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from fastai.vision.all import *
from io import BytesIO
import requests
import streamlit as st
import numpy as np
import torch
import time
import cv2
from numpy import random
from models.experimental import attempt_load
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
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)
def detect_modify(img0, model, conf=0.4, imgsz=640, conf_thres = 0.25, iou_thres=0.45):
st.image(img0, caption="Your image", use_column_width=True)
stride = int(model.stride.max()) # model stride
imgsz = check_img_size(imgsz, s=stride) # check img_size
# Padded resize
img0 = cv2.cvtColor(np.asarray(img0), cv2.COLOR_RGB2BGR)
img = letterbox(img0, imgsz, stride=stride)[0]
# Convert
img = img[:, :, ::-1].transpose(2, 0, 1) # BGR to RGB, to 3x416x416
img = np.ascontiguousarray(img)
# Get names and colors
names = model.module.names if hasattr(model, 'module') else model.names
colors = [[random.randint(0, 255) for _ in range(3)] for _ in names]
# Run inference
old_img_w = old_img_h = imgsz
old_img_b = 1
t0 = time.time()
img = torch.from_numpy(img).to(device)
# img /= 255.0 # 0 - 255 to 0.0 - 1.0
img = img/255.0
if img.ndimension() == 3:
img = img.unsqueeze(0)
# Inference
# t1 = time_synchronized()
with torch.no_grad(): # Calculating gradients would cause a GPU memory leak
pred = model(img)[0]
# t2 = time_synchronized()
# Apply NMS
pred = non_max_suppression(pred, conf_thres, iou_thres)
# t3 = time_synchronized()
# Process detections
# for i, det in enumerate(pred): # detections per image
gn = torch.tensor(img0.shape)[[1, 0, 1, 0]] # normalization gain whwh
det = pred[0]
if len(det):
# Rescale boxes from img_size to im0 size
det[:, :4] = scale_coords(img.shape[2:], det[:, :4], img0.shape).round()
# Print results
s = ''
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
# Write results
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=1)
f"""
### Prediction result:
"""
img0 = cv2.cvtColor(np.asarray(img0), cv2.COLOR_BGR2RGB)
st.image(img0, caption="Prediction Result", use_column_width=True)
#set paramters
weight_path = './best.pt'
imgsz = 640
conf = 0.4
conf_thres = 0.25
iou_thres=0.45
device = torch.device("cpu")
path = "./"
# Load model
model = attempt_load(weight_path, map_location=torch.device('cpu')) # load FP32 model
"""
# YOLOv7
This is a object detection model for [Cars].
"""
option = st.radio("", ["Upload Image", "Image URL", "Image Example"])
if option == "Upload Image":
uploaded_file = st.file_uploader("Please upload an image.")
if uploaded_file is not None:
img = PILImage.create(uploaded_file)
detect_modify(img, model, conf=conf, imgsz=imgsz, conf_thres=conf_thres, iou_thres=iou_thres)
else:
url = st.text_input("Please input a url.")
if url != "":
try:
response = requests.get(url)
pil_img = PILImage.create(BytesIO(response.content))
detect_modify(pil_img, model, conf=conf, imgsz=imgsz, conf_thres=conf_thres, iou_thres=iou_thres)
except:
st.text("Problem reading image from", url)
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