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import torch
import gradio as gr
import cv2
import numpy as np
import random
import numpy as np
from models.experimental import attempt_load
from utils.general import check_img_size, non_max_suppression, \
scale_coords
from utils.plots import plot_one_box
from utils.torch_utils import time_synchronized
import time
def letterbox(im, new_shape=(640, 640), color=(114, 114, 114), auto=True, scaleup=True, stride=32):
# Resize and pad image while meeting stride-multiple constraints
shape = im.shape[:2] # current shape [height, width]
if isinstance(new_shape, int):
new_shape = (new_shape, new_shape)
# Scale ratio (new / old)
r = min(new_shape[0] / shape[0], new_shape[1] / shape[1])
if not scaleup: # only scale down, do not scale up (for better val mAP)
r = min(r, 1.0)
# Compute padding
new_unpad = int(round(shape[1] * r)), int(round(shape[0] * r))
dw, dh = new_shape[1] - new_unpad[0], new_shape[0] - new_unpad[1] # wh padding
if auto: # minimum rectangle
dw, dh = np.mod(dw, stride), np.mod(dh, stride) # wh padding
dw /= 2 # divide padding into 2 sides
dh /= 2
if shape[::-1] != new_unpad: # resize
im = cv2.resize(im, new_unpad, interpolation=cv2.INTER_LINEAR)
top, bottom = int(round(dh - 0.1)), int(round(dh + 0.1))
left, right = int(round(dw - 0.1)), int(round(dw + 0.1))
im = cv2.copyMakeBorder(im, top, bottom, left, right, cv2.BORDER_CONSTANT, value=color) # add border
return im, r, (dw, dh)
names = ["animal",
"autorickshaw",
"bicycle",
"bus",
"car",
"motorcycle",
"person",
"rider",
"traffic light",
"traffic sign",
"truck"
]
#colors = [[random.randint(0, 255) for _ in range(3)] for _ in names]
colors = {
"animal": [246,198, 145],
"autorickshaw": [255,204, 54],
"bicycle": [119,11, 32],
"bus": [ 0,60,100],
"car": [ 0,0,142],
"motorcycle": [ 0,0,230],
"person": [220,20, 60],
"rider": [255,0, 0],
"traffic light": [250,170, 30],
"traffic sign": [220,220, 0],
"truck": [ 0,0, 70]
}
def detect(img,model,device,iou_threshold=0.45,confidence_threshold=0.25):
imgsz = 640
img = np.array(img)
stride = int(model.stride.max()) # model stride
imgsz = check_img_size(imgsz, s=stride) # check img_size
# Get names and colors
names = model.module.names if hasattr(model, 'module') else model.names
# Run inference
imgs = img.copy() # for NMS
image, ratio, dwdh = letterbox(img, auto=False)
image = image.transpose((2, 0, 1))
img = torch.from_numpy(image).to(device)
img = img.float() # uint8 to fp16/32
img /= 255.0 # 0 - 255 to 0.0 - 1.0
if img.ndimension() == 3:
img = img.unsqueeze(0)
# Inference
t1 = time_synchronized()
start = time.time()
with torch.no_grad(): # Calculating gradients would cause a GPU memory leak
pred = model(img,augment=True)[0]
fps_inference = 1/(time.time()-start)
t2 = time_synchronized()
# Apply NMS
pred = non_max_suppression(pred, confidence_threshold, iou_threshold, classes=None, agnostic=True)
t3 = time_synchronized()
for i, det in enumerate(pred): # detections per image
if len(det):
# Rescale boxes from img_size to im0 size
det[:, :4] = scale_coords(img.shape[2:], det[:, :4], imgs.shape).round()
# Write results
for *xyxy, conf, cls in reversed(det):
label = f'{names[int(cls)]} {conf:.2f}'
plot_one_box(xyxy, imgs, label=label, color=colors[names[int(cls)]], line_thickness=2)
return imgs,fps_inference
def inference(img,model_link,iou_threshold,confidence_threshold):
print(model_link)
device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
# Load model
model_path = 'weights/'+str(model_link)+'.pt'
model = attempt_load(model_path, map_location=device)
return detect(img,model,device,iou_threshold,confidence_threshold)
def inference2(video,model_link,iou_threshold,confidence_threshold):
print(model_link)
device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
# Load model
model_path = 'weights/'+str(model_link)+'.pt'
model = attempt_load(model_path, map_location=device)
frames = cv2.VideoCapture(video)
fps = frames.get(cv2.CAP_PROP_FPS)
image_size = (int(frames.get(cv2.CAP_PROP_FRAME_WIDTH)),int(frames.get(cv2.CAP_PROP_FRAME_HEIGHT)))
finalVideo = cv2.VideoWriter('output.mp4',cv2.VideoWriter_fourcc(*'VP90'), fps, image_size)
fps_video = []
while frames.isOpened():
ret,frame = frames.read()
if not ret:
break
frame,fps = detect(frame,model,device,iou_threshold,confidence_threshold)
print(fps)
fps_video.append[fps]
finalVideo.write(frame)
frames.release()
finalVideo.release()
return 'output.mp4',np.mean(fps_video)
examples_images = ['data/images/1.jpg',
'data/images/2.jpg',
'data/images/bus.jpg',
'data/images/3.jpg']
examples_videos = ['data/video/1.mp4','data/video/2.mp4']
models = ['yolov7','yolov7t']
with gr.Blocks() as demo:
gr.Markdown("## YOLOv7 Inference")
with gr.Tab("Image"):
gr.Markdown("## YOLOv7 Inference on Image")
with gr.Row():
image_input = gr.Image(type='pil', label="Input Image", source="upload")
image_output = gr.Image(type='pil', label="Output Image", source="upload")
fps_image = gr.Number(0,label='FPS')
image_drop = gr.Dropdown(choices=models,value=models[0])
image_iou_threshold = gr.Slider(label="IOU Threshold",interactive=True, minimum=0.0, maximum=1.0, value=0.45)
image_conf_threshold = gr.Slider(label="Confidence Threshold",interactive=True, minimum=0.0, maximum=1.0, value=0.25)
gr.Examples(examples=examples_images,inputs=image_input,outputs=image_output)
text_button = gr.Button("Detect")
with gr.Tab("Video"):
gr.Markdown("## YOLOv7 Inference on Video")
with gr.Row():
video_input = gr.Video(type='pil', label="Input Video", source="upload")
video_output = gr.Video(type="pil", label="Output Video",format="mp4")
fps_video = gr.Number(0,label='FPS')
video_drop = gr.Dropdown(choices=models,value=models[0])
video_iou_threshold = gr.Slider(label="IOU Threshold",interactive=True, minimum=0.0, maximum=1.0, value=0.45)
video_conf_threshold = gr.Slider(label="Confidence Threshold",interactive=True, minimum=0.0, maximum=1.0, value=0.25)
gr.Examples(examples=examples_videos,inputs=video_input,outputs=video_output)
video_button = gr.Button("Detect")
# with gr.Tab("Webcam Video"):
# gr.Markdown("## YOLOv7 Inference on Webcam Video")
# gr.Markdown("Coming Soon")
text_button.click(inference, inputs=[image_input,image_drop,
image_iou_threshold,image_conf_threshold],
outputs=[image_output,fps_image])
video_button.click(inference2, inputs=[video_input,video_drop,
video_iou_threshold,video_conf_threshold],
outputs=[video_output,fps_video])
demo.launch(debug=True,enable_queue=True) |