Delete app.py

app.py

@@ -1,146 +0,0 @@
-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 = './resnet34_stage-l.pkl'
-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 [Objects].
-"""
-option = st.radio("", ["Upload Image", "Image URL"])
-
-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)