def viewResult(sample,idx,prediction,config,save_images_instead=1,size=(384,384)):
displays = []
camera_idx = 0
camera = sample['cameras'][camera_idx]
subject = sample['subject'][camera_idx]
action = sample['action'][camera_idx]
keypoints3d_pred = prediction['keypoints_3d_pred'].cpu()
keypoints_3d_pred = keypoints3d_pred[0,:, :3].detach().numpy()
keypoints_3d_gt = sample['keypoints_3d'][:, :3]
# Project and draw keypoints on images
for camera_idx in range(len(sample['cameras'])): #camera_indexes_to_show:
camera = sample['cameras'][camera_idx]
keypoints_2d_pred = project(camera.projection, keypoints_3d_pred)
keypoints_2d_gt = project(camera.projection, keypoints_3d_gt)
# import ipdb; ipdb.set_trace()
img = sample['images'][camera_idx]
pred_kind = config.pred_kind if hasattr(config, "pred_kind") else config.kind
display = vis.draw_2d_pose_cv2(keypoints_2d_pred, img, kind=pred_kind)
#display = vis.draw_2d_pose_cv2(keypoints_2d_gt, img, kind=config.kind)
cv2.putText(display, f"Cam {camera_idx:02}", (10, 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 0, 0))
displays.append(display)
display3 = vis.draw_3d_pose_image(keypoints_3d_pred,kind=pred_kind,radius=450)
display3 = cv2.cvtColor(display3,cv2.COLOR_RGBA2RGB)
display3 = cv2.resize(display3, size, interpolation=cv2.INTER_AREA)
cv2.putText(display3, f"3D prediction", (10, 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 0, 0))
displays.append(display3)
display3_gt = vis.draw_3d_pose_image(sample['keypoints_3d'][:, :3],kind=pred_kind,radius=450)
display3_gt = cv2.cvtColor(display3_gt,cv2.COLOR_RGBA2RGB)
display3_gt = cv2.resize(display3_gt, size, interpolation=cv2.INTER_AREA)
cv2.putText(display3_gt, f"3D GT", (10, 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 0, 0))
displays.append(display3_gt)
# Fancy stacked images
for j, display in enumerate(displays):
if j == 0:
combined = display
else:
combined = np.concatenate((combined, display), axis=1)
# Load
if save_images_instead:
file = f"./result/result-{subject}-{action}-{camera.name}-{idx}.png"
cv2.imwrite(file, combined)
else:
cv2.imshow('w', combined)
cv2.setWindowTitle('w', f"Index {idx}")
c = cv2.waitKey(0) % 256
if c == ord('q') or c == 27:
print('Quitting...')
cv2.destroyAllWindows()
def read_cam(video_capture):
if video_capture.isOpened():
windowName = "yolo"
cv2.namedWindow(windowName, cv2.WINDOW_NORMAL)
cv2.resizeWindow(windowName, 1280, 720)
cv2.moveWindow(windowName, 0, 0)
cv2.setWindowTitle(windowName, "Yolo Object Detection")
while True:
# Check to see if the user closed the window
if cv2.getWindowProperty(windowName, 0) < 0:
break
ret_val, frame = video_capture.read()
frame = cv2.resize(frame,(224,224))
cv2.imshow(windowName, frame)
frame = np.expand_dims(frame, axis=0)
frame = preprocess_input(frame)
preds = model.predict(frame)
print(preds)
print('Predicted:', decode_predictions(preds, top=3)[0])
cv2.waitKey(5)
else:
print("camera open failed")
def take_calibration_images(cam_id=0):
"""
Captures calibration images by showing a live feed of the camera and saving the current image when Space is pressed.
:param cam_settings: Path to the file to load the camera settings from (or None to use Spotter's default settings)
"""
v = cv2.VideoCapture(cam_id)
ensure_folder_exists(CALIB_FOLDER) # Make sure folder exists to prevent errors on file saving
for file_name in glob(generate_calibration_filename("{}*".format(CALIB_INPUT_PREFIX))):
os.remove(file_name) # Remove any file from a previous calibration so they don't get mixed
cnt_img = 0
win_title = "Capturing calibration images - {} captured".format(cnt_img)
cv2.namedWindow(win_title)
cv2.moveWindow(win_title, 100,100)
while True:
ret, cam_frame = v.read()
assert ret, "Couldn't grab a frame from cam {}".format(cam_id)
cv2.imshow(win_title, cv2.resize(cam_frame, dsize=None, fx=0.5, fy=0.5))
key = cv2.waitKeyEx(1)
if key == ord(' '): # Save a new image when Space is pressed
cnt_img += 1
file_name = generate_calibration_filename("{}_{}.{}".format(CALIB_INPUT_PREFIX, cnt_img, CALIB_INPUT_FORMAT))
cv2.setWindowTitle(win_title, "{} - {} captured".format(win_title.split(" - ")[0], cnt_img))
cv2.imwrite(file_name, cam_frame)
print("Captured new calibration image: {}".format(file_name))
elif key >= 0: # Exit if a key other than Space was pressed
break
cv2.destroyAllWindows()
def check_with_cnn(image):
number_img = 255 - image
number_img = number_img / 255
found = sum(sum(number_img))
if 58 < found < 94:
number_img = cv2.dilate(number_img,
np.ones((2, 2), np.uint8),
iterations=1)
elif found < 58:
number_img = cv2.dilate(number_img,
np.ones((3, 3), np.uint8),
iterations=1)
send = number_img.reshape(1, 28, 28, 1)
chance = model.predict(send)
chance = chance[0]
pop = max(chance)
number = np.argmax(chance)
if prikazSlanjaCNN:
cv2.resizeWindow('CNN', 300, 300)
cv2.imshow('CNN', image)
cv2.setWindowTitle('CNN', str(number) + " sa " + str(pop))
cv2.waitKey(1)
time.sleep(0.5)
return number, max(chance)
def open_display_window(width, height):
"""Open the cv2 window for displaying images with bounding boxeses."""
cv2.namedWindow(WINDOW_NAME, cv2.WINDOW_NORMAL)
cv2.resizeWindow(WINDOW_NAME, width, height)
cv2.moveWindow(WINDOW_NAME, 0, 0)
cv2.setWindowTitle(WINDOW_NAME, 'Camera TFTRT Object Detection Demo '
'for Jetson TX2/TX1')
def run(self):
cv2.namedWindow("render", cv2.WINDOW_NORMAL)
cv2.moveWindow("render", self.window_width // 4, self.window_height // 4)
cv2.resizeWindow('render', self.window_width, self.window_height)
self.start_time = self.last_time = time.time()
while True:
theta = 0.03 * (time.time() - self.start_time)
view = np.dot(self.view, rotation(theta, 0, 1, 0))
mvp = np.dot(self.proj, view)
attributes = {'position': self.vertices}
if self.colors is not None:
attributes['color'] = self.colors
image = rasterize(attributes, self.indices, mvp=mvp, width=self.window_width, height=self.window_height)
image = image[::-1, :, :]
cv2.imshow("render", image)
key = cv2.waitKey(1)
if key == 27:
return
# Display frames per second
cur_time = time.time()
dtime = cur_time - self.last_time
fps = 1.0 / float(dtime + 1e-9)
self.last_time = cur_time
cv2.setWindowTitle("render", "Renderer demo, fps = %.2f" % fps)
def open_window(width, height):
cv2.namedWindow(WINDOW_NAME, cv2.WINDOW_NORMAL)
cv2.resizeWindow(WINDOW_NAME, width, height)
cv2.moveWindow(WINDOW_NAME, 0, 0)
cv2.setWindowTitle(
WINDOW_NAME, 'Camera SSD Object Detection Demo '
'for Jetson TX2/TX1')
def draw_blob_detection(self,
window_name,
detect_image,
display_image,
connectivity=8,
minimum_size=0):
num_labels, labels, stats, centroids = self.blob_detection(
detect_image, connectivity)
# Draw circles where blobs where found
for i, val in enumerate(centroids):
if stats[i, cv2.CC_STAT_AREA] > minimum_size:
cv2.circle(display_image,
(int(centroids[i][0]), int(centroids[i][1])), 10,
(255, 0, 0), 3, 8, 0)
# Show the result
cv2.namedWindow(window_name, cv2.WINDOW_NORMAL)
cv2.imshow(window_name, display_image)
cv2.waitKey(1)
new_window_name = window_name + ": " + str(num_labels)
cv2.setWindowTitle(window_name, new_window_name)
def run(self):
"""
Main processing loop
"""
try:
self.distorted_points = points
self.undistorted_points = targets
self.quit()
#self.find_optimal_warp()
except:
cv2.namedWindow("window")
cv.setMouseCallback("window", self.on_mouse, 0)
while True: # one frame
cur_frame = self.cur_frame.copy()
cur_frame = cv2.resize(cur_frame,
(cur_frame.shape[1] // self.plot_ds,
cur_frame.shape[0] // self.plot_ds))
cv2.imshow("window", cur_frame)
title = "Dummy Title"
cv2.setWindowTitle("window", str(title))
key = cv2.waitKey(1)
if key == ord("q"):
self.quit()
break
elif key == ord("u"):
self.undo()
def open_display_window(width, height):
"""Open the cv2 window for displaying images with bounding boxeses."""
cv2.namedWindow(WINDOW_NAME, cv2.WINDOW_NORMAL)
cv2.resizeWindow(WINDOW_NAME, width, height)
cv2.moveWindow(WINDOW_NAME, 0, 0)
cv2.setWindowTitle(WINDOW_NAME, WINDOW_NAME)
set_full_screen(True)
def run(self):
cv2.namedWindow("window")
cv.setMouseCallback("window", self.on_mouse, 0)
while (self.cont): # one frame
if self.new is not None:
self.plot_axes()
self.new = None
cv2.imshow("window", self.cur_image)
title = "{} toggle class (1), switch frame (8-9), clear all (c), undo(u), quit (q), switch frame (8-9)".format(
self.class_names[self.cur_class])
cv2.setWindowTitle("window", str(title))
key = cv2.waitKey(1)
if key == ord('9'):
self.next()
# elif key == ord('8'):
# self.prev()
elif key == ord('c'):
self.clear()
elif key == ord("q"):
self.quit()
elif key == ord("1"):
self.toggle_class()
elif key == ord("u"):
self.undo()
elif key == ord("d"):
self.remove()
elif key == ord("l"):
self.load()
def inone():
sequence='30'
path='../data/first/birdview/old/'
impath = os.path.join(path, sequence)
list = glob.glob(impath + '/*.png')
cv2.setWindowTitle("show", "1")
for i in list:
label = i.replace('png', 'txt')
img = cv2.imread(i)
w, h, c = img.shape
f = open(label)
lines = f.readlines()
f.close()
for l in lines:
l = l.split(" ")
xmid = float(l[1])
ymid = float(l[2])
width = float(l[3])
height = float(l[4])
xmin = int((xmid - width / 2) * w)
ymin = int((ymid - height / 2) * h)
xmax = int((xmid + width / 2) * w)
ymax = int((ymid + height / 2) * h)
img = cv2.rectangle(img, (xmin, ymin), (xmax, ymax), (255, 0, 0), 2)
cv2.imshow("show", img)
cv2.waitKey()
cv2.destroyAllWindows()
def showpicts(name, wl, begin, end):
cv.namedWindow('picture', cv.WINDOW_NORMAL)
for i in range(begin, end + 1):
pfluor = cv.imread('Test\\' + name + str(i) + '_' + str(wl) + '.tiff', cv.IMREAD_GRAYSCALE)
p0 = cv.imread('Test\\' + name + str(i) + '_0.tiff', cv.IMREAD_GRAYSCALE)
p = pfluor - p0
ma1 = np.max(p)
if ma1 > 252:
m, p = cv.threshold(p, 252, 255, cv.THRESH_TOZERO_INV)
ma1 = np.max(p)
blur = cv.GaussianBlur(p, (5, 5), 0)
pshow = (blur * (255 / ma1)).astype(np.uint8)
cv.imshow('picture', pshow)
cv.setWindowTitle('picture', 'picture' + str(i))
# h = cv.calcHist(blur, [0], None, [255], [[0, 255]])
m, thgauss = cv.threshold(blur, 0, 255, cv.THRESH_BINARY + cv.THRESH_OTSU)
print(m)
cv.namedWindow('threshblur', cv.WINDOW_NORMAL)
cv.imshow('threshblur', thgauss)
if cv.waitKey(0) == BTN_ESC:
cv.destroyAllWindows()
break
def plot_movements(self):
denorm = transforms.Normalize(
mean=[-0.485 / 0.229, -0.456 / 0.224, -0.406 / 0.225],
std=[1 / 0.229, 1 / 0.224, 1 / 0.225])
avg = denorm(self.running_avg_frame)
avg = avg.numpy().transpose(1, 2, 0)
im = avg.copy()
#plot ignored regions
# for box in self.cam["ignore"]:
# color = (0.1,0.1,0.1) #colors[int(obj.cls)]
# c1 = (int(box[0]),int(box[1]))
# c2 = (int(box[2]),int(box[3]))
# im = cv2.rectangle(im,c1,c2,color,1)
#plot source regions
for i, box in enumerate(self.sources):
if self.source_box_classes[i] == 0:
color = (0.1, 0.9, 0.1) #colors[int(obj.cls)]
else:
color = (0.9, 0.1, 0.1)
c1 = (int(box[0]), int(box[1]))
c2 = (int(box[2]), int(box[3]))
im = cv2.rectangle(im, c1, c2, color, 1)
for s_idx, source in enumerate(self.sinks):
for b_idx, box in enumerate(source):
color = (0.1, 0.1, 0.9) #colors[int(obj.cls)]
c1 = (int(box[0]), int(box[1]))
c2 = (int(box[2]), int(box[3]))
im = cv2.rectangle(im, c1, c2, color, 1)
for movement in self.recorded_movements:
tracks = self.recorded_movements[movement]
for track in tracks:
for f in range(len(track) - 1):
if np.sum(track[f]) != 0 and np.sum(track[f + 1]) != 0:
x0 = int((track[f, 0] + track[f, 2]) / 2.0)
y0 = int((track[f, 1] + track[f, 3]) / 2.0)
x1 = int((track[f + 1, 0] + track[f + 1, 2]) / 2.0)
y1 = int((track[f + 1, 1] + track[f + 1, 3]) / 2.0)
color = self.movement_colors[movement - 1]
color = (color[0] / 255.0, color[1] / 255.0,
color[2] / 255.0)
im = cv2.line(im, (x0, y0), (x1, y1), color, 1)
#im = cv2.resize(im, (1920,1080))
cv2.imshow("frame", im)
cv2.setWindowTitle(
"frame",
str("Average frame for {}".format(
self.sequence_name.split("/")[-1])))
cv2.imwrite(
"_output/" + self.sequence_name.split("/")[-1].split(".")[0] +
"_avg.png", im * 255)
cv2.waitKey(10)
cv2.destroyAllWindows()
def imshow(name, title, _in):
img = None
# Load img if necessary
if isinstance(_in, str):
img = imread_rotated(_in)
elif isinstance(_in, np.ndarray):
img = _in
# Load exception if input argument invalid
if img is None:
raise TypeError('Expected file path or image')
# Create window if necessary
global windows
if name not in windows:
windows[name] = cv.namedWindow(name, cv.WINDOW_OPENGL | cv.WINDOW_KEEPRATIO)
(h, w, _) = img.shape
if h < w:
ratio = 1280. / w
else:
ratio = 800. / h
h *= ratio
w *= ratio
img = cv.resize(img, None, fx=ratio, fy=ratio)
cv.imshow(name, img)
cv.setWindowTitle(name, title)
cv.resizeWindow(name, int(w), int(h))
def main():
global nameOfImage
oldNameOfImage = ""
# font
font = cv2.FONT_HERSHEY_SIMPLEX
# org
org = (50, 50)
# fontScale
fontScale = 1
# Blue color in BGR
color = (0, 150, 0)
# Line thickness of 2 px
thickness = 2
cv2.namedWindow(WINDOW_NAME, cv2.WINDOW_AUTOSIZE)
cv2.setWindowTitle(WINDOW_NAME, 'Click "Q or Esc" to Quit.')
while True:
try:
img = cv2.imread(nameOfImage, cv2.IMREAD_COLOR)
img = cv2.putText(img, "Today : " + getTime(), org, font,
fontScale, color, thickness, cv2.LINE_AA)
cv2.imshow(WINDOW_NAME, img)
except:
pass
buttonPressed = cv2.waitKey(1)
if buttonPressed == 27:
## Terminate the Program
break
cv2.destroyAllWindows()
def prepare_window():
window_name = "Save snapshots"
cv2.namedWindow(window_name, cv2.WINDOW_NORMAL)
cv2.resizeWindow(window_name, 1280, 720)
cv2.moveWindow(window_name, 0, 0)
cv2.setWindowTitle(window_name, "Save snapshots")
return window_name
def prepare_display_window():
window = None
cv2.namedWindow(window, cv2.WINDOW_NORMAL)
cv2.resizeWindow(window, 1280, 720)
cv2.moveWindow(window, 0, 0)
cv2.setWindowTitle(window, "Video capture")
return window
def detect_faces(img, debug=False):
"""检测图片中的人脸并返回第一个有效的人脸区域"""
# OpenCV的检测效果似乎受图像角度影响很大，因此要尝试旋转图片（顺时针为正）
rotate_angles = (0, 10, -10, 20, -20, 30, -30, 40, -40, 50, -50, 60, -60)
for angle in rotate_angles:
rotated = rotate_bound(img, angle)
grey = cv.cvtColor(rotated, cv.COLOR_BGR2GRAY)
faces = face_cascade.detectMultiScale(grey, 1.1, 4)
for area in faces:
if isValidArea(area):
face_pos, crop_pos = calc_origin_area(rotated, area, angle,
img.shape[:2])
return face_pos, crop_pos
# 有效区域用绿框标记，无效区域用红框标记
color = (0, 255, 0) if isValidArea(area) else (0, 0, 255)
(x, y, w, h) = area
cv.rectangle(rotated, (x, y), (x + w, y + h), color, 2)
recog_info = f'({x},{y}), {w}x{h}'
(h2, w2) = rotated.shape[:2]
pos = (min(w2 - 170, x), max(y - 10, 12)) # 避免信息显示到绘图区外
cv.putText(rotated, recog_info, pos, cv.FONT_HERSHEY_SIMPLEX, 0.5,
color, 1)
if debug:
win_name = "Actress detection"
cv.imshow(win_name, rotated)
cv.setWindowTitle(win_name,
win_name + f" with ratotion {angle} degrees")
cv.waitKey(0)
def show(self):
cv2.imshow(self.contours.filename, self.img)
cv2.setWindowTitle(self.contours.filename,
self.contours.filename + ' ' + self.modoed)
while True:
key = cv2.waitKey(1)
if key != -1:
print(key)
if key == 27:
break
elif key == 121: # y
self.direccion = 'y'
elif key == 120: # x
self.direccion = 'x'
elif key == 32: # espacio
self.direccion = ''
elif key == 105: # i
self.dibimagen = not self.dibimagen
self.draw_contours()
elif key == 99: # c
self.dibcurvas = not self.dibcurvas
self.draw_contours()
elif key == 9: # tab
print(self.modoed)
if self.modoed == 'dib':
self.modoed = 'ajuste'
else:
self.modoed = 'dib'
cv2.setWindowTitle(self.contours.filename,
self.contours.filename + ' ' + self.modoed)
cv2.destroyAllWindows()
def vis_detections_cv(im_name, im, dets_all, cls_all, thresh=0.5):
"""Draw detected bounding boxes."""
assert len(dets_all) == len(cls_all)
windowName = 'demo_vehicles'
inds = np.where(dets_all[:, -1] >= thresh)[0]
for i in inds:
bbox = dets_all[i, :4]
score = dets_all[i, -1]
cls = cls_all[i]
cv2.rectangle(im, (bbox[0], bbox[1]), (bbox[2], bbox[3]), COLORS[cls],
2)
txt = '{:s} {:.3f}'.format(CLASSES[cls], score)
cv2.putText(im, txt, (int(bbox[0]) + 1, int(bbox[1]) - 2),
cv2.FONT_HERSHEY_PLAIN, 1.0, (32, 32, 32), 4, cv2.LINE_AA)
cv2.putText(im, txt, (int(bbox[0]), int(bbox[1]) - 2),
cv2.FONT_HERSHEY_PLAIN, 1.0, (240, 240, 240), 1,
cv2.LINE_AA)
cv2.namedWindow(windowName, cv2.WINDOW_NORMAL)
height, width, colors = im.shape
cv2.resizeWindow(windowName, width, height)
cv2.moveWindow(windowName, 0, 0)
cv2.setWindowTitle(windowName, im_name)
cv2.imshow(windowName, im)
key = cv2.waitKey(0)
cv2.destroyAllWindows()
def process_img(img, name):
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
shape = gray.shape
# Find the chess board corners
ret, corners = cv2.findChessboardCorners(gray, (args.nx, args.ny),
None, detect_corner_flags)
# If found, add object points, image points (after refining them)
if ret:
objpoints.append(objp)
imgpoints.append(corners)
imgs.append(img)
names.append(name)
# Draw and display the corners
img_show = img.copy()
corners2 = cv2.cornerSubPix(gray, corners, (11, 11), (-1, -1),
criteria)
cv2.drawChessboardCorners(img_show, (args.nx, args.ny), corners2,
ret)
sc = 1024 / np.max(img_show.shape)
cv2.imshow('detected corners',
cv2.resize(img_show, None, fx=sc, fy=sc))
else:
print('cant find the corners from %s' % name)
sc = 1024 / np.max(img.shape)
cv2.imshow('detected corners', cv2.resize(img, None, fx=sc, fy=sc))
cv2.setWindowTitle('detected corners', name)
cv2.waitKey(0 if args.pause else 500)
return shape
def run(input_path, labels_path, mode=None):
df_labels: pd.DataFrame = pd.read_csv(labels_path)
df_labels = df_labels.loc[df_labels['level'] != 0]
change_list = []
print(f'INFO> Manual annotation of {len(df_labels)} frames starting...')
cv2.namedWindow(WINDOW_NAME, cv2.WINDOW_NORMAL)
cv2.resizeWindow(WINDOW_NAME, WINDOW_WIDTH, WINDOW_HEIGHT)
cv2.createTrackbar('progress', WINDOW_NAME, 0, 100, lambda a: None)
for i, row in tqdm(enumerate(df_labels.itertuples()), smoothing=0, total=len(df_labels)):
prefix = 'pos' if row.level > 0 else 'neg'
img = cv2.imread(os.path.join(input_path, prefix, row.image))
cv2.imshow(WINDOW_NAME, img)
cv2.setWindowTitle(WINDOW_NAME, f'Name: {os.path.splitext(row.image)[0]} - Grade: {row.level}')
key_code = cv2.waitKey(0)
while key_code not in KEYCODES.values():
key_code = cv2.waitKey(0)
user_input = get_class_from_key(key_code)
if user_input != row.level:
df_labels.loc[row.Index, 'level'] = user_input
change_list.append((row.image, row.level, user_input))
cv2.setTrackbarPos('progress', WINDOW_NAME, int(i // (len(df_labels) / 100)))
cv2.destroyAllWindows()
df_labels.to_csv(os.path.join(input_path, OUTPUT_NAME))
print(f'INFO> Manual annotation finished. {len(change_list)} rows affected.')
print(change_list)
def run(self):
self.frame_boxes[self.frame] = self.cur_frame_boxes
cv2.namedWindow("window")
cv.setMouseCallback("window", self.on_mouse, 0)
while(self.cont): # one frame
if self.new is not None:
self.cur_image = cv2.rectangle(self.cur_image,(self.new[0],self.new[1]),(self.new[2],self.new[3]),self.colors[self.new[4]],2)
self.new = None
cv2.imshow("window", self.cur_image)
cv2.setWindowTitle("window",str(self.frame))
key = cv2.waitKey(1)
if key == ord('2'):
self.next()
elif key == ord('1'):
self.prev()
elif key == ord('c'):
self.clear()
elif key == ord("q"):
self.quit()
elif key == ord("0"):
self.toggle_class()
elif key == ord("3"):
self.undo()
def imshow(name, title, _in):
img = None
# Load img if necessary
if isinstance(_in, str):
img = imread_rotated(_in)
elif isinstance(_in, np.ndarray):
img = _in
# Load exception if input argument invalid
if img is None:
raise TypeError('Expected file path or image')
# Create window if necessary
global windows
if name not in windows:
windows[name] = cv.namedWindow(name,
cv.WINDOW_OPENGL | cv.WINDOW_KEEPRATIO)
(h, w, _) = img.shape
if h < w:
ratio = 1280. / w
else:
ratio = 800. / h
h *= ratio
w *= ratio
img = cv.resize(img, None, fx=ratio, fy=ratio)
cv.imshow(name, img)
cv.setWindowTitle(name, title)
cv.resizeWindow(name, int(w), int(h))
def loop(self):
images = self.category.images
img = None
load = True
done = False
help = True
i = 0
key = -1
while True:
if help:
help = False
if self.helpCallback is not None:
self.helpCallback(i, self.data)
if key != -1 and self.keyCallback is not None:
i2 = i
i, done, help, load, self.data = self.keyCallback(key, i, self.data)
if not self.wrap:
i = max(0, min(len(images)-1, i))
if i != i2:
load = True
if load:
load = False
i = i % len(images)
imgFile = images[i]
img = cv2.imread(imgFile);
cv2.setWindowTitle(self.windowName, "{} ({})".format(imgFile.basename, self.title))
cv2.imshow(self.windowName, img) # show the image
if self.loadCallback is not None:
self.overlays, data = self.loadCallback(i, imgFile, self.data)
if len(self.overlays) > 0:
imgDrawn = img.copy()
# draw the overlays
for overlay in self.overlays:
box = overlay.rect
color = overlay.color
width = overlay.width
cv2.rectangle(imgDrawn, (box.x,box.y), (box.x+box.width,box.y+box.height), color, width)
cv2.imshow(self.windowName, imgDrawn) # show the image with the overlay
# http://stackoverflow.com/questions/35003476/opencv-python-how-to-detect-if-a-window-is-closed/37881722#37881722
#for j in range(4):
# print("{} {}".format(j, cv2.getWindowProperty(self.windowName, j)))
#print()
if cv2.getWindowProperty(self.windowName, 1) != 0:
done = True
if done:
# http://stackoverflow.com/questions/6116564/destroywindow-does-not-close-window-on-mac-using-python-and-opencv
cv2.destroyWindow(self.windowName)
for i in range(4):
cv2.waitKey(1)
return self.data
key = cv2.waitKey(10) #wait 10 ms for a key press
def open_window(window_name, title, width=None, height=None):
"""Open the display window."""
print(1)
cv2.namedWindow('image', 0)
print(2)
cv2.setWindowTitle(window_name, title)
if width and height:
cv2.resizeWindow(window_name, width, height)
def show_image(image, fps):
global is_streaming
cv2.imshow('unique_window_identifier', image)
cv2.setWindowTitle("unique_window_identifier", f"fps= {fps}")
# Press Q to Quit
if cv2.waitKey(1) == ord('q'):
camera.image_received_cb.remove_callback(show_image)
is_streaming = False
def show_image(title, img):
cv2.namedWindow('window', cv2.WINDOW_NORMAL)
cv2.setWindowTitle('window', title)
cv2.imshow('window', img)
value = cv2.waitKey(0)
if value == 27:
os._exit(1)
return value
def show_image(__self__):
"""Creates the window and displays an image file"""
name = 'ImageViewer'
frame = cv2.imread(__self__, 1)
cv2.namedWindow(name, cv2.WINDOW_GUI_NORMAL)
cv2.setWindowProperty(name, cv2.WND_PROP_FULLSCREEN, cv2.WINDOW_KEEPRATIO)
cv2.setWindowTitle(name, __self__)
cv2.imshow(name, frame)
def main(args):
frame_interval = 3 # Number of frames after which to run face detection
fps_display_interval = 5 # seconds
frame_rate = 0
frame_count = 0
count = 0
save_path = str('/work/MachineLearning/my_dataset/train_aligned/' +
args.name)
if not os.path.exists(save_path):
os.mkdir(save_path)
print("Saving images into " + save_path)
video_capture = cv2.VideoCapture(0)
face_detection = face.Detection()
# face_recognition = face.Recognition()
start_time = time.time()
while True:
# Capture frame-by-frame
ret, frame = video_capture.read()
if (frame_count % frame_interval) == 0:
faces = face_detection.find_faces(frame)
# Check our current fps
end_time = time.time()
if (end_time - start_time) > fps_display_interval:
frame_rate = int(frame_count / (end_time - start_time))
start_time = time.time()
frame_count = 0
# add_overlays(frame, faces, frame_rate)
frame_count += 1
if len(faces) == 1:
frame = faces[0].image
cv2.imshow('Enrolling', frame)
cv2.setWindowTitle('Enrolling',
str(args.name) + " " + str(count + 1))
#cv2.putText(faces[0].image, 'Image: ' + str(frame_count+1), (0, 0), cv2.FONT_HERSHEY_SIMPLEX, 1,
# (255, 0, 0), thickness=2, lineType=2)
rgb_frame = frame[:, :, ::-1]
img = Image.fromarray(rgb_frame, "RGB")
if img is not None:
img.save(
os.path.join(save_path + "/" + str(count) + ".jpg"))
count += 1
#if frame_count > 100:
# break
if cv2.waitKey(1) & 0xFF == ord('q'):
break
# When everything is done, release the capture
video_capture.release()
cv2.destroyAllWindows()
def imshow(name, img):
# Make it 800px wide on major axis
(h, w, _) = img.shape
if h < w:
ratio = 800.0 / w
else:
ratio = 500.0 / h
h *= ratio
w *= ratio
cv.imshow("main", img)
cv.setWindowTitle("main", name)
cv.resizeWindow("main", int(w), int(h))
# while (True):
while cap.isOpened(): # 检查是否成功初始化，否则就 使用函数 cap.open()
# Capture frame-by-frame
ret, frame = cap.read() # ret 返回一个布尔值 True/False
# print('frame shape:',frame.shape)#(720, 1280, 3)
frame = cv2.flip(frame, flipCode=1) # 左右翻转,使用笔记本电脑摄像头才有用。
# flipCode：翻转方向：1：水平翻转；0：垂直翻转；-1：水平垂直翻转
# Our operations on the frame come here
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
# Display the resulting frame
cv2.imshow('frame', gray)
cv2.setWindowTitle('frame', 'COLOR_BGR2GRAY')
# Property=cv2.getWindowProperty('frame',0)#无用
# if cv2.waitKey(1) & 0xFF == ord('q'):#不行
# break
key = cv2.waitKey(delay=10)
if key == ord("q"):
break
# When everything done, release the capture
cap.release()
cv2.destroyAllWindows()
if not state.scale or out.shape[:2] == (h, w):
pointcloud(out, verts, texcoords, color_source)
else:
tmp = np.zeros((h, w, 3), dtype=np.uint8)
pointcloud(tmp, verts, texcoords, color_source)
tmp = cv2.resize(
tmp, out.shape[:2][::-1], interpolation=cv2.INTER_NEAREST)
np.putmask(out, tmp > 0, tmp)
if any(state.mouse_btns):
axes(out, view(state.pivot), state.rotation, thickness=4)
dt = time.time() - now
cv2.setWindowTitle(
state.WIN_NAME, "RealSense (%dx%d) %dFPS (%.2fms) %s" %
(w, h, 1.0/dt, dt*1000, "PAUSED" if state.paused else ""))
cv2.imshow(state.WIN_NAME, out)
key = cv2.waitKey(1)
if key == ord("r"):
state.reset()
if key == ord("p"):
state.paused ^= True
if key == ord("d"):
state.decimate = (state.decimate + 1) % 3
decimate.set_option(rs.option.filter_magnitude, 2 ** state.decimate)
def set_title(self, title):
cv2.setWindowTitle(self.name, title)
import cv2
import numpy as np
cap0 = cv2.VideoCapture(0)
cap1 = cv2.VideoCapture(1)
ret = cap0.set(3, 320)
ret = cap0.set(4, 240)
ret = cap1.set(3, 320)
ret = cap1.set(4, 240)
while cap0.isOpened() and cap1.isOpened():
ret0, frame0 = cap0.read()
ret1, frame1 = cap1.read()
if ret0:
cv2.imshow('frame0', frame0)
cv2.setWindowTitle('frame0','On Top')
if ret1:
cv2.imshow('frame1', frame1)
# cv2.moveWindow('frame1', x=frame0.shape[1], y=0)
cv2.moveWindow('frame1', x=320, y=40)
key = cv2.waitKey(delay=2)
if key == ord("q"):
break
# When everything done, release the capture
cap0.release()
cap1.release()
cv2.destroyAllWindows()