def main(frame_path, dataset_name):
print(frame_path)
filename, f_type = getBaseName(frame_path)
createFolder('img')
createFolder('img/out-simple')
createFolder('img/out-simple/' + dataset_name)
save_path = 'img/out/' + dataset_name + '/simple/'
createFolder(save_path)
img = cv2.imread(frame_path, 1)
h, w, c = img.shape
[b, g, r] = cv2.split(img)
img_hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
img_H = img_hsv[:, :, 0]
img_S = img_hsv[:, :, 1]
img_V = img_hsv[:, :, 2]
img_color_intensity = color_intensity(img)
### Light Detection
img_gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) / 255.0
ret, img_gray = cv2.threshold(img_gray, 0.4, 1, cv2.THRESH_TOZERO)
#ret , img_gray_th = cv2.threshold(img_gray, 245/255, 1, cv2.THRESH_TOZERO)
#ret, img_gray_th = cv2.threshold(img_gray, 245/255, 1, cv2.THRESH_TOZERO)
#ret, img_color_intensity_th = cv2.threshold(img_color_intensity, 0.2, 1, cv2.THRESH_TOZERO)
img_nakagami = Nakagami_image_enhancement(img_gray, 5) / 255.0
img_nakagami_c = Nakagami_image_enhancement(img_color_intensity, 5) / 255.0
ret, img_nakagami_norm_th = cv2.threshold(img_nakagami, 240 / 255, 1,
cv2.THRESH_TOZERO)
ret, img_nakagami_c_th = cv2.threshold(img_nakagami_c, 240 / 255, 1,
cv2.THRESH_TOZERO)
pipline = [
'img', 'img_H', 'img_S', 'img_V', 'img_gray', 'img_color_intensity',
'img_nakagami', 'img_nakagami_c', 'img_nakagami_norm_th',
'img_nakagami_c_th'
]
imgs = [
img, img_H, img_S, img_V, img_gray, img_color_intensity, img_nakagami,
img_nakagami_c, img_nakagami_norm_th, img_nakagami_c_th
]
fig = plt.figure(figsize=(32, 64))
for i in range(len(imgs)):
ax = fig.add_subplot(len(imgs) // 2 + 1, 2, i + 1)
img_tmp = BGR_to_RGB(imgs[i])
ax.set_title(pipline[i])
if pipline[i] in ['img_gray_th_', 'img_nakagami_']:
plt.imshow(img_tmp)
plt.colorbar()
elif len(img_tmp.shape) == 2:
plt.imshow(img_tmp, cmap='gray')
else:
plt.imshow(img_tmp)
plt.savefig(save_path + dataset_name + '_' + filename + '.png', dpi=200)
return -1, -1
def main(frame_path):
filename, f_type = getBaseName(frame_path)
createFolder('img')
createFolder('img/out')
createFolder('img/out/' + filename)
save_path = 'img/out/' + filename + '/'
img = cv2.imread(frame_path, 1)
h, w, c = img.shape
[b, g, r] = cv2.split(img)
img_red_filted, idx_red = Euclidean_filter(img, 1, 150, 0, [255, 0, 0],
[b, g, r], save_path)
img_white_filted, idx_white = Euclidean_filter(img, 1, 255, 7,
[255, 255, 255], [b, g, r],
save_path)
img_symbolic_idx, img_symbolic_show = symbolic_image([idx_red, idx_white],
(h, w))
img_light_b = np.where(img_symbolic_idx > 0, 255, 0).astype('uint8')
img_ccl_origin, img_ccl_show, label_num = ccl(img_light_b)
img_box, boxes = find_BoundingBox(img_ccl_origin, img)
"""
cv2.imshow("img_ccl", img_ccl)
cv2.waitKey(0)
cv2.destroyAllWindows()
"""
#print(frame_path,label_num, img_ccl_origin.shape)
if save:
cv2.imwrite('img/out/' + filename + '/' + filename + '_origin.png',
img)
cv2.imwrite(
'img/out/' + filename + '/' + filename + '_img_red_filted.png',
img_red_filted)
cv2.imwrite(
'img/out/' + filename + '/' + filename + '_img_white_filted.png',
img_white_filted)
cv2.imwrite(
'img/out/' + filename + '/' + filename + '_img_symbolic.png',
img_symbolic_show)
cv2.imwrite(
'img/out/' + filename + '/' + filename + '_img_light_b.png',
img_light_b)
cv2.imwrite('img/out/' + filename + '/' + filename + '_img_ccl.png',
img_ccl_show)
cv2.imwrite('img/out/' + filename + '/' + filename + '_img_box.png',
img_box)
def make_training_data(base, frame_path, dataset_name):
print(frame_path)
filename, f_type = getBaseName(frame_path)
img = cv2.imread(base + dataset + "/" + filename + '.bmp',
1).astype('uint8')
img_ground = cv2.imread(base + "ground_truth/" + filename + '.bmp',
1).astype('uint8')
img_yolo_b = cv2.imread(base + "yolo_binary/" + filename + '.png',
0).astype('uint8')
img_hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
img_hsv[:, :, 0] = img_hsv[:, :, 0] / img_hsv[:, :, 0].max() * 255.0
img_hsv[:, :, 0] = img_hsv[:, :, 1] / img_hsv[:, :, 1].max() * 255.0
img_hsv[:, :, 0] = img_hsv[:, :, 2] / img_hsv[:, :, 2].max() * 255.0
img_H = img_hsv[:, :, 0]
img_S = img_hsv[:, :, 1]
img_V = img_hsv[:, :, 2]
img_ground_mask = binary_color_filter(img_ground).astype('uint8')
img_ccl_origin, img_ccl_show, label_num = ccl(img_ground_mask)
img_box, boxes = find_BoundingBox(img_ccl_origin, img)
features = []
answers = []
for b in boxes:
x = b[0]
y = b[1]
w = b[2] - b[0]
h = b[3] - b[1]
### make feature
yolo_and_edgebox = img_yolo_b[y:y + h, x:x + w]
area_yolo_and_edgebox = (yolo_and_edgebox // 255).sum()
ROI_combine = area_yolo_and_edgebox / (w * h)
center_line = img_S[y + h // 2, x:x + w]
ROI_center_std = np.std(center_line)
ROI_center_min = np.amin(center_line)
ROI_center_area = w * h
ROI_height = y
feature = [
filename, ROI_combine, ROI_center_min, ROI_center_std, ROI_height,
ROI_center_area, [x, y, w, h]
]
#print(feature)
### make answer
yolo_and_ground = np.bitwise_and(img_ground_mask[y:y + h, x:x + w],
img_yolo_b[y:y + h, x:x + w])
area_yolo_and_ground = (yolo_and_ground // 255).sum()
img_ROI = img_ground[y:y + h, x:x + w]
t, ct = np.unique(img_ROI.reshape(-1, img_ROI.shape[2]),
axis=0,
return_counts=True)
#print(t,ct)
answer = ""
#if len(t)==1:
t = t.tolist()
idx, = np.where(ct == ct.max())
if t[int(idx)] in [[0, 0, 255], [0, 0, 0]]:
answer = 0
else:
answer = 1
if answer not in [0, 1]:
if t in [[0, 0, 255], [0, 0, 0]]:
answer = 0
else:
answer = 1
cv2.imshow("img_ROI", img_ROI)
cv2.waitKey(0)
cv2.destroyAllWindows()
"""
print("iou:", ROI_combine, "answer:", answer)
#cv2.imshow("img_ground", img_ground)
cv2.imshow("img_ROI", img_ROI)
cv2.waitKey(0)
cv2.destroyAllWindows()
"""
features.append(feature)
answers.append(answer)
#print(boxes)
"""
cv2.imshow("img_ground", img_box)
cv2.imshow("img_ground_mask", img_ground_mask)
cv2.waitKey(0)
cv2.destroyAllWindows()
"""
return features, answers
def main(frame_path, dataset_name):
print(frame_path)
filename, f_type = getBaseName(frame_path)
#createFolder('img/out/'+dataset_name+'/'+filename)
#save_path = 'img/out/'+dataset_name+'/'+filename+'/'
img = cv2.imread(frame_path, 1)
h, w, c = img.shape
[b,g,r] = cv2.split(img)
img_hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
img_hsv[:,:,0] = img_hsv[:,:,0] / img_hsv[:,:,0].max() * 255.0
img_hsv[:,:,0] = img_hsv[:,:,1] / img_hsv[:,:,1].max() * 255.0
img_hsv[:,:,0] = img_hsv[:,:,2] / img_hsv[:,:,2].max() * 255.0
img_H = img_hsv[:,:,0]
img_S = img_hsv[:,:,1]
img_V = img_hsv[:,:,2]
### Light Detection
img_gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) / 255.0
ret , img_gray_th = cv2.threshold(img_gray, 0.4, 1, cv2.THRESH_TOZERO)
if nakagami:
img_nakagami = Nakagami_image_enhancement(img_gray_th, 3)
else:
img_nakagami = img_gray_th
img_nakagami_norm = img_nakagami/img_nakagami.max() # resize to [0,1]
ret, img_nakagami_norm_th = cv2.threshold(img_nakagami_norm, 0.88, 1, cv2.THRESH_TOZERO)
### Area filter
thresh = (img_nakagami_norm_th * 255.0).astype('uint8')
img_ccl_origin, img_ccl_show, label_nums = ccl(thresh) ### need imwrite
"""
cv2.imshow("thresh", thresh)
cv2.imshow("opening", img_ccl_show)
#cv2.imshow("sure_bg", sure_bg)
cv2.waitKey(0)
cv2.destroyAllWindows()
"""
"""
for (i, label) in enumerate(np.unique(img_ccl_origin)):
if label == 0: #background
continue
else:
labelMask = np.zeros(img_nakagami_norm_th_tmp.shape, dtype="uint8")
labelMask[labels == label] = 255
numPixels = cv2.countNonZero(labelMask)
"""
### clip center
img_nakagami_norm_th_clip = clip_center(img_nakagami_norm_th, int(h/3), int(h))
#print(img_nakagami_norm_th_center.max())
#print(img_nakagami_norm_th.max())
### Color filter
#img_white_filted, idx_white, img_white_d = Euclidean_filter(img=img, threshold=15, color=[255,255,255], img_BGR_spilt=[b,g,r], save_path=save_path)
ret , img_white_filted = cv2.threshold(img_gray, 245/255.0, 1, cv2.THRESH_TOZERO)
img_white_filted = img_white_filted*255.0
#img_red_filted, idx_red, img_red_d = Euclidean_filter(img=img, threshold=150, color=[255,50,150], img_BGR_spilt=[b,g,r], save_path=save_path)
lower_red = np.array([0,100,0])
upper_red = np.array([5,255,255])
mask0 = cv2.inRange(img_hsv, lower_red, upper_red)
lower_red = np.array([170,100,0])
upper_red = np.array([179,255,255])
mask1 = cv2.inRange(img_hsv, lower_red, upper_red)
mask = mask0 + mask1
img_red_filted = img.copy()
img_red_filted[np.where(mask==0)] = 0
"""
cv2.imshow("mask0", mask0)
cv2.imshow("mask1", mask1)
cv2.imshow("mask", mask)
#cv2.imshow("sure_bg", sure_bg)
cv2.waitKey(0)
cv2.destroyAllWindows()
"""
###### 形態學操作
#img_white_filted_gray = cv2.cvtColor(img_white_filted, cv2.COLOR_BGR2GRAY)
img_white_filted_gray = img_white_filted
k=np.ones((3,3), np.uint8)
img_white_mor = cv2.morphologyEx(img_white_filted_gray,cv2.MORPH_CLOSE, k,iterations=3) / 255.0
img_red_filted_gray = cv2.cvtColor(img_red_filted, cv2.COLOR_BGR2GRAY)
ret , img_red_filted_gray_th = cv2.threshold(img_red_filted_gray, 150, 255, cv2.THRESH_TOZERO)
k=np.ones((13,13), np.uint8)
#k=cv2.getStructuringElement(cv2.MORPH_ELLIPSE,(13,13))
img_red_mor = cv2.morphologyEx(img_red_filted_gray,cv2.MORPH_CLOSE, k,iterations=5)
ret, img_red_mor_th = cv2.threshold(img_red_mor, 80, 255, cv2.THRESH_TOZERO)
### Multiply color and light
img_white_multiply = img_nakagami_norm_th_clip * img_white_mor
img_red_multiply = img_nakagami_norm_th_clip * img_red_mor_th
ret, img_white_b = cv2.threshold(img_white_multiply, 0.5, 255, cv2.THRESH_BINARY)
ret, img_red_b = cv2.threshold(img_red_multiply, 0.5, 255, cv2.THRESH_BINARY)
img_white_light = img_white_b - img_red_b
img_red_light = img_red_b
img_white_light = np.clip(img_white_light,0,255)
img_red_light = np.clip(img_red_light,0,255)
### Contour
img_red_contour = contour_Detection(img_red_light)
img_white_contour = contour_Detection(img_white_light)
### EdgeBox
img_roi_combine = np.copy(img)
img_red_edgeboxes,img_roi_combine, boxes_red = Edgeboxes(img_gray=img_red_contour, img_origin=img, color=[0,255,0], img_roi_combine=img_roi_combine, state=state, filename=filename, base=base) #contour, img_origin, box color(BGR)
img_white_edgeboxes,img_roi_combine, boxes_white = Edgeboxes(img_gray=img_white_contour, img_origin=img, color=[255,0,0], img_roi_combine=img_red_edgeboxes, state=state, filename=filename, base=base) #contour, img_origin, box color(BGR)
### Fusion
### fusion ###
img_ground = cv2.imread(base+"ground_truth/"+filename+'.bmp',1).astype('uint8')
img_ground_mask = binary_color_filter(img_ground).astype('uint8')
img_yolo_b = cv2.imread(base+"yolo_binary/"+filename+'.png',0).astype('uint8')
features_red, answers_red = make_feature(boxes_red, img_ground=img_ground, img_ground_mask=img_ground_mask, state=state, img_H=img_H, img_yolo_b=img_yolo_b, filename=filename)
features_white, answers_white = make_feature(boxes_white, img_ground=img_ground, img_ground_mask=img_ground_mask, state=state, img_H=img_H, img_yolo_b=img_yolo_b, filename=filename)
features = features_red + features_white
answers = answers_red + answers_white
imgs = [
img, img_hsv, img_H, img_S, img_V, img_gray*255.0,
img_gray_th*255.0, img_nakagami_norm*255.0, img_nakagami_norm_th*255.0, img_nakagami_norm_th_clip*255.0,
img_white_filted, img_white_filted_gray, img_white_mor*255.0, img_white_multiply*255.0,
img_red_filted, img_red_filted_gray, img_red_filted_gray_th, img_red_mor,
img_red_mor_th, img_red_multiply, img_white_b, img_red_b,
img_white_light, img_red_light, img_red_contour, img_white_contour,
img_red_edgeboxes, img_white_edgeboxes, img_roi_combine
]
for i in range(len(names)):
save_path = 'img/out/'+dataset_name+'/'+names[i]+'/'+filename
try:
cv2.imwrite(save_path+'.png', imgs[i])
except:
print('error on ', names[i])
#img_white_edgeboxes,img_roi_combine,
#cv2.imwrite(save_path+'_img_nakagami_thB.png', img_nakagami_thB*255.0)
#img_white_filted, idx_white, img_white_d = Euclidean_filter(img=img, threshold=20, color=[255,255,255], img_BGR_spilt=[b,g,r], save_path=save_path)
"""
cv2.imshow("img_nakagami", img_gray)
cv2.waitKey(0)
cv2.destroyAllWindows()
"""
"""
### 形態學操作
#r1=cv2.morphologyEx(img_red,cv2.MORPH_CLOSE, k,iterations=3)
#k=np.ones((3,3), np.uint8)
k=cv2.getStructuringElement(cv2.MORPH_ELLIPSE,(5,5))
img_mor = cv2.morphologyEx(img_red, cv2.MORPH_CLOSE, k,iterations=3)
img_ccl_origin, img_ccl_show, label_num = ccl(img_mor)
img_red_box, red_boxes = find_BoundingBox(img_ccl_origin, img)
"""
"""
cv2.imshow("img_red_filled", img_box)
cv2.waitKey(0)
cv2.destroyAllWindows()
"""
"""
cv2.imshow("img_red", img_mor)
cv2.waitKey(0)
cv2.destroyAllWindows()
"""
"""
h, w, c = img.shape
img_hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
img_H = img_hsv[:,:,0]
img_S = img_hsv[:,:,1]
img_V = img_hsv[:,:,2]
[b,g,r] = cv2.split(img)
### image enhacement
img_th = color_intensity(img)
img_nakagami = Nakagami_image_enhancement(img_th, 3)
ret, img_nakagami_thB = cv2.threshold(img_nakagami, 100, 1, cv2.THRESH_BINARY)
img_red_filted, idx_red = Euclidean_filter(img, 1,150,0,[255,0,0], [b,g,r], save_path) #img, percent,up_threshold,lower_threshold color(RGB), save_path
img_white_filted,idx_white = Euclidean_filter(img, 1,255,7, [255,255,255], [b,g,r], save_path) #img, percent, color(RGB), save_path
img_red_filted_gray = cv2.cvtColor(img_red_filted, cv2.COLOR_BGR2GRAY)
img_white_filted_gray = cv2.cvtColor(img_white_filted, cv2.COLOR_BGR2GRAY)
img_red_filted_gray_max = img_red_filted_gray.max()
img_white_filted_gray_max = img_white_filted_gray.max()
img_red_filted_gray_norm = img_red_filted_gray/img_red_filted_gray_max
img_white_filted_gray_norm = img_white_filted_gray/img_white_filted_gray_max
img_red_multiply = img_nakagami_thB * img_red_filted_gray_norm
img_white_multiply = img_nakagami_thB * img_white_filted_gray_norm
img_red_multiply = img_red_multiply*255.0
img_white_multiply = img_white_multiply*255.0
#img_red_nakagami = Nakagami_image_enhancement(img_red_filted_gray_norm, 3) #gray_img, kernel_size
#img_white_nakagami = Nakagami_image_enhancement(img_white_filted_gray_norm, 3) #gray_img, kernel_size
img_red_nakagami_cliped = clip_center(img_red_multiply, int(h/3), int(h)), # img, y_up, y_down
img_white_nakagami_cliped = clip_center(img_white_multiply, int(h/3), int(h)), # img, y_up, y_down
img_red_nakagami_cliped = img_red_nakagami_cliped[0]
img_white_nakagami_cliped = img_white_nakagami_cliped[0]
img_red_contour = contour_Detection(img_red_nakagami_cliped)
img_white_contour = contour_Detection(img_white_nakagami_cliped)
#print(type(img_red_contour_cliped[0]))
"""
"""
cv2.imshow("img_red_contour_cliped", img_red_contour_cliped)
#cv2.imshow("img_white_filted_gray", img_white_filted_gray)
#cv2.imshow("img_red_nakagami", img_red_nakagami)
cv2.waitKey(0)
cv2.destroyAllWindows()
"""
"""
img_roi_combine = np.copy(img)
img_red_edgeboxes,img_roi_combine, boxes_red = Edgeboxes(img_red_contour, img, [0,255,0], img_roi_combine, state=state, filename=filename, base=base) #contour, img_origin, box color(BGR)
img_white_edgeboxes,img_roi_combine, boxes_white = Edgeboxes(img_white_contour, img, [255,0,0], img_red_edgeboxes, state=state, filename=filename, base=base) #contour, img_origin, box color(BGR)
### fusion ###
img_ground = cv2.imread(base+"ground_truth/"+filename+'.bmp',1).astype('uint8')
img_ground_mask = binary_color_filter(img_ground).astype('uint8')
img_yolo_b = cv2.imread(base+"yolo_binary/"+filename+'.png',0).astype('uint8')
features_red, answers_red = make_feature(boxes_red, img_ground=img_ground, img_ground_mask=img_ground_mask, state=state, img_H=img_H, img_yolo_b=img_yolo_b, filename=filename)
features_white, answers_white = make_feature(boxes_white, img_ground=img_ground, img_ground_mask=img_ground_mask, state=state, img_H=img_H, img_yolo_b=img_yolo_b, filename=filename)
features = features_red + features_white
answers = answers_red + answers_white
### report ###
if save:
cv2.imwrite(save_path+'_origin.png', img)
cv2.imwrite(save_path+'_color_intensity_with_threshold.png', img_th*255.0)
cv2.imwrite(save_path+'_img_nakagami.png', img_nakagami)
cv2.imwrite(save_path+'_img_nakagami_thB.png', img_nakagami_thB*255.0)
cv2.imwrite(save_path+'_img_H.png', img_H)
cv2.imwrite(save_path+'_img_S.png', img_S)
cv2.imwrite(save_path+'_img_V.png', img_V)
cv2.imwrite(save_path+'_img_red_filted.png', img_red_filted)
cv2.imwrite(save_path+'_img_white_filted.png', img_white_filted)
cv2.imwrite(save_path+'_img_red_filted_gray.png', img_red_filted_gray)
#cv2.imwrite(save_path+'_img_red_nakagami.png', img_red_nakagami)
#cv2.imwrite(save_path+'_img_white_nakagami.png', img_white_nakagami)
cv2.imwrite(save_path+'_img_red_nakagami_cliped.png', img_red_nakagami_cliped)
cv2.imwrite(save_path+'_img_white_nakagami_cliped.png', img_white_nakagami_cliped)
cv2.imwrite(save_path+'_img_red_multiply.png', img_red_multiply)
cv2.imwrite(save_path+'_img_white_multiply.png', img_white_multiply)
cv2.imwrite(save_path+'_img_red_contour.png', img_red_contour)
cv2.imwrite(save_path+'_img_white_contour.png', img_white_contour)
cv2.imwrite(save_path+'_img_red_edgeboxes.png', img_red_edgeboxes)
cv2.imwrite(save_path+'_img_white_edgeboxes.png', img_white_edgeboxes)
cv2.imwrite(save_path+'_img_roi_combine.png', img_roi_combine)
#cv2.imwrite(save_path+'_img_red.png', r)
return features, answers
"""
return features, answers
'img', 'img_result_light_mask/combine', 'img_result_light_mask/combine_rect', 'img_result_light_mask/light', 'img_result_light_mask/light_rect',
'img_result_light_mask/yolo', 'img_result_light_mask/yolo_rect']
#folder_names = ['img_result_light_mask/yolo']
for folder in folder_names:
image_path = '../img/demo-out/3/'+folder+'/'
files = glob.glob(image_path+'*.png')
i=0
filenames = []
for f in files:
filename = getBaseName(f)[0]
filenames.append(int(filename))
i+=1
print(folder, " has %d files"%i)
# batch process
batch = i//3000 + 1
batch_filenames = []
files = []
ct=0
for f in sorted(filenames):
files.append(int(f))
ct+=1
img = cv2.imread(image_path+'0.png')
h,w,c = img.shape
def main(frame_path, dataset_name, boxI=None):
print(frame_path)
filename, f_type = getBaseName(frame_path)
#createFolder('img/out/'+dataset_name+'/'+filename)
#save_path = 'img/out/'+dataset_name+'/'+filename+'/'
img = cv2.imread(frame_path, 1)
h, w, c = img.shape
[b, g, r] = cv2.split(img)
img_hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
img_hsv[:, :, 0] = img_hsv[:, :, 0] / img_hsv[:, :, 0].max() * 255.0
img_hsv[:, :, 0] = img_hsv[:, :, 1] / img_hsv[:, :, 1].max() * 255.0
img_hsv[:, :, 0] = img_hsv[:, :, 2] / img_hsv[:, :, 2].max() * 255.0
img_H = img_hsv[:, :, 0]
img_S = img_hsv[:, :, 1]
img_V = img_hsv[:, :, 2]
### Light Detection
img_gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) / 255.0
ret, img_gray_th = cv2.threshold(img_gray, 0.4, 1, cv2.THRESH_TOZERO)
img_sigmoid = img_gray * 255.0
img_sigmoid = 1 / (1 + np.exp(-(img_gray - 128) / 20))
print(img_sigmoid.min(), img_sigmoid.max())
d = img_sigmoid.max() - img_sigmoid.min()
img_sigmoid = 255 * (img_sigmoid - img_sigmoid.min()) / d
print(img_sigmoid.min(), img_sigmoid.max())
ret, img_sigmoid_th = cv2.threshold(img_sigmoid, 245, 255,
cv2.THRESH_TOZERO)
createFolder('img/out/' + dataset_name + '/' + "img_test_sigmoid")
createFolder('img/out/' + dataset_name + '/' + "img_test_sigmoid_th")
cv2.imwrite(
'img/out/' + dataset_name + '/' + "img_test_sigmoid" + '/' + filename +
'.png', img_sigmoid)
cv2.imwrite(
'img/out/' + dataset_name + '/' + "img_test_sigmoid_th" + '/' +
filename + '.png', img_sigmoid_th)
if nakagami:
img_nakagami = Nakagami_image_enhancement(img_gray_th, 3)
else:
img_nakagami = img_gray_th
img_nakagami_norm = img_nakagami / img_nakagami.max() # resize to [0,1]
ret, img_nakagami_norm_th = cv2.threshold(img_nakagami_norm, 200 / 255, 1,
cv2.THRESH_TOZERO)
### clip center
img_nakagami_norm_th_clip = clip_center(img_nakagami_norm_th, int(h / 3),
int(h))
img_white_filted = img_nakagami_norm_th_clip * 255.0
#ret , img_white_filted = cv2.threshold(img_nakagami_norm_th_clip, 245/255.0, 1, cv2.THRESH_TOZERO)
#img_white_filted = img_white_filted*255.0
k = np.ones((3, 3), np.uint8)
img_white_mor = cv2.morphologyEx(
img_white_filted, cv2.MORPH_CLOSE, k, iterations=3) / 255.0
ret, img_white_b = cv2.threshold(img_white_mor, 0.9, 255,
cv2.THRESH_BINARY)
### Contour
img_white_contour = contour_Detection(img_white_b)
### EdgeBox
img_roi_combine = np.copy(img)
#img_red_edgeboxes,img_roi_combine, boxes_red = Edgeboxes(img_gray=img_red_contour, img_origin=img, color=[0,255,0], img_roi_combine=img_roi_combine, state=state, filename=filename, base=base) #contour, img_origin, box color(BGR)
img_white_edgeboxes, img_roi_combine, boxes_white = Edgeboxes(
img_gray=img_white_contour,
img_origin=img,
color=[255, 0, 0],
img_roi_combine=img_roi_combine,
state=state,
filename=filename,
base=base) #contour, img_origin, box color(BGR)
### Fusion
### fusion ###
img_ground = cv2.imread(base + "ground_truth/" + filename + '.bmp',
1).astype('uint8')
img_ground_mask = binary_color_filter(img_ground).astype('uint8')
img_yolo_b = cv2.imread(base + "yolo_binary/" + filename + '.png',
0).astype('uint8')
features_white, answers_white, bI = make_feature(
boxes=boxes_white,
version='v7-hand',
img_ground=img_ground,
img_ground_mask=img_ground_mask,
state=state,
img_S=img_S,
img_yolo_b=img_yolo_b,
filename=filename,
img=img,
boxI=boxI)
features = features_white
answers = answers_white
imgs = [
img, img_hsv, img_H, img_S, img_V, img_gray * 255.0,
img_gray_th * 255.0, img_nakagami_norm * 255.0,
img_nakagami_norm_th * 255.0, img_nakagami_norm_th_clip * 255.0,
img_white_filted, img_white_mor * 255.0, img_white_contour,
img_white_edgeboxes, img_roi_combine
]
for i in range(len(names)):
save_path = 'img/out/' + dataset_name + '/' + names[i] + '/' + filename
try:
cv2.imwrite(save_path + '.png', imgs[i])
except:
print('error on ', names[i])
#img_white_edgeboxes,img_roi_combine,
#cv2.imwrite(save_path+'_img_nakagami_thB.png', img_nakagami_thB*255.0)
#img_white_filted, idx_white, img_white_d = Euclidean_filter(img=img, threshold=20, color=[255,255,255], img_BGR_spilt=[b,g,r], save_path=save_path)
"""
cv2.imshow("img_nakagami", img_gray)
cv2.waitKey(0)
cv2.destroyAllWindows()
"""
return features, answers, bI
def main(frame_path, dataset_name):
print(frame_path)
filename, f_type = getBaseName(frame_path)
createFolder('img')
createFolder('img/out')
createFolder('img/out/'+dataset_name)
createFolder('img/out/'+dataset_name+'/'+filename)
save_path = 'img/out/'+dataset_name+'/'+filename+'/'
img = cv2.imread(frame_path, 1)
h, w, c = img.shape
img_hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
img_H = img_hsv[:,:,0]
img_S = img_hsv[:,:,1]
img_V = img_hsv[:,:,2]
[b,g,r] = cv2.split(img)
### image enhacement
img_th = color_intensity(img)
img_nakagami = Nakagami_image_enhancement(img_th, 3)
ret, img_nakagami_thB = cv2.threshold(img_nakagami, 100, 1, cv2.THRESH_BINARY)
img_red_filted, idx_red = Euclidean_filter(img, 1,150,0,[255,0,0], [b,g,r], save_path) #img, percent,up_threshold,lower_threshold color(RGB), save_path
img_white_filted,idx_white = Euclidean_filter(img, 1,255,7, [255,255,255], [b,g,r], save_path) #img, percent, color(RGB), save_path
img_red_filted_gray = cv2.cvtColor(img_red_filted, cv2.COLOR_BGR2GRAY)
img_white_filted_gray = cv2.cvtColor(img_white_filted, cv2.COLOR_BGR2GRAY)
img_red_filted_gray_max = img_red_filted_gray.max()
img_white_filted_gray_max = img_white_filted_gray.max()
img_red_filted_gray_norm = img_red_filted_gray/img_red_filted_gray_max
img_white_filted_gray_norm = img_white_filted_gray/img_white_filted_gray_max
img_red_multiply = img_nakagami_thB * img_red_filted_gray_norm
img_white_multiply = img_nakagami_thB * img_white_filted_gray_norm
img_red_multiply = img_red_multiply*255.0
img_white_multiply = img_white_multiply*255.0
#img_red_nakagami = Nakagami_image_enhancement(img_red_filted_gray_norm, 3) #gray_img, kernel_size
#img_white_nakagami = Nakagami_image_enhancement(img_white_filted_gray_norm, 3) #gray_img, kernel_size
img_red_nakagami_cliped = clip_center(img_red_multiply, int(h/3), int(h)), # img, y_up, y_down
img_white_nakagami_cliped = clip_center(img_white_multiply, int(h/3), int(h)), # img, y_up, y_down
img_red_nakagami_cliped = img_red_nakagami_cliped[0]
img_white_nakagami_cliped = img_white_nakagami_cliped[0]
img_red_contour = contour_Detection(img_red_nakagami_cliped)
img_white_contour = contour_Detection(img_white_nakagami_cliped)
#print(type(img_red_contour_cliped[0]))
"""
cv2.imshow("img_red_contour_cliped", img_red_contour_cliped)
#cv2.imshow("img_white_filted_gray", img_white_filted_gray)
#cv2.imshow("img_red_nakagami", img_red_nakagami)
cv2.waitKey(0)
cv2.destroyAllWindows()
"""
img_roi_combine = np.copy(img)
img_red_edgeboxes,img_roi_combine, boxes_red = Edgeboxes(img_red_contour, img, [0,255,0], img_roi_combine, state=state, filename=filename, base=base) #contour, img_origin, box color(BGR)
img_white_edgeboxes,img_roi_combine, boxes_white = Edgeboxes(img_white_contour, img, [255,0,0], img_red_edgeboxes, state=state, filename=filename, base=base) #contour, img_origin, box color(BGR)
### fusion ###
img_ground = cv2.imread(base+"ground_truth/"+filename+'.bmp',1).astype('uint8')
img_ground_mask = binary_color_filter(img_ground).astype('uint8')
img_yolo_b = cv2.imread(base+"yolo_binary/"+filename+'.png',0).astype('uint8')
features_red, answers_red = make_feature(boxes_red, img_ground=img_ground, img_ground_mask=img_ground_mask, state=state, img_H=img_H, img_yolo_b=img_yolo_b, filename=filename)
features_white, answers_white = make_feature(boxes_white, img_ground=img_ground, img_ground_mask=img_ground_mask, state=state, img_H=img_H, img_yolo_b=img_yolo_b, filename=filename)
features = features_red + features_white
answers = answers_red + answers_white
### report ###
if save:
cv2.imwrite(save_path+'_origin.png', img)
cv2.imwrite(save_path+'_color_intensity_with_threshold.png', img_th*255.0)
cv2.imwrite(save_path+'_img_nakagami.png', img_nakagami)
cv2.imwrite(save_path+'_img_nakagami_thB.png', img_nakagami_thB*255.0)
cv2.imwrite(save_path+'_img_H.png', img_H)
cv2.imwrite(save_path+'_img_S.png', img_S)
cv2.imwrite(save_path+'_img_V.png', img_V)
cv2.imwrite(save_path+'_img_red_filted.png', img_red_filted)
cv2.imwrite(save_path+'_img_white_filted.png', img_white_filted)
cv2.imwrite(save_path+'_img_red_filted_gray.png', img_red_filted_gray)
#cv2.imwrite(save_path+'_img_red_nakagami.png', img_red_nakagami)
#cv2.imwrite(save_path+'_img_white_nakagami.png', img_white_nakagami)
cv2.imwrite(save_path+'_img_red_nakagami_cliped.png', img_red_nakagami_cliped)
cv2.imwrite(save_path+'_img_white_nakagami_cliped.png', img_white_nakagami_cliped)
cv2.imwrite(save_path+'_img_red_multiply.png', img_red_multiply)
cv2.imwrite(save_path+'_img_white_multiply.png', img_white_multiply)
cv2.imwrite(save_path+'_img_red_contour.png', img_red_contour)
cv2.imwrite(save_path+'_img_white_contour.png', img_white_contour)
cv2.imwrite(save_path+'_img_red_edgeboxes.png', img_red_edgeboxes)
cv2.imwrite(save_path+'_img_white_edgeboxes.png', img_white_edgeboxes)
cv2.imwrite(save_path+'_img_roi_combine.png', img_roi_combine)
#cv2.imwrite(save_path+'_img_red.png', r)
return features, answers
def main(frame_name):
features = ""
print(frame_name)
filename, f_type = getBaseName(frame_name)
img = cv2.imread(frame_name, 1)
h, w, c = img.shape
[b, g, r] = cv2.split(img)
img_hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
img_hsv[:, :, 0] = img_hsv[:, :, 0] / img_hsv[:, :, 0].max() * 255.0
img_hsv[:, :, 0] = img_hsv[:, :, 1] / img_hsv[:, :, 1].max() * 255.0
img_hsv[:, :, 0] = img_hsv[:, :, 2] / img_hsv[:, :, 2].max() * 255.0
img_H = img_hsv[:, :, 0]
img_S = img_hsv[:, :, 1]
img_V = img_hsv[:, :, 2]
### Light Detection
img_gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) / 255.0
ret, img_gray_th = cv2.threshold(img_gray, 0.4, 1, cv2.THRESH_TOZERO)
if nakagami:
img_nakagami = Nakagami_image_enhancement(img_gray_th, 3)
else:
img_nakagami = img_gray_th
img_nakagami_norm = img_nakagami / img_nakagami.max() # resize to [0,1]
ret, img_nakagami_norm_th = cv2.threshold(img_nakagami_norm, 200 / 255, 1,
cv2.THRESH_TOZERO)
### clip center
img_nakagami_norm_th_clip = clip_center(img_nakagami_norm_th, int(h / 3),
int(h))
img_white_filted = img_nakagami_norm_th_clip * 255.0
#ret, img_white_filted = cv2.threshold(img_white_filted, 200, 255, cv2.THRESH_TOZERO)
#ret , img_white_filted = cv2.threshold(img_nakagami_norm_th_clip, 245/255.0, 1, cv2.THRESH_TOZERO)
#img_white_filted = img_white_filted*255.0
k = np.ones((3, 3), np.uint8)
img_white_mor = cv2.morphologyEx(
img_white_filted, cv2.MORPH_CLOSE, k, iterations=3) / 255.0
ret, img_white_b = cv2.threshold(img_white_mor, 0.9, 255,
cv2.THRESH_BINARY)
### Contour
img_white_contour = contour_Detection(img_white_b)
### EdgeBox
img_roi_combine = np.copy(img)
#img_red_edgeboxes,img_roi_combine, boxes_red = Edgeboxes(img_gray=img_red_contour, img_origin=img, color=[0,255,0], img_roi_combine=img_roi_combine, state=state, filename=filename, base=base) #contour, img_origin, box color(BGR)
img_white_edgeboxes, img_roi_combine, boxes_white = Edgeboxes(
img_gray=img_white_contour,
img_origin=img,
color=[255, 0, 0],
img_roi_combine=img_roi_combine,
state=state,
filename=filename,
base=None) #contour, img_origin, box color(BGR)
img_yolo_b = cv2.imread(yolo_b_path + filename + '.png', 0).astype('uint8')
features_white = make_feature(boxes=boxes_white,
version='v6',
img_ground=None,
img_ground_mask=None,
state='test',
img_S=img_S,
img_yolo_b=img_yolo_b,
filename=filename)
features = features_white
### save files ###
imgs = [
img, img_hsv, img_H, img_S, img_V, img_gray * 255.0,
img_gray_th * 255.0, img_nakagami_norm * 255.0,
img_nakagami_norm_th * 255.0, img_nakagami_norm_th_clip * 255.0,
img_white_filted, img_white_mor * 255.0, img_white_b,
img_white_contour, img_white_edgeboxes, img_roi_combine
]
dataset_name = frame_name.split('/')[2]
for i in range(len(names)):
save_path = 'img/demo-out/' + dataset_name + '/' + names[
i] + '/' + filename
try:
cv2.imwrite(save_path + '.png', imgs[i])
except:
print('error on ', names[i])
return features
sys.path.insert(0, parentdir)
from utils.files import getBaseName, createFolder
try:
shutil.rmtree(
'/home/alanhc-school/Downloads/research/research-beta/architecture0714/img/demo-out'
)
except:
print('folder clean.')
files = glob.glob('../../../dataset/highway_video/*')
video_path = files[1]
print(files)
filename = getBaseName(video_path)[0]
createFolder('../img')
createFolder('../img/demo-out')
createFolder('../img/demo-out/' + filename)
createFolder('../img/demo-out/' + filename + '/origin')
save_path = '../img/demo-out/' + filename + '/'
cyclegan_path = '/home/alanhc-school/Desktop/CycleGAN-Tensorflow-2/'
vidcap = cv2.VideoCapture(video_path)
success, image = vidcap.read()
count = 0
while success:
try:
# save frame as JPEG file
success, image = vidcap.read()