app.py

import gradio as gr
import torch
import numpy as np
import yaml

from yolov5.models.common import DetectMultiBackend
from yolov5.utils.datasets import IMG_FORMATS, VID_FORMATS, LoadImages, LoadStreams
from yolov5.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 yolov5.utils.plots import Annotator, colors, save_one_box
from yolov5.utils.torch_utils import select_device, time_sync
from yolov5.utils.augmentations import letterbox

device = 'cpu'
half = False
weights = 'yolov5/joint_all_multi.pt'
model = DetectMultiBackend(weights, device=device, dnn=False, data=None)
stride, names, pt, jit, onnx, engine = model.stride, model.names, model.pt, model.jit, model.onnx, model.engine
bs = 1
imgsz = (640, 640)
conf_thres = 0.1
iou_thres = 0.1
hide_labels = False
hide_conf = True
line_thickness = 1

with open('yolov5/joint_all_multi.yaml', 'r') as f:
 LABELS = yaml.safe_load(f)['names']

def joint_detection(img0):
 global imgsz
 img = letterbox(img0, 640, stride=stride, auto=pt)[0]
 img = img.transpose((2, 0, 1))[::-1] # HWC to CHW, BGR to RGB
 img = np.ascontiguousarray(img)

 im = torch.from_numpy(img).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
 # Padded resize

 # Convert
 imgsz = check_img_size(imgsz, s=stride) # check image size

 # Inference
 model.warmup(imgsz=(1 if pt else bs, 3, *imgsz), half=half) # warmup
 pred = model(im, augment=False, visualize=False)
 t3 = time_sync()

 # NMS
 pred = non_max_suppression(pred, conf_thres, iou_thres, None, False, max_det=1000)

 # Second-stage classifier (optional)
 # pred = utils.general.apply_classifier(pred, classifier_model, im, im0s)

 # Process predictions
 for i, det in enumerate(pred): # per image
 im0 = img0.copy()
 gn = torch.tensor(im0.shape)[[1, 0, 1, 0]] # normalization gain whwh
 annotator = Annotator(im0, line_width=line_thickness, example=str(names))
 imc = im0
 if len(det):
 # Rescale boxes from img_size to im0 size
 det[:, :4] = scale_coords(im.shape[2:], det[:, :4], im0.shape).round()

 # Write results
 for *xyxy, conf, cls in reversed(det):
 c = int(cls) # integer class
 label = None if hide_labels else (names[c] if hide_conf else f'{names[c]} {conf:.2f}')
 annotator.box_label(xyxy, label, color=colors(c, True))

 # save as text
 # Write results
 content = {}
 for *xyxy, conf, cls in reversed(det):
 xywh = (xyxy2xywh(torch.tensor(xyxy).view(1, 4)) / gn).view(-1).tolist() # normalized xywh
 x, y, width, height = xywh
 current_label = LABELS[int(cls.item())]

 if content.get(current_label, None) is None:
 content[current_label] = []

 current_dict = {'x': x, 'y': y, 'width': width, 'height': height}
 content[current_label].append(current_dict) # label format

 # Stream results
 im0 = annotator.result()
 return im0, content
 # if view_img:
 # cv2.imshow(str(p), im0)
 # cv2.waitKey(1) # 1 millisecond
 #
 # # Save results (image with detections)
 # if save_img:
 # if dataset.mode == 'image':
 # cv2.imwrite(save_path, im0)
 # else: # 'video' or 'stream'
 # if vid_path[i] != save_path: # new video
 # vid_path[i] = save_path
 # if isinstance(vid_writer[i], cv2.VideoWriter):
 # vid_writer[i].release() # release previous video writer
 # if vid_cap: # video
 # fps = vid_cap.get(cv2.CAP_PROP_FPS)
 # w = int(vid_cap.get(cv2.CAP_PROP_FRAME_WIDTH))
 # h = int(vid_cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
 # else: # stream
 # fps, w, h = 30, im0.shape[1], im0.shape[0]
 # save_path = str(Path(save_path).with_suffix('.mp4')) # force *.mp4 suffix on results videos
 # vid_writer[i] = cv2.VideoWriter(save_path, cv2.VideoWriter_fourcc(*'mp4v'), fps, (w, h))
 # vid_writer[i].write(im0)

 # Print time (inference-only)

iface = gr.Interface(fn=joint_detection, inputs="image", outputs=["image", "json"])
iface.launch(server_name="0.0.0.0", server_port=7860)