def dnn_detector(frame):
gray_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
frame_height = frame.shape[0]
frame_width = frame.shape[1]
blob = cv2.dnn.blobFromImage(frame, 1.0, (300, 300), [104, 117, 123],
False, False)
net.setInput(blob)
detections = net.forward()
bboxes = []
idx = 0
offset = 15
x_pos, y_pos = 10, 40
for i in range(detections.shape[2]):
confidence = detections[0, 0, i, 2]
if confidence > conf_threshold:
idx += 1
x1 = int(detections[0, 0, i, 3] * frame_width)
y1 = int(detections[0, 0, i, 4] * frame_height)
x2 = int(detections[0, 0, i, 5] * frame_width)
y2 = int(detections[0, 0, i, 6] * frame_height)
bboxes.append([x1, y1, x2, y2])
face = [x1, y1, x2 - x1, y2 - y1]
if hist_eq:
gray_frame = cv2.equalizeHist(gray_frame)
model_in = get_model_compatible_input(gray_frame,
utils.bb_to_rect(face))
predicted_proba = model.predict(model_in)
predicted_label = np.argmax(predicted_proba[0])
cv2.rectangle(frame, (x1, y1), (x2, y2), (255, 0, 0), 2)
text = f"Person {idx}: {label2text[predicted_label]}"
utils.draw_text_with_backgroud(frame,
text,
x1 + 5,
y1,
font_scale=0.4)
text = f"Person {idx} : "
y_pos = y_pos + 2 * offset
utils.draw_text_with_backgroud(frame,
text,
x_pos,
y_pos,
font_scale=0.3,
box_coords_2=(2, -2))
for k, v in label2text.items():
text = f"{v}: {round(predicted_proba[0][k]*100, 3)}%"
y_pos = y_pos + offset
utils.draw_text_with_backgroud(frame,
text,
x_pos,
y_pos,
font_scale=0.3,
box_coords_2=(2, -2))
def cascade_detector(frame_orig, detector):
if not detector in ["haar", "lbp"]:
raise ValueError("Invalid cascade detector")
face_detector = (haar_detector if detector == "haar" else lbp_detector)
frame = frame_orig.copy()
gray_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
faces = face_detector.detectMultiScale(gray_frame, 1.32, 5)
for face in faces:
landmarks_coord = utils.get_landmarks(gray_frame,
frame,
utils.bb_to_rect(face),
annotate=args["landmarks"])
if args["delaunay_triangulation"]:
utils.annotate_delaunay_triangulation(frame,
landmarks_coord,
line_thickness=2)
if args["region_of_interest"]:
utils.annotate_ROI(frame, landmarks_coord)
if args["bounding_box"]:
x, y, w, h = face
cv2.rectangle(frame, (x, y), (x + w, y + h), (255, 0, 0), 2)
return frame
def _raw_face_landmarks(face_image, face_locations=None):
if face_locations is None:
face_locations = _raw_face_locations(face_image)
else:
face_locations = [
utils.bb_to_rect(face_location) for face_location in face_locations
]
return [
_post_predictor(face_image, face_location)
for face_location in face_locations
]
def haar_detector(frame):
gray_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
face_frame = np.zeros(gray_frame.shape, dtype="uint8")
offset = 15
x_pos, y_pos = 10, 40
faces = cascade_detector.detectMultiScale(gray_frame, 1.32, 5)
for idx, face in enumerate(faces):
if hist_eq:
gray_frame = cv2.equalizeHist(gray_frame)
img_arr = utils.align_face(gray_frame, utils.bb_to_rect(face),
desiredLeftEye)
face_frame = cv2.resize(img_arr, (48, 48),
interpolation=cv2.INTER_CUBIC)
img_arr = utils.preprocess_img(img_arr, resize=False)
predicted_proba = model.predict(img_arr)
predicted_label = np.argmax(predicted_proba[0])
x, y, w, h = face
cv2.rectangle(frame, (x, y), (x + w, y + h), (255, 0, 0), 2)
text = f"Person {idx+1}: {label2text[predicted_label]}"
utils.draw_text_with_backgroud(frame, text, x + 5, y, font_scale=0.4)
text = f"Person {idx+1} : "
y_pos = y_pos + 2 * offset
utils.draw_text_with_backgroud(frame,
text,
x_pos,
y_pos,
font_scale=0.3,
box_coords_2=(2, -2))
for k, v in label2text.items():
text = f"{v}: {round(predicted_proba[0][k]*100, 3)}%"
y_pos = y_pos + offset
utils.draw_text_with_backgroud(frame,
text,
x_pos,
y_pos,
font_scale=0.3,
box_coords_2=(2, -2))
return frame, face_frame
def dnn_detector(frame_orig):
frame = frame_orig.copy()
frame_height = frame.shape[0]
frame_width = frame.shape[1]
blob = cv2.dnn.blobFromImage(frame, 1.0, (300, 300), [104, 117, 123],
False, False)
net.setInput(blob)
detections = net.forward()
bboxes = []
for i in range(detections.shape[2]):
confidence = detections[0, 0, i, 2]
if confidence > conf_threshold:
x1 = int(detections[0, 0, i, 3] * frame_width)
y1 = int(detections[0, 0, i, 4] * frame_height)
x2 = int(detections[0, 0, i, 5] * frame_width)
y2 = int(detections[0, 0, i, 6] * frame_height)
bboxes.append([x1, y1, x2, y2])
face = [x1, y1, x2 - x1, y2 - y1]
gray_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
landmarks_coord = utils.get_landmarks(gray_frame,
frame,
utils.bb_to_rect(face),
annotate=args["landmarks"])
if args["delaunay_triangulation"]:
utils.annotate_delaunay_triangulation(frame,
landmarks_coord,
line_thickness=2)
if args["region_of_interest"]:
utils.annotate_ROI(frame, landmarks_coord)
if args["bounding_box"]:
cv2.rectangle(frame, (x1, y1), (x2, y2), (255, 0, 0), 2)
return frame
def dnn_detector(frame):
frame_height = frame.shape[0]
frame_width = frame.shape[1]
blob = cv2.dnn.blobFromImage(frame, 1.0, (300, 300), [104, 117, 123],
False, False)
net.setInput(blob)
detections = net.forward()
bboxes = []
idx = 0
offset = 15
x_pos, y_pos = 10, 40
gray_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
face_frame = np.zeros(gray_frame.shape, dtype="uint8")
for i in range(detections.shape[2]):
confidence = detections[0, 0, i, 2]
if confidence > conf_threshold:
idx += 1
x1 = int(detections[0, 0, i, 3] * frame_width)
y1 = int(detections[0, 0, i, 4] * frame_height)
x2 = int(detections[0, 0, i, 5] * frame_width)
y2 = int(detections[0, 0, i, 6] * frame_height)
bboxes.append([x1, y1, x2, y2])
face = [x1, y1, x2 - x1, y2 - y1]
if hist_eq:
gray_frame = cv2.equalizeHist(gray_frame)
img_arr = utils.align_face(gray_frame, utils.bb_to_rect(face),
desiredLeftEye)
face_frame = cv2.resize(img_arr, (48, 48),
interpolation=cv2.INTER_CUBIC)
img_arr = utils.preprocess_img(img_arr, resize=False)
predicted_proba = model.predict(img_arr)
predicted_label = np.argmax(predicted_proba[0])
cv2.rectangle(frame, (x1, y1), (x2, y2), (255, 0, 0), 2)
text = f"Person {idx}: {label2text[predicted_label]}"
utils.draw_text_with_backgroud(frame,
text,
x1 + 5,
y1,
font_scale=0.4)
text = f"Person {idx} : "
y_pos = y_pos + 2 * offset
utils.draw_text_with_backgroud(frame,
text,
x_pos,
y_pos,
font_scale=0.3,
box_coords_2=(2, -2))
for k, v in label2text.items():
text = f"{v}: {round(predicted_proba[0][k]*100, 3)}%"
y_pos = y_pos + offset
utils.draw_text_with_backgroud(frame,
text,
x_pos,
y_pos,
font_scale=0.3,
box_coords_2=(2, -2))
return frame, face_frame
utils.imshow_scaled("Images", imref)
console.print(Rule())
console.print(
"Press ENTER to validate, else press any other key to start again.",
style="yellow bold blink")
key = cv.waitKey(0)
if key == 13: # if ENTER is pressed, exit loop
break
# Initial length and clamp ROI center
l0 = utils.compute_length(ROI1, ROI2)
center0 = utils.ROIcenter(ROIcorner)
# Init trackers
tracker1 = dlib.correlation_tracker()
tracker1.start_track(imrefbw, utils.bb_to_rect(ROI1))
tracker2 = dlib.correlation_tracker()
tracker2.start_track(imrefbw, utils.bb_to_rect(ROI2))
tracker3 = dlib.correlation_tracker()
tracker3.start_track(imrefbw, utils.bb_to_rect(ROIcorner))
# Init strain and displacement plot
plt.ion()
fig, ax1 = plt.subplots()
frames = []
strain = []
disp = []
line1, = ax1.plot(frames, strain, "ro")
ax1.set_xlabel("Image")
ax1.set_ylabel("Strain [%]", color="red")
ax1.tick_params(axis="y", labelcolor="red")