File size: 6,317 Bytes
88359db |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 |
import argparse
import time
import cv2
import numpy as np
import onnxruntime as ort
from imagenet_classes import IMAGENET2012_CLASSES
def parse_arguments():
parser = argparse.ArgumentParser(description="Video inference with TensorRT")
parser.add_argument("--output_video", type=str, help="Path to output video file")
parser.add_argument("--input_video", type=str, help="Path to input video file")
parser.add_argument("--webcam", action="store_true", help="Use webcam as input")
parser.add_argument(
"--live", action="store_true", help="View video live during inference"
)
return parser.parse_args()
def get_ort_session(model_path):
providers = [
(
"TensorrtExecutionProvider",
{
"device_id": 0,
"trt_max_workspace_size": 8589934592,
"trt_fp16_enable": True,
"trt_engine_cache_enable": True,
"trt_engine_cache_path": "./trt_cache",
"trt_force_sequential_engine_build": False,
"trt_max_partition_iterations": 10000,
"trt_min_subgraph_size": 1,
"trt_builder_optimization_level": 5,
"trt_timing_cache_enable": True,
},
),
]
return ort.InferenceSession(model_path, providers=providers)
def preprocess_frame(frame):
# Use cv2 for resizing instead of PIL for better performance
resized = cv2.resize(frame, (448, 448), interpolation=cv2.INTER_LINEAR)
img_numpy = cv2.cvtColor(resized, cv2.COLOR_BGR2RGB).astype(np.float32)
img_numpy = img_numpy.transpose(2, 0, 1)
img_numpy = np.expand_dims(img_numpy, axis=0)
return img_numpy
def get_top_predictions(output, top_k=5):
# Apply softmax
exp_output = np.exp(output - np.max(output, axis=1, keepdims=True))
probabilities = exp_output / np.sum(exp_output, axis=1, keepdims=True)
# Get top k indices and probabilities
top_indices = np.argsort(probabilities[0])[-top_k:][::-1]
top_probs = probabilities[0][top_indices] * 100
im_classes = list(IMAGENET2012_CLASSES.values())
class_names = [im_classes[i] for i in top_indices]
return list(zip(class_names, top_probs.tolist()))
def draw_predictions(frame, predictions, fps):
# Draw FPS in the top right corner with dark blue background
fps_text = f"FPS: {fps:.2f}"
(text_width, text_height), _ = cv2.getTextSize(
fps_text, cv2.FONT_HERSHEY_SIMPLEX, 0.7, 2
)
text_offset_x = frame.shape[1] - text_width - 10
text_offset_y = 30
box_coords = (
(text_offset_x - 5, text_offset_y + 5),
(text_offset_x + text_width + 5, text_offset_y - text_height - 5),
)
cv2.rectangle(
frame, box_coords[0], box_coords[1], (139, 0, 0), cv2.FILLED
) # Dark blue background
cv2.putText(
frame,
fps_text,
(text_offset_x, text_offset_y),
cv2.FONT_HERSHEY_SIMPLEX,
0.7,
(255, 255, 255), # White text
2,
)
# Draw predictions
for i, (name, prob) in enumerate(predictions):
text = f"{name}: {prob:.2f}%"
cv2.putText(
frame,
text,
(10, 30 + i * 30),
cv2.FONT_HERSHEY_SIMPLEX,
0.7,
(0, 255, 0),
2,
)
# Draw model name at the bottom of the frame with red background
model_name = "Model: eva02_large_patch14_448"
(text_width, text_height), _ = cv2.getTextSize(
model_name, cv2.FONT_HERSHEY_SIMPLEX, 0.7, 2
)
text_x = (frame.shape[1] - text_width) // 2
text_y = frame.shape[0] - 20
box_coords = (
(text_x - 5, text_y + 5),
(text_x + text_width + 5, text_y - text_height - 5),
)
cv2.rectangle(
frame, box_coords[0], box_coords[1], (0, 0, 255), cv2.FILLED
) # Red background
cv2.putText(
frame,
model_name,
(text_x, text_y),
cv2.FONT_HERSHEY_SIMPLEX,
0.7,
(255, 255, 255), # White text
2,
)
return frame
def process_video(input_path, output_path, session, live_view=False, use_webcam=False):
if use_webcam:
cap = cv2.VideoCapture(0)
else:
cap = cv2.VideoCapture(input_path)
width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
fps = int(cap.get(cv2.CAP_PROP_FPS))
out = None
if output_path:
fourcc = cv2.VideoWriter_fourcc(*"mp4v")
out = cv2.VideoWriter(output_path, fourcc, fps, (width, height))
input_name = session.get_inputs()[0].name
output_name = session.get_outputs()[0].name
frame_count = 0
total_time = 0
current_fps = 0
while cap.isOpened():
ret, frame = cap.read()
if not ret:
break
start_time = time.time()
preprocessed = preprocess_frame(frame)
output = session.run([output_name], {input_name: preprocessed})
predictions = get_top_predictions(output[0])
end_time = time.time()
frame_time = end_time - start_time
current_fps = 1 / frame_time
frame_with_predictions = draw_predictions(frame, predictions, current_fps)
if out:
out.write(frame_with_predictions)
if live_view:
cv2.imshow("Inference", frame_with_predictions)
if cv2.waitKey(1) & 0xFF == ord("q"):
break
total_time += frame_time
frame_count += 1
print(
f"Processed frame {frame_count}, Time: {frame_time:.3f}s, FPS: {current_fps:.2f}"
)
cap.release()
if out:
out.release()
cv2.destroyAllWindows()
avg_time = total_time / frame_count
print(f"Average processing time per frame: {avg_time:.3f}s")
print(f"Average FPS: {1/avg_time:.2f}")
def main():
args = parse_arguments()
session = get_ort_session("merged_model_compose.onnx")
if args.webcam:
process_video(None, args.output_video, session, args.live, use_webcam=True)
elif args.input_video:
process_video(args.input_video, args.output_video, session, args.live)
else:
print("Error: Please specify either --input_video or --webcam")
return
if __name__ == "__main__":
main()
|