rval, frame = vc.read()
else:
rval = False
while rval:
while frame.shape[0] < arrows.shape[0] or frame.shape[
1] < arrows.shape[1]:
scale_percent = 30 # percent of original size
width = int(arrows.shape[1] * scale_percent / 100)
height = int(arrows.shape[0] * scale_percent / 100)
dim = (width, height)
arrows = cv2.resize(arrows, dim, interpolation=cv2.INTER_AREA)
sofa = cv2.resize(sofa, dim, interpolation=cv2.INTER_AREA)
width, height, inference_time, results = yolo.inference(frame)
for detection in results:
id, name, confidence, x, y, w, h = detection
cx = x + (w / 2)
cy = y + (h / 2)
# draw a bounding box rectangle and label on the image
color = (0, 255, 255)
cv2.rectangle(frame, (x, y), (x + w, y + h), color, 2)
x_offset = x
y_offset = y
if x_offset < 0:
x_offset = 0
elif x_offset + arrows.shape[1] > frame.shape[1]:
x_offset = frame.shape[1] - arrows.shape[1]
if y_offset < 0:
def press():
new_model = load_model('Letter_Model_V3_999_1')
alph = 'ABCDEFGHIJKLMNOPQRSTUVWXY'
alph_dict = {}
for i, n in enumerate(alph):
alph_dict.update({i: n})
camera = cv2.VideoCapture(0)
#cv2.namedWindow("test")
img_counter = 0
List_alph = [i for i in alph]
letter = random.choice(List_alph)
while True:
ret, frame = camera.read()
if not ret:
print("failed to grab frame")
break
color = (0, 255, 255)
cv2.putText(frame, 'Please try to sign the letter: ' + letter,
(20, 20), cv2.FONT_HERSHEY_SIMPLEX, 0.5, color, 2)
cv2.imshow("Gesture Detector", frame)
k = cv2.waitKey(1)
if k % 256 == 27:
# ESC pressed
print("Escape hit, closing...")
break
elif k % 256 == 32:
# SPACE pressed
img_name = "opencv_frame_{}.png".format(img_counter)
print("{} written!".format(img_name))
x = frame
img_counter += 1
yolo = YOLO(
"/Users/Denny/Desktop/Hack_The_Northeast/yolo-hand-detection/models/cross-hands.cfg",
"/Users/Denny/Desktop/Hack_The_Northeast/yolo-hand-detection/models/cross-hands.weights",
["hand"])
width, height, inference_time, results = yolo.inference(x)
frame = x
for detection in results:
id, name, confidence, x, y, w, h = detection
cx = x + (w / 2)
cy = y + (h / 2)
crop_img = frame[y - 50:y + h + 50, x - 50:x + w + 50]
#cv2.waitKey(0)
im = Image.fromarray(crop_img)
im.save("your_file.png")
im = cv2.imread('your_file.png', 0)
new_img = cv2.resize(im, (28, 28))
cv2.imshow("preview", new_img)
cv2.waitKey(0)
the_class = new_model.predict_classes(
new_img.reshape(1, 28, 28, 1))
Answer = alph_dict[the_class[0]]
# draw a bounding box rectangle and label on the image
color = (0, 255, 255)
#text = "%s (%s)" % (name, round(confidence, 2))
if Answer == letter:
cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0),
2)
text = 'Correct Answer for: ' + Answer
cv2.putText(frame, text, (x, y - 5),
cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2)
else:
cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 0, 255),
2)
text = 'Try again!'
cv2.putText(frame, text, (x, y - 5),
cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255), 2)
cv2.imshow("preview", frame)
# if Answer==tkvar:
# cv2.putText(frame, "Worked well", (x, y), cv2.FONT_HERSHEY_SIMPLEX,
# 0.5, color, 2)
# else:
# cv2.putText(frame, "Try again", (x, y), cv2.FONT_HERSHEY_SIMPLEX,
# 0.5, color, 2)
camera.release()
cv2.destroyAllWindows()
def scrollV(cap, network, device, size, confidence):
if network == "normal":
print("loading yolo...")
yolo = YOLO("models/cross-hands.cfg", "models/cross-hands.weights",
["hand"])
elif network == "prn":
print("loading yolo-tiny-prn...")
yolo = YOLO("models/cross-hands-tiny-prn.cfg",
"models/cross-hands-tiny-prn.weights", ["hand"])
else:
print("loading yolo-tiny...")
yolo = YOLO("models/cross-hands-tiny.cfg",
"models/cross-hands-tiny.weights", ["hand"])
yolo.size = size
yolo.confidence = confidence
cnt = 0
curr = 0
prev = 0
exit = 0
rval, frame = cap.read()
while True:
width, height, inference_time, results = yolo.inference(frame)
if len(results) == 1:
exit = 0
cnt += 1
id, name, confidence, x, y, w, h = results[0]
cx = x + (w // 2)
cy = y + (h // 2)
if cnt <= 5:
curr = cy
color = (0, 255, 255)
cv2.circle(frame, (cx, cy), 10, color, -1)
#print("Cy: ", cy)
if cnt % 10 == 0 and cnt > 5:
prev = curr
curr = cy
#print("Prev: ",prev)
#print("Curr: ", curr)
clicks = prev - curr
#print(clicks)
#if clicks>30 and clicks<170:
clicks = clicks // 2
if abs(clicks) > 10:
pyautogui.scroll(clicks)
else:
exit += 1
if exit > 50:
print(exit)
break
cv2.imshow("preview", frame)
rval, frame = cap.read()
key = cv2.waitKey(1)
if key == 27: # exit on ESC
break
cv2.destroyWindow("preview")
def reading_video(filename):
ap = argparse.ArgumentParser()
ap.add_argument('-n',
'--network',
default="normal",
help='Network Type: normal / tiny / prn / v4-tiny')
ap.add_argument('-d', '--device', default=0, help='Device to use')
ap.add_argument('-v',
'--videos',
default="videos",
help='Path to videos or video file')
ap.add_argument('-s', '--size', default=416, help='Size for yolo')
ap.add_argument('-c',
'--confidence',
default=0.2,
help='Confidence for yolo')
ap.add_argument("-f",
"--fff",
help="a dummy argument to fool ipython",
default="1")
args = ap.parse_args()
if args.network == "normal":
print("loading yolo...")
yolo = YOLO("models/cross-hands.cfg", "models/cross-hands.weights",
["hand"])
elif args.network == "prn":
print("loading yolo-tiny-prn...")
yolo = YOLO("models/cross-hands-tiny-prn.cfg",
"models/cross-hands-tiny-prn.weights", ["hand"])
elif args.network == "v4-tiny":
print("loading yolov4-tiny-prn...")
yolo = YOLO("models/cross-hands-yolov4-tiny.cfg",
"models/cross-hands-yolov4-tiny.weights", ["hand"])
else:
print("loading yolo-tiny...")
yolo = YOLO("models/cross-hands-tiny.cfg",
"models/cross-hands-tiny.weights", ["hand"])
yolo.size = int(args.size)
yolo.confidence = float(args.confidence)
# opening a window called preview
cv2.namedWindow("preview")
# to open and capture frames from video
vc = cv2.VideoCapture(filename)
if vc.isOpened(): # try to get the first frame
rval, frame = vc.read()
# to get the first frame
else:
# some error causes the video to not open
rval = False
while (vc.isOpened()):
# Applying YOLO on the frames
width, height, inference_time, results = yolo.inference(frame)
for detection in results:
id, name, confidence, x, y, w, h = detection
cx = x + (w / 2)
cy = y + (h / 2)
# draw a bounding box rectangle and label on the image
color = (0, 255, 255)
cv2.rectangle(frame, (x, y), (x + w, y + h), color, 2)
text = "%s (%s)" % (name, round(confidence, 2))
# put a text on the detected hand with the confidence ratio
cv2.putText(frame, text, (x, y - 5), cv2.FONT_HERSHEY_SIMPLEX, 0.5,
color, 2)
cv2.imshow("preview", frame)
rval, frame = vc.read()
# to close the window we need to click on the ESC button
key = cv2.waitKey(20)
if key == 27: # exit on ESC
break
cv2.destroyWindow("preview")
vc.release()