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test_camera_light_onnx.py
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test_camera_light_onnx.py
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"""
This code uses the onnx model to detect faces from live video or cameras.
Use a much faster face detector: https://github.com/Linzaer/Ultra-Light-Fast-Generic-Face-Detector-1MB
Date: 3/26/2020 by Cunjian Chen (ccunjian@gmail.com)
"""
import time
import cv2
import numpy as np
import onnx
import vision.utils.box_utils_numpy as box_utils
from caffe2.python.onnx import backend
# onnx runtime
import onnxruntime as ort
# import libraries for landmark
from common.utils import BBox,drawLandmark,drawLandmark_multiple
from PIL import Image
import torchvision.transforms as transforms
# setup the parameters
resize = transforms.Resize([56, 56])
to_tensor = transforms.ToTensor()
normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
# import the landmark detection models
import onnx
import onnxruntime
onnx_model_landmark = onnx.load("onnx/landmark_detection_56_se_external.onnx")
onnx.checker.check_model(onnx_model_landmark)
ort_session_landmark = onnxruntime.InferenceSession("onnx/landmark_detection_56_se_external.onnx")
def to_numpy(tensor):
return tensor.detach().cpu().numpy() if tensor.requires_grad else tensor.cpu().numpy()
# face detection setting
def predict(width, height, confidences, boxes, prob_threshold, iou_threshold=0.3, top_k=-1):
boxes = boxes[0]
confidences = confidences[0]
picked_box_probs = []
picked_labels = []
for class_index in range(1, confidences.shape[1]):
probs = confidences[:, class_index]
mask = probs > prob_threshold
probs = probs[mask]
if probs.shape[0] == 0:
continue
subset_boxes = boxes[mask, :]
box_probs = np.concatenate([subset_boxes, probs.reshape(-1, 1)], axis=1)
box_probs = box_utils.hard_nms(box_probs,
iou_threshold=iou_threshold,
top_k=top_k,
)
picked_box_probs.append(box_probs)
picked_labels.extend([class_index] * box_probs.shape[0])
if not picked_box_probs:
return np.array([]), np.array([]), np.array([])
picked_box_probs = np.concatenate(picked_box_probs)
picked_box_probs[:, 0] *= width
picked_box_probs[:, 1] *= height
picked_box_probs[:, 2] *= width
picked_box_probs[:, 3] *= height
return picked_box_probs[:, :4].astype(np.int32), np.array(picked_labels), picked_box_probs[:, 4]
label_path = "models/voc-model-labels.txt"
onnx_path = "models/onnx/version-RFB-320.onnx"
class_names = [name.strip() for name in open(label_path).readlines()]
predictor = onnx.load(onnx_path)
onnx.checker.check_model(predictor)
onnx.helper.printable_graph(predictor.graph)
predictor = backend.prepare(predictor, device="CPU") # default CPU
ort_session = ort.InferenceSession(onnx_path)
input_name = ort_session.get_inputs()[0].name
# perform face detection and alignment from camera
cap = cv2.VideoCapture(0) # capture from camera
threshold = 0.7
sum = 0
while True:
ret, orig_image = cap.read()
if orig_image is None:
print("no img")
break
image = cv2.cvtColor(orig_image, cv2.COLOR_BGR2RGB)
image = cv2.resize(image, (320, 240))
# image = cv2.resize(image, (640, 480))
image_mean = np.array([127, 127, 127])
image = (image - image_mean) / 128
image = np.transpose(image, [2, 0, 1])
image = np.expand_dims(image, axis=0)
image = image.astype(np.float32)
# confidences, boxes = predictor.run(image)
time_time = time.time()
confidences, boxes = ort_session.run(None, {input_name: image})
print("cost time:{}".format(time.time() - time_time))
boxes, labels, probs = predict(orig_image.shape[1], orig_image.shape[0], confidences, boxes, threshold)
for i in range(boxes.shape[0]):
box = boxes[i, :]
label = f"{class_names[labels[i]]}: {probs[i]:.2f}"
#cv2.rectangle(orig_image, (box[0], box[1]), (box[2], box[3]), (255, 255, 0), 4)
# perform landmark detection
out_size = 56
img=orig_image.copy()
height,width,_=img.shape
x1=box[0]
y1=box[1]
x2=box[2]
y2=box[3]
w = x2 - x1 + 1
h = y2 - y1 + 1
size = int(max([w, h])*1.1)
cx = x1 + w//2
cy = y1 + h//2
x1 = cx - size//2
x2 = x1 + size
y1 = cy - size//2
y2 = y1 + size
dx = max(0, -x1)
dy = max(0, -y1)
x1 = max(0, x1)
y1 = max(0, y1)
edx = max(0, x2 - width)
edy = max(0, y2 - height)
x2 = min(width, x2)
y2 = min(height, y2)
new_bbox = list(map(int, [x1, x2, y1, y2]))
new_bbox = BBox(new_bbox)
cropped=img[new_bbox.top:new_bbox.bottom,new_bbox.left:new_bbox.right]
if (dx > 0 or dy > 0 or edx > 0 or edy > 0):
cropped = cv2.copyMakeBorder(cropped, int(dy), int(edy), int(dx), int(edx), cv2.BORDER_CONSTANT, 0)
cropped_face = cv2.resize(cropped, (out_size, out_size))
if cropped_face.shape[0]<=0 or cropped_face.shape[1]<=0:
continue
cropped_face = cv2.cvtColor(cropped_face, cv2.COLOR_BGR2RGB)
cropped_face = Image.fromarray(cropped_face)
test_face = resize(cropped_face)
test_face = to_tensor(test_face)
test_face = normalize(test_face)
test_face.unsqueeze_(0)
start = time.time()
ort_inputs = {ort_session.get_inputs()[0].name: to_numpy(test_face)}
ort_outs = ort_session_landmark.run(None, ort_inputs)
end = time.time()
print('Time: {:.6f}s.'.format(end - start))
landmark = ort_outs[0]
landmark = landmark.reshape(-1,2)
landmark = new_bbox.reprojectLandmark(landmark)
orig_image = drawLandmark_multiple(orig_image, new_bbox, landmark)
sum += boxes.shape[0]
orig_image = cv2.resize(orig_image, (0, 0), fx=0.7, fy=0.7)
cv2.imshow('annotated', orig_image)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
cap.release()
cv2.destroyAllWindows()
print("sum:{}".format(sum))