def generate():
propcountarr = []
with open("/Data2TB/correctly_registered/S12/train/output.json") as f:
jsonprop = json.load(f)
png_files = [
f for f in listdir(depth_folder) if isfile(join(depth_folder, f))
]
for i in range(0, len(png_files)):
print(png_files[i])
image_name = png_files[i] #jsonprop["proposals"][i]["file"]
count = 0
for j in range(0, len(jsonprop)): #[i]["objects"])):
if (jsonprop[j]["file"] == image_name):
propcount = 0
for k in range(0, len(jsonprop[j]["objects"])):
propcount = propcount + 1
x = jsonprop[j]["objects"][k]["x"]
y = jsonprop[j]["objects"][k]["y"]
z = jsonprop[j]["objects"][k]["z"]
xmiw, ymiw, xmaw, ymaw = core_functions.get_bounding_box_WASSIMEA(
x, y, z)
prop_object = [xmiw, ymiw, xmaw, ymaw, y, x]
headpoint = [prop_object[4], prop_object[5]]
image, headpoint_new = core_functions.get_neg_roi(
depth_folder + image_name, prop_object, headpoint)
width, height = image.shape[1], image.shape[0]
if (width > 30 and height > 30):
c1, c2, c3, mod = core_functions.get_channels(
image, headpoint_new)
cv2.imwrite(
out_folder + image_name.replace(".png", "_id_") +
str(count) + ".jpg", mod)
count = count + 1
zabre = np.mean(propcountarr)
pingit = 1
def get_proposals(filename, jsonprop):
proparray = []
for im_object in jsonprop:
if im_object["file"] == filename:
for object_prop in im_object["objects"]:
x = object_prop["x"]
y = object_prop["y"]
z = object_prop["z"]
xmiw, ymiw, xmaw, ymaw = core_functions.get_bounding_box_WASSIMEA(
x, y, z)
proparray.append([xmiw, ymiw, xmaw, ymaw, y, x])
return proparray
def process_image():
with open("/home/wassimea/Desktop/wzebb.json") as f:
jsonprop = json.load(f)
filename = "/Data2TB/correctly_registered/S12/test/depth/video_fifth_2018-04-23_CAM1_1524500433782.png"
filename_only = "video_fifth_2018-04-23_CAM1_1524500433782.png"
png_image = cv2.imread(filename, -1)
for im_object in jsonprop["proposals"]:
if im_object["file"] == filename_only:
count = 0
for object_prop in im_object["objects"]:
x = object_prop["x"]
y = object_prop["y"]
z = object_prop["z"]
xmiw,ymiw,xmaw,ymaw = core_functions.get_bounding_box_WASSIMEA(x,y,z)
prop_object = [xmiw,ymiw,xmaw,ymaw,y,x]
headpoint = [y,x]
image, headpoint_new = core_functions.get_neg_roi(filename_only, prop_object, headpoint)
width,height = image.shape[1], image.shape[0]
c1,c2,c3,mod = core_functions.get_channels(image, headpoint_new)
cv2.imwrite("/home/wassimea/Desktop/tst_out/testing_2/" + filename_only.replace(".png", "_id_") + str(count) + ".jpg", mod)
count = count + 1
def generate():
propcountarr = []
with open(config.prop_json) as f:
jsonprop = json.load(f)
rgb_files = [f for f in listdir(rgb_folder) if isfile(join(rgb_folder, f))]
for i in range(0, len(rgb_files)):
#print(rgb_files[i])
image_name = rgb_files[i] #jsonprop["proposals"][i]["file"]
image_name_png = image_name.replace("jpg", "png")
count = 0
for j in range(0, len(jsonprop)): #[i]["objects"])):
if (jsonprop[j]["file"] == image_name_png):
propcount = 0
for k in range(0, len(jsonprop[j]["objects"])):
propcount = propcount + 1
x = jsonprop[j]["objects"][k]["x"]
y = jsonprop[j]["objects"][k]["y"]
z = jsonprop[j]["objects"][k]["z"]
xmiw, ymiw, xmaw, ymaw = core_functions.get_bounding_box_WASSIMEA(
x, y, z)
prop_object = [xmiw, ymiw, xmaw, ymaw, y, x]
headpoint = [prop_object[4], prop_object[5]]
image, headpoint_new = core_functions.get_neg_roi(
rgb_folder + image_name, prop_object, headpoint)
if config.mod == True:
image = mod.get_channels(image, headpoint_new)
cv2.normalize(image, image, 0, 255, cv2.NORM_MINMAX)
width, height = image.shape[1], image.shape[0]
if (width > 30 and height > 30):
#c1,c2,c3,mod = core_functions.get_channels(image, headpoint_new)
cv2.imwrite(
out_folder + image_name.replace(".jpg", "_id_") +
str(count) + ".jpg", image)
count = count + 1
zabre = np.mean(propcountarr)
pingit = 1
def annotate(
): #function similar to evaluate() in terms of principle, differs in that it draws the detected boxes and the ground truth boxes instead of calculating accuracies
with tf.device('/gpu:0'):
pingit = HeadClassifier()
depth_folder = "/Data2TB/correctly_registered/S12/test/depth/"
out_folder = "/home/wassimea/Desktop/SMATS/images/8bit/test_out/"
with open("/home/wassimea/Desktop/wzebb.json") as f:
jsonprop = json.load(f)
images = [
f for f in listdir(depth_folder) if isfile(join(depth_folder, f))
]
for image in images:
rgb_image = cv2.imread(
"/Data2TB/correctly_registered/S12/test/color/" +
image.replace(".png", ".jpg"))
cvimage = cv2.imread(depth_folder + image, -1)
count = 0
for frame in jsonprop["proposals"]:
if frame["file"] == image:
for object in frame["objects"]:
x = object["x"]
y = object["y"]
z = object["z"]
cv2.circle(rgb_image, (x, y), 3, (0, 0, 255), 2)
headpoint = [y, x]
xmiw, ymiw, xmaw, ymaw = core_functions.get_bounding_box_WASSIMEA(
x, y, z)
#cv2.rectangle(rgb_image,(xmiw,ymiw),(xmaw,ymaw),(255,0,0),2)
roi = cvimage[ymiw:ymaw, xmiw:xmaw]
width, height = roi.shape[1], roi.shape[0]
if (width > 0 and height > 0): # and z <= 4500):
headpoint[0] = headpoint[0] - ymiw
headpoint[1] = headpoint[1] - xmiw
c1, c2, c3, mod = core_functions.get_channels(
roi, headpoint)
#cv2.imwrite("/home/wassimea/Desktop/chl_work/outs/" + str(count)+ ".jpg",mod)
#all = pingit.get_classification(mod)
boxes = pingit.get_classification(mod)[0]
scores = pingit.get_classification(mod)[1]
classes = pingit.get_classification(mod)[2]
scores_sorted = sorted(scores, reverse=True)
print(str(scores_sorted[0][0]))
#scores = sorted(scores,reverse = True)
maximum = max(scores)
maxclass = max(classes)
#cv2.imshow("ayre",mod)
#cv2.waitKey()
localcount = 0
for i in range(0, len(scores[0])):
if (scores[0][i] >= 0.003):
localcount = localcount + 1
if (classes[0][i] == 1.0):
xmin = xmiw + int(boxes[0][i][1] * width)
ymin = xmaw + int(boxes[0][i][0] * height)
xmax = xmaw + int(boxes[0][i][3] * width)
ymax = ymaw + int(boxes[0][i][2] * height)
#cv2.rectangle(rgb_image,xmin, ymin, xmax, ymaw + ymax),(0,255,0), 3)
cv2.rectangle(rgb_image,
(xmiw - 1, ymiw - 1),
(xmaw + 1, ymaw + 1),
(0, 0, 255), 2)
#else:
#cv2.rectangle(mod,(int(boxes[0][i][1] * width), int(boxes[0][i][0] * height)), (int(boxes[0][i][3] * width), int(boxes[0][i][2] * height)),(255,0,0), 3)
if (localcount > 1):
toll = 0
#cv2.imwrite("/home/wassimea/Desktop/chl_work/outs/" + image,mod)
#print("check")
#cv2.waitKey()
count = count + 1
cv2.imwrite(
"/home/wassimea/Desktop/chl_work/outs/" +
image.replace(".png", "_") + str(count) + ".jpg",
rgb_image)
for k in range(0, len(images)):
if ("resized-1-rotated-0" in images[k]):
print(images[k])
img = np.array(Image.open(folder + images[k]))
new_img = img
for i in range(0, len(jsondata['frames'])):
if (jsondata['frames'][i]['file'] == images[k]):
for j in range(0,
len(jsondata['frames'][i]['annotations'])):
if (jsondata['frames'][i]['annotations'][j]['label'] ==
'Head'):
xmin = jsondata['frames'][i]['annotations'][j]['x']
ymin = jsondata['frames'][i]['annotations'][j]['y']
xmax = xmin + jsondata['frames'][i]['annotations'][
j]['width']
ymax = ymin + jsondata['frames'][i]['annotations'][
j]['height']
cv2.rectangle(new_img, (xmin, ymin), (xmax, ymax),
(255, 0, 0), 3)
boxes = pingit.get_classification(img)[0]
scores = pingit.get_classification(img)[1]
for i in range(0, len(scores[0])):
if (scores[0][i] > 0.15):
cv2.rectangle(
new_img,
(int(boxes[0][i][1] * 640), int(boxes[0][i][0] * 480)),
(int(boxes[0][i][3] * 640), int(boxes[0][i][2] * 480)),
(0, 0, 255), 3)
cv2.imwrite(out_folder + images[k], new_img)
def generate():
gtheads = 0
propheads = 0
image_count = 0
propcountarr = []
depth_folder = "/Data2TB/correctly_registered/S2/tester/"
with open("/home/wassimea/Desktop/wzebb.json") as f:
jsonprop = json.load(f)
with open("/Data2TB/sample/annotations.json") as f:
jsongt = json.load(f)
png_files = [
f for f in listdir(depth_folder) if isfile(join(depth_folder, f))
]
for i in range(0, len(png_files)):
image_name = png_files[i] #jsonprop["proposals"][i]["file"]
image_name_jpg = image_name.replace("png", "jpg")
if image_name_jpg in jsongt:
image_count = image_count + 1
paletted_image = cv2.imread(
"/Data2TB/correctly_registered/S2/tester/paletted/" +
image_name_jpg)
gtarray = [] #np.zeros(shape = (0,4))
for k in range(0, len(jsongt[image_name_jpg]["annotations"])):
if (jsongt[image_name_jpg]["annotations"][k]["category"] ==
"Head"):
xmingt = jsongt[image_name_jpg]["annotations"][k]["x"] + 5
ymingt = jsongt[image_name_jpg]["annotations"][k]["y"]
width = jsongt[image_name_jpg]["annotations"][k]["width"]
height = jsongt[image_name_jpg]["annotations"][k]["height"]
xmaxgt = xmingt + width
ymaxgt = ymingt + height
if (xmingt >= 0 and ymingt >= 0 and xmingt <= 1000
and ymingt <= 1000):
gtheads = gtheads + 1
gtarray.append(
[xmingt, ymingt, xmaxgt, ymaxgt]
) # = np.append(gtarray, [xmingt,ymingt, xmaxgt,ymaxgt])
cv2.rectangle(paletted_image, (xmingt, ymingt),
(xmaxgt, ymaxgt), (0, 0, 255), 3)
#os.mkdir("/home/wassimea/Desktop/testing/" + str(i))
proparray = [] #np.zeros(shape = (0,4))
for j in range(0, len(jsonprop["proposals"])): #[i]["objects"])):
if (jsonprop["proposals"][j]["file"] == image_name):
propcount = 0
for k in range(0,
len(jsonprop["proposals"][j]["objects"])):
propcount = propcount + 1
x = jsonprop["proposals"][j]["objects"][k]["x"]
y = jsonprop["proposals"][j]["objects"][k]["y"]
z = jsonprop["proposals"][j]["objects"][k]["z"]
cv2.circle(paletted_image, (x, y), 2, (255, 0, 0), 3)
xmiw, ymiw, xmaw, ymaw = core_functions.get_bounding_box_WASSIMEA(
x, y, z)
proparray.append([xmiw, ymiw, xmaw, ymaw])
cv2.rectangle(paletted_image, (xmiw, ymiw),
(xmaw, ymaw), (0, 255, 0), 3)
propcountarr.append(propcount)
cv2.imshow("AYRE", paletted_image)
cv2.waitKey()
#cv2.imwrite("/home/wassimea/Desktop/testing/" + image_name.replace("png","jpg"),paletted_image)
for m in range(0, len(gtarray)):
for n in range(0, len(proparray)):
iou = core_functions.bb_intersection_over_union(
gtarray[m], proparray[n])
contained = False #check_if_rectangle_contained(gtarray[m],proparray[n])
if (iou > 0.5):
propheads = propheads + 1
contained = True
break
if (contained == False):
print(image_name_jpg)
#cv2.imshow("kissimmik", paletted_image)
#cv2.waitKey()
zabre = np.mean(propcountarr)
pingit = 1
def process_image(filename, jsongt, jsonprop):
png_image = cv2.imread(png_folder + filename, -1)
gtarray = []
proparray = []
print(filename)
counter = 0
filename_jpg = filename.replace("png", "jpg")
if filename_jpg in jsongt and len(jsongt[filename_jpg]["annotations"]) > 0:
if not os.path.exists(out_folder + filename.replace(".png", "")):
os.mkdir(out_folder + filename.replace(".png", ""))
for object_gt in jsongt[filename_jpg]["annotations"]:
if object_gt["category"] == "Head":
xmingt = object_gt["x"] #+ 5
ymingt = object_gt["y"]
width = object_gt["width"]
height = object_gt["height"]
xmaxgt = xmingt + width
ymaxgt = ymingt + height
id = object_gt["id"]
gtarray.append([xmingt, ymingt, xmaxgt, ymaxgt, id])
for im_object in jsonprop["proposals"]:
if im_object["file"] == filename:
for object_prop in im_object["objects"]:
x = object_prop["x"]
y = object_prop["y"]
z = object_prop["z"]
xmiw, ymiw, xmaw, ymaw = core_functions.get_bounding_box_WASSIMEA(
x, y, z)
proparray.append([xmiw, ymiw, xmaw, ymaw, y, x])
#head = [object["x"],object["y"],object["x"] + object["width"],object["y"] + object["height"]]
#right_shoulder = [-1,-1]
#left_shoulder = [-1,-1]
#id = object["id"]
if not os.path.exists(out_folder +
filename.replace(".png", "") + "/" +
str(id)):
os.mkdir(out_folder + filename.replace(".png", "") + "/" +
str(id))
#for object_candidate in jsongt[filename_jpg]["annotations"]:
#if object_candidate["id"] == id and object_candidate["category"] == "Right Shoulder":
#right_shoulder = [object_candidate["x"],object_candidate["y"]]
#elif object_candidate["id"] == id and object_candidate["category"] == "Left Shoulder":
#left_shoulder = [object_candidate["x"],object_candidate["y"]]
for gt_object in gtarray:
id = gt_object[4]
for prop_object in proparray:
iou = bb_intersection_over_union(gt_object, prop_object)
if (iou > 0.4):
contained = True
right_shoulder = [-1, -1]
left_shoulder = [-1, -1]
for object_candidate in jsongt[filename_jpg][
"annotations"]:
if object_candidate["id"] == id and object_candidate[
"category"] == "Right Shoulder":
right_shoulder = [
object_candidate["x"], object_candidate["y"]
]
elif object_candidate["id"] == id and object_candidate[
"category"] == "Left Shoulder":
left_shoulder = [
object_candidate["x"], object_candidate["y"]
]
headpoint = [prop_object[4], prop_object[5]]
image, head_new, right_shoulder_new, left_shoulder_new, headpoint_new = get_new_roi_with_annotations(
filename, prop_object, gt_object, right_shoulder,
left_shoulder, headpoint)
num_zeros = (image == 0).sum()
width, height = image.shape[1], image.shape[0]
if (width > 2 and height > 2 and num_zeros <
(width * height) / 3):
data['frames'].append({
'file':
filename.replace(".png", "_id_") + str(id) +
".jpg",
'width':
width,
'height':
height,
'annotations': [{
'label': 'Head',
'x': head_new[0],
'y': head_new[1],
'width': head_new[2] - head_new[0],
'height': head_new[3] - head_new[1]
}, {
'label': 'Headpoint',
'x': headpoint_new[0],
'y': headpoint_new[1]
}, {
'label': 'Right Shoulder',
'x': right_shoulder_new[1],
'y': right_shoulder_new[0]
}, {
'label': 'Left Shoulder',
'x': left_shoulder_new[1],
'y': left_shoulder_new[0]
}]
})
c1, c2, c3, mod = core_functions.get_channels(
image, headpoint_new)
#cv2.rectangle(mod,(head_new[0],head_new[1]),(head_new[2] ,head_new[3]),(255,0,0),3)
#cv2.circle(mod,(right_shoulder_new[0], right_shoulder_new[1]), 5, (0,255,0), -1)
#cv2.circle(mod,(left_shoulder_new[0], left_shoulder_new[1]), 5, (0,255,0), -1)
#cv2.imwrite(out_folder + filename.replace(".png","") + "/" + str(id) + "/c1.png",c1)
#cv2.imwrite(out_folder + filename.replace(".png","") + "/" + str(id) + "/c2.jpg",c2)
#cv2.imwrite(out_folder + filename.replace(".png","") + "/" + str(id) + "/c3.jpg",c3)
#cv2.imwrite(out_folder + filename.replace(".png","") + "/" + str(id) + "/mod.jpg",mod)
cv2.imwrite(
out_folder.replace("testing", "testing_2/") +
filename.replace(".png", "_id_") + str(id) +
".jpg", mod)
break
else:
headpoint = [prop_object[4], prop_object[5]]
image, headpoint_new = core_functions.get_neg_roi(
filename, prop_object, headpoint)
width, height = image.shape[1], image.shape[0]
data['frames'].append({
'file':
filename.replace(".png", "_id_") + str(id) + ".jpg",
'width':
width,
'height':
height,
'annotations': []
})
if (width > 5 and height > 5):
c1, c2, c3, mod = core_functions.get_channels(
image, headpoint_new)
cv2.imwrite(
out_folder.replace("testing", "testing_2/") +
filename.replace(".png", "_id_") +
str(proparray.index(prop_object)) + ".jpg", mod)
