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test_face.py
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test_face.py
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import cv2
import numpy as np
import skimage
import skimage.transform
import json, base64
import time
import scipy
from tflite_runtime.interpreter import Interpreter
from anchors import ANCHOR
from cv_utils import decode_yolov3, preprocess
from copy import deepcopy
OBJ_THRES = 0.7
NMS_THRES = 0.4
VARIANCE = [0.1, 0.2]
FACE_DIMENSION = [96, 112]
def pred_boxes(box_output, score_output, ldmk_output):
'''
generate box information from output
:param box_output: 3160*4
:param score_output: 3160*2
:param ldmk_output: 3160*10
:return:
'''
# select boxes greater than threshold probability
prob = scipy.special.softmax(score_output, axis=-1)
pre_select_boxes_mask = prob[:, 1] > OBJ_THRES
pre_select_boxes_index = np.where(pre_select_boxes_mask)[0]
pre_select_anchor = ANCHOR[pre_select_boxes_index, :]
# calculate coordinate
box_cord = box_output[pre_select_boxes_index, :]
box_cord = np.concatenate((
pre_select_anchor[:, :2] + box_cord[:, :2] * VARIANCE[0] * pre_select_anchor[:, 2:],
pre_select_anchor[:, 2:] * np.exp(box_cord[:, 2:] * VARIANCE[1])), 1)
box_cord[:, :2] -= box_cord[:, 2:] / 2
box_cord[:, 2:] += box_cord[:, :2]
# calculate ldmk coordinate
ldmk = ldmk_output[pre_select_boxes_index, :]
ldmk[:, 0::2] = pre_select_anchor[:, 0:1] + ldmk[:, 0::2] * VARIANCE[0] * pre_select_anchor[:, 2:3]
ldmk[:, 1::2] = pre_select_anchor[:, 1:2] + ldmk[:, 1::2] * VARIANCE[0] * pre_select_anchor[:, 3:4]
# get prob
box_prob = prob[pre_select_boxes_index, 1]
return box_prob, box_cord, ldmk
def nms_oneclass(bbox: np.ndarray, score: np.ndarray, thresh: float = NMS_THRES) -> np.ndarray:
'''
non maximum suppression by iou
:param bbox:
:param score:
:param thresh:
:return:
'''
x1 = bbox[:, 0]
y1 = bbox[:, 1]
x2 = bbox[:, 2]
y2 = bbox[:, 3]
areas = (x2 - x1 + 1) * (y2 - y1 + 1)
order = score.argsort()[::-1]
keep = []
while order.size > 0:
i = order[0]
keep.append(i)
xx1 = np.maximum(x1[i], x1[order[1:]])
yy1 = np.maximum(y1[i], y1[order[1:]])
xx2 = np.minimum(x2[i], x2[order[1:]])
yy2 = np.minimum(y2[i], y2[order[1:]])
w = np.maximum(0.0, xx2 - xx1 + 1)
h = np.maximum(0.0, yy2 - yy1 + 1)
inter = w * h
ovr = inter / (areas[i] + areas[order[1:]] - inter)
inds = np.where(ovr <= thresh)[0]
order = order[inds + 1]
return np.array(keep)
class NetworkExecutor(object):
def __init__(self, model_file):
self.interpreter = Interpreter(model_file, num_threads=3)
self.interpreter.allocate_tensors()
_, self.input_height, self.input_width, _ = self.interpreter.get_input_details()[0]['shape']
self.tensor_index = self.interpreter.get_input_details()[0]['index']
def get_output_tensors(self):
output_details = self.interpreter.get_output_details()
tensor_list = []
for output in output_details:
tensor = np.squeeze(self.interpreter.get_tensor(output['index']))
tensor_list.append(tensor)
return tensor_list
def run(self, image):
#if image.shape[1:2] != (self.input_height, self.input_width):
# img = cv2.resize(image, (self.input_width, self.input_height))
#img = preprocess(img)
img = image
img = np.expand_dims(img, 0)
self.interpreter.set_tensor(self.tensor_index, img)
self.interpreter.invoke()
return self.get_output_tensors()
class FaceDetector():
def __init__(self, model_file, image_height, image_width):
self.fd_model = NetworkExecutor(model_file)
self.image_size = [320, 240]
self.resize_factors = [image_width / self.fd_model.input_width,
image_height / self.fd_model.input_height]
print(self.resize_factors)
def detect_face(self, image):
bbox, ldmk, prob = self.fd_model.run(image)
# post processing
pred_prob, pred_bbox, pred_ldmk = pred_boxes(bbox, prob, ldmk)
# calculate bbox corrdinate
pred_bbox_pixel = pred_bbox * np.tile(self.image_size, 2)
pred_ldmk_pixel = pred_ldmk * np.tile(self.image_size, 5)
# nms
keep = nms_oneclass(pred_bbox_pixel, pred_prob)
if len(keep) > 0:
pred_bbox_pixel = pred_bbox_pixel[keep, :]
pred_ldmk_pixel = pred_ldmk_pixel[keep, :]
pred_prob = pred_prob[keep]
else:
return [], [], []
return pred_bbox_pixel, pred_ldmk_pixel, pred_prob
def draw_bounding_boxes(self, frame, detections):
color = (0, 255, 0)
label_color = (125, 125, 125)
pred_bbox_pixel, pred_ldmk_pixel, pred_prob = detections
for i in range(len(pred_bbox_pixel)):
box = [d for d in pred_bbox_pixel[i]]
# Obtain frame size and resized bounding box positions
frame_height, frame_width = frame.shape[:2]
x_min, x_max = [int(position * self.resize_factors[0]) for position in box[0::2]]
y_min, y_max = [int(position * self.resize_factors[1]) for position in box[1::2]]
# Ensure box stays within the frame
x_min, y_min = max(0, x_min), max(0, y_min)
x_max, y_max = min(frame_width, x_max), min(frame_height, y_max)
# Draw bounding box around detected object
cv2.rectangle(frame, (x_min, y_min), (x_max, y_max), color, 2)
# Create label for detected object class
label = 'ID: {} Name: {} {:.2f}%'.format(0, 0, 0)
label_color = (255, 255, 255)
# Make sure label always stays on-screen
x_text, y_text = cv2.getTextSize(label, cv2.FONT_HERSHEY_DUPLEX, 1, 1)[0][:2]
lbl_box_xy_min = (x_min, y_min if y_min<25 else y_min - y_text)
lbl_box_xy_max = (x_min + int(0.75 * x_text), y_min + y_text if y_min<25 else y_min)
lbl_text_pos = (x_min + 5, y_min + 16 if y_min<25 else y_min - 5)
# Add label and confidence value
cv2.rectangle(frame, lbl_box_xy_min, lbl_box_xy_max, color, -1)
cv2.putText(frame, label, lbl_text_pos, cv2.FONT_HERSHEY_DUPLEX, 0.70, label_color, 1, cv2.LINE_AA)
kpts = ((pred_ldmk_pixel[i]).reshape(5, 2)*self.resize_factors).astype(int)
for kpt in kpts:
cv2.circle(frame, (kpt[0], kpt[1]), 5, (255, 0, 0), 2)
return frame
if __name__ == "__main__":
orig_img = cv2.imread("test.jpg")
face_test = FaceDetector("face_rec_models/ulffd_landmark.tflite", orig_img.shape[0], orig_img.shape[1])
img = cv2.resize(orig_img, (320, 240)) # resize the images
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB).astype('float32') #flipped[...,::-1].copy().astype('float32') #
img = (img / 255) - 0.5 # normalization
pred_bbox_pixel, pred_ldmk_pixel, pred_prob = face_test.detect_face(img)
print(pred_bbox_pixel, pred_ldmk_pixel, pred_prob)
detections = pred_bbox_pixel, pred_ldmk_pixel, pred_prob
orig_img = face_test.draw_bounding_boxes(orig_img, detections)
print(orig_img.shape)
cv2.imwrite('processed.jpg', orig_img)