnect"], 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))
if show_window and cv2.waitKey(1) == 27: break # esc to quit
if show_window: cv2.destroyAllWindows()
video_reader.release()
video_writer.release()
else: # do detection on an image or a set of images
image_paths = []
if os.path.isdir(input_path):
for inp_file in os.listdir(input_path):
image_paths += [input_path + inp_file]
else:
image_paths += [input_path]
image_paths = [inp_file for inp_file in image_paths if (inp_file[-4:] in ['.jpg', '.png', 'JPEG'])]
# the main loop
times = []
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):
# draw bounding boxes on the image using labels
I = image.copy()
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))
file = open(args.output + '/time.txt','w')
file.write('Tiempo promedio:' + str(np.mean(times)))
file.close()
file = open(args.output + '/time.txt','w')
file.write('Tiempo promedio:' + str(np.mean(times)))
file.close()
if __name__ == '__main__':
argparser = argparse.ArgumentParser(description='Predict with a trained yolo model')