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detect_api.py
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"""Run inference with a YOLOv5 model on images, videos, directories, streams
Usage:
$ python path/to/detect.py --source path/to/img.jpg --weights yolov5s.pt --img 640
"""
import argparse
import sys, os
import time
from pathlib import Path
import cv2
import csv
import torch
import torch.backends.cudnn as cudnn
FILE = Path(__file__).resolve()
ROOT = FILE.parents[0] # YOLOv5 root directory
if str(ROOT) not in sys.path:
sys.path.append(str(ROOT)) # add ROOT to PATH
ROOT = Path(os.path.relpath(ROOT, Path.cwd())) # relative
import numpy as np
from PIL import Image, ImageDraw, ImageFont
from models.common import DetectMultiBackend
from utils.datasets import IMG_FORMATS, VID_FORMATS, LoadImages, LoadStreams
from utils.general import (LOGGER, check_file, check_img_size, check_imshow, check_requirements, colorstr,
increment_path, non_max_suppression, print_args, scale_coords, strip_optimizer, xyxy2xywh)
from utils.plots import Annotator, colors, save_one_box
from utils.torch_utils import select_device, time_sync
from utils.general import is_ascii, xyxy2xywh, xywh2xyxy
device, model, class_names = 'cpu', None, None # cuda device, i.e. 0 or 0,1,2,3 or cpu
""" Initialize the model weights """
def init_model(weights=ROOT / 'yolov5s.pt', imgsz=640, dnn=False, half=False):
global device, model, class_names
# Load model
device = select_device(device)
model = DetectMultiBackend(weights, device=device, dnn=dnn)
stride, class_names, pt, jit, onnx, engine = model.stride, model.names, model.pt, model.jit, model.onnx, model.engine
imgsz = check_img_size(imgsz, s=stride) # check image size
# Half
half &= (pt or jit or engine) and device.type != 'cpu' # half precision only supported by PyTorch on CUDA
if pt or jit:
model.model.half() if half else model.model.float()
model.warmup(imgsz=(1, 3, imgsz, imgsz), half=half) # warmup
print("Initialized Model. \nClass names: ", class_names, " ")
return device, model, class_names
""" loads an image from dataset, returns img, original hw, resized hw """
def load_image(image_path, img_size=640):
if type(image_path) == str:
img0 = cv2.imread(image_path) # load file path as cv2 BGR
else:
img0 = image_path # cv2 loaded image assigned
assert img0 is not None, 'Image Not Found ' + image_path
h0, w0 = img0.shape[:2] # orig hw
r = img_size / max(h0, w0) # resize image to img_size
# Padded resize
img = letterbox(img0, new_shape=img_size)[0]
# Convert
img = img[:, :, ::-1].transpose(2, 0, 1) # BGR to RGB, to 3x416x416
img = np.ascontiguousarray(img)
return img0, (h0, w0), img, img.shape[:2] # img, hw_original, hw_resized
""" Format the detection results """
def format_detect_result(path, img_og, img, preds, names):
detection_results = []
# Process detections per image
for i, det in enumerate(preds):
#print(i, " ---> ")
if det is not None and len(det):
# Rescale boxes from img_size to img0 size
det[:, :4] = scale_coords(img.shape[2:], det[:, :4], img_og.shape).round()
# Write results
for *xyxy, conf, clf in reversed(det):
conf = conf.detach().numpy()
(xmin, ymin, xmax, ymax) = torch.tensor(xyxy).view(1, 4).numpy()[0]
box_list = list(map(int, [xmin, ymin, xmax, ymax]))
detection_results.append({"rect": box_list, "conf": float(conf),"cls": int(clf)})
return detection_results
def plot_one_box(box, im, color=(128, 128, 128), txt_color=(255, 255, 255), label=None, line_width=3, use_pil=False):
# Plots one xyxy box on image im with label
assert im.data.contiguous, 'Image not contiguous. Apply np.ascontiguousarray(im) to plot_on_box() input image.'
lw = line_width or max(int(min(im.size) / 200), 2) # line width
if use_pil or not is_ascii(label): # use PIL
im = Image.fromarray(im)
draw = ImageDraw.Draw(im)
draw.rectangle(box, width=lw + 1, outline=color) # plot
if label:
font = ImageFont.truetype("Arial.ttf", size=max(round(max(im.size) / 40), 12))
txt_width, txt_height = font.getsize(label)
draw.rectangle([box[0], box[1] - txt_height + 4, box[0] + txt_width, box[1]], fill=color)
draw.text((box[0], box[1] - txt_height + 1), label, fill=txt_color, font=font)
return np.asarray(im)
else: # use OpenCV
c1, c2 = (int(box[0]), int(box[1])), (int(box[2]), int(box[3]))
cv2.rectangle(im, c1, c2, color, thickness=lw, lineType=cv2.LINE_AA)
if label:
tf = max(lw - 1, 1) # font thickness
txt_width, txt_height = cv2.getTextSize(label, 0, fontScale=lw / 3, thickness=tf)[0]
c2 = c1[0] + txt_width, c1[1] - txt_height - 3
cv2.rectangle(im, c1, c2, color, -1, cv2.LINE_AA) # filled
cv2.putText(im, label, (c1[0], c1[1] - 2), 0, lw / 3, txt_color, thickness=tf, lineType=cv2.LINE_AA)
return im
@torch.no_grad()
def detect( image,
source='data/images', # file/dir/URL/glob, 0 for webcam
imgsz=640, # inference size (pixels)
conf_thres=0.25, # confidence threshold
iou_thres=0.45, # NMS IOU threshold
classes=None, # filter by class: --class 0, or --class 0 2 3
agnostic_nms=False, # class-agnostic NMS
augment=False, # augmented inference
):
global device, model, class_names
t1 = time_sync()
# Prepare image
image_names = []
img0s, img_hw0s, imgs = [], [], []
if type(image) == str:
image_names.append(image)
img0, img_hw0, img, _ = load_image(image_path=image, img_size=imgsz)
img0s.append(img0); img_hw0s.append(img_hw0); imgs.append(img)
elif type(image) == list:
for image_item in image:
image_names.append(os.path.basename(image_item))
img0, img_hw0, img, _ = load_image(image_path=image_item, img_size=imgsz)
img0s.append(img0); img_hw0s.append(img_hw0); imgs.append(img)
else:
image_names.append(source)
img0, img_hw0, img, _ = load_image(image_path=image, img_size=imgsz)
img0s.append(img0); img_hw0s.append(img_hw0); imgs.append(img)
# Process
preds, formatted_results = [], []
for path, img0, img_hw0, img in zip(image_names, img0s, img_hw0s, imgs):
stride = int(model.stride.max()) # model stride
imgsz = check_img_size(imgsz, s=stride) # check image size
img = torch.from_numpy(img).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)
t2 = time_sync()
# Inference
pred = model(img, augment=augment)[0]
t3 = time_sync()
# Apply NMS
pred = non_max_suppression(pred, conf_thres, iou_thres, classes, agnostic_nms)
formatted_results.append(format_detect_result(path, img0, img, pred, class_names))
# Print time (inference + NMS)
t4 = time_sync()
print('Prep:{0:3.1f}ms,\t Infr:{1:3.1f}ms,\t Post:{2:3.1f}ms, \t\t {3} detections in {4:3.1f}ms'.format((t2-t1)*1000, (t3-t2)*1000, (t4-t3)*1000, len(formatted_results), (t4-t1)*1000))
return formatted_results
@torch.no_grad()
def stream( source="http://192.168.4.25:8080/video", # file/dir/URL/glob, 0 for webcam
imgsz=640, # inference size (pixels)
conf_thres=0.25, # confidence threshold
iou_thres=0.45, # NMS IOU threshold
max_det=1000, # maximum detections per image
classes=None, # filter by class: --class 0, or --class 0 2 3
agnostic_nms=False, # class-agnostic NMS
augment=False, # augmented inference
view_img=False, # show results
half=False, # use FP16 half-precision inference
line_thickness=3 # bounding box thickness (pixels)
):
global device, model, class_names
#view_img = check_imshow()
dataset = LoadImages(source, img_size=imgsz, stride=model.stride, auto=True)
if not view_img:
f = open('{}.dump'.format(source), 'w')
writer = csv.writer(f)
header = ['frame_id', 'idx', 'xmin', 'ymin', 'xmax', 'ymax', 'label', 'conf_str']
writer.writerow(header)
# Process
t0 = time.time()
dt, seen = [0.0, 0.0, 0.0], 0
for path, im, im0s, vid_cap, s in dataset:
t1 = time_sync()
im = torch.from_numpy(im).to(device)
im = im.half() if half else im.float() # uint8 to fp16/32
im /= 255 # 0 - 255 to 0.0 - 1.0
if len(im.shape) == 3:
im = im[None] # expand for batch dim
t2 = time_sync()
dt[0] += t2 - t1
# Inference
pred = model(im, augment=augment)
t3 = time_sync()
dt[1] += t3 - t2
# Apply NMS
pred = non_max_suppression(pred, conf_thres, iou_thres, classes, agnostic_nms, max_det=max_det)
dt[2] += time_sync() - t3
# Process predictions
for i, det in enumerate(pred): # detections per image
seen += 1
#p, s, im0, frame = path[i], f'{i}: ', im0s[i].copy(), dataset.count
p, im0, frame = path, im0s.copy(), getattr(dataset, 'frame', 0)
p = Path(p) # to Path
s += '%gx%g ' % im.shape[2:] # print string
gn = torch.tensor(im0.shape)[[1, 0, 1, 0]] # normalization gain whwh
imc = im0.copy()
annotator = Annotator(imc, line_width=line_thickness, example=str(class_names))
if len(det):
# Rescale boxes from img_size to im0 size
det[:, :4] = scale_coords(im.shape[2:], det[:, :4], im0.shape).round()
# Print results
for c in det[:, -1].unique():
n = (det[:, -1] == c).sum() # detections per class
s += f"{n} {class_names[int(c)]}{'s' * (n > 1)}, " # add to string
# Write results
for *xyxy, conf, cls in reversed(det):
if view_img: # Add bbox to image
c = int(cls) # integer class
label = f'{class_names[c]} {conf:.2f}'
#imc = plot_one_box(xyxy, imc, label=label, color=colors(c, True), line_width=line_thickness)
annotator.box_label(xyxy, label, color=colors(c, True))
# Print time (inference + NMS)
t4 = time_sync()
print('{0}.)\t\t Prep:{1:3.1f}ms,\t Infr:{2:3.1f}ms,\t Post:{3:3.1f}ms, \t\t {4} detections in {5:3.1f}ms'.format(frame, (t2-t1)*1000, (t3-t2)*1000, (t4-t3)*1000, len(det), (t4-t1)*1000))
# Stream results
imc = annotator.result()
if view_img:
cv2.imshow(str(p), imc)
cv2.waitKey(1) # 1 millisecond
else:
det_rows = format_detection_result(source, frame, imc, det, class_names)
if det_rows is not None:
writer.writerows(det_rows)
if not view_img:
f.close() # close the file
return pred
def format_detection_result(path, frame_id, img, det, names):
rows_data = None
# Process detections per image
if det is not None and len(det):
# process results
idx = 0
rows_data = []
for xmin, ymin, xmax, ymax, conf, cls in reversed(det):
idx += 1
c = int(cls) # integer class
label = f'{names[c]}'
conf_str = f'{conf:.2f}'
print("\t[{}.{}]\t\t ({} {} {} {}) \t\t {} {}".format(frame_id, idx, int(xmin), int(ymin), int(xmax), int(ymax), label, conf_str))
row_item = [frame_id, idx, int(xmin), int(ymin), int(xmax), int(ymax), label, conf_str]
rows_data.append(row_item)
return rows_data
if __name__ == "__main__":
pass