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import gradio as gr
import os
os.system('git clone https://github.com/WongKinYiu/yolov7.git')
def detect(inp):
os.system('python ./yolov7/detect.py --weights best.pt --conf 0.25 --img-size 640 --source f{inp} "--project","./yolov7/runs/detect ')
otp=inp.split('/')[2]
return f"./yolov7/runs/detect/exp/*"
#f"./yolov7/runs/detect/exp/{otp}"
import argparse
from pathlib import Path
import cv2
import torch
import numpy as np
from numpy import random
from . import models
from models.experimental import attempt_load
from utils.datasets import LoadStreams, LoadImages
from utils.general import check_img_size, check_requirements, check_imshow, non_max_suppression, apply_classifier,scale_coords, xyxy2xywh, strip_optimizer, set_logging, increment_path
from utils.plots import plot_one_box
from utils.torch_utils import select_device, time_synchronized
def letterbox(img, new_shape=(640, 640), color=(114, 114, 114), auto=True, scaleFill=False, scaleup=True, stride=32):
# Resize and pad image while meeting stride-multiple constraints
shape = img.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 test mAP)
r = min(r, 1.0)
# Compute padding
ratio = r, r # width, height ratios
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
elif scaleFill: # stretch
dw, dh = 0.0, 0.0
new_unpad = (new_shape[1], new_shape[0])
ratio = new_shape[1] / shape[1], new_shape[0] / shape[0] # width, height ratios
dw /= 2 # divide padding into 2 sides
dh /= 2
if shape[::-1] != new_unpad: # resize
img = cv2.resize(img, 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))
img = cv2.copyMakeBorder(img, top, bottom, left, right, cv2.BORDER_CONSTANT, value=color) # add border
return img, ratio, (dw, dh)
opt = {
"weights": "best.pt", # Path to weights file default weights are for nano model
"yaml" : "custom.yaml",
"img-size": 640, # default image size
"conf-thres": 0.25, # confidence threshold for inference.
"iou-thres" : 0.45, # NMS IoU threshold for inference.
"device" : '0', # device to run our model i.e. 0 or 0,1,2,3 or cpu
"classes" : classes_to_filter # list of classes to filter or None
}
def detect2(inp):
with torch.no_grad():
weights, imgsz = opt['weights'], opt['img-size']
set_logging()
device = select_device(opt['device'])
half = device.type != 'cpu'
model = attempt_load(weights, map_location=device) # load FP32 model
stride = int(model.stride.max()) # model stride
imgsz = check_img_size(imgsz, s=stride) # check img_size
if half:
model.half()
names = model.module.names if hasattr(model, 'module') else model.names
colors = [[random.randint(0, 255) for _ in range(3)] for _ in names]
if device.type != 'cpu':
model(torch.zeros(1, 3, imgsz, imgsz).to(device).type_as(next(model.parameters())))
img0 = cv2.imread(inp)
img = letterbox(img0, imgsz, stride=stride)[0]
img = img[:, :, ::-1].transpose(2, 0, 1) # BGR to RGB, to 3x416x416
img = np.ascontiguousarray(img)
img = torch.from_numpy(img).to(device)
img = img.half() if half else 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()
pred = model(img, augment= False)[0]
# Apply NMS
classes = None
if opt['classes']:
classes = []
for class_name in opt['classes']:
classes.append(names.index(class_name))
if classes:
classes = [i for i in range(len(names)) if i not in classes]
pred = non_max_suppression(pred, opt['conf-thres'], opt['iou-thres'], classes= [17], agnostic= False)
t2 = time_synchronized()
for i, det in enumerate(pred):
s = ''
s += '%gx%g ' % img.shape[2:] # print string
gn = torch.tensor(img0.shape)[[1, 0, 1, 0]]
if len(det):
det[:, :4] = scale_coords(img.shape[2:], det[:, :4], img0.shape).round()
for c in det[:, -1].unique():
n = (det[:, -1] == c).sum() # detections per class
s += f"{n} {names[int(c)]}{'s' * (n > 1)}, " # add to string
for *xyxy, conf, cls in reversed(det):
label = f'{names[int(cls)]} {conf:.2f}'
plot_one_box(xyxy, img0, label=label, color=colors[int(cls)], line_thickness=3)
return img0
inp = gr.inputs.Image(type="filepath", label="Input")
outputs=gr.outputs.Image(type="pil", label="Output Image")
#output = gr.outputs.Image(type="filepath", label="Output")
#.outputs.Textbox()
io=gr.Interface(fn=detect2, inputs=inp, outputs=output, title='Pot Hole Detection With Custom YOLOv7 ',examples=[["Examples/img-300_jpg.rf.6b7b035dff1cda092ce3dc22be8d0135.jpg"]])
#,examples=["Examples/img-300_jpg.rf.6b7b035dff1cda092ce3dc22be8d0135.jpg"]
io.launch(debug=True,share=False)
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