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app.py

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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 = './yolov7.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 [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)

requirements.txt

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+# Usage: pip install -r requirements.txt
+fastbook
+altair<5
+
+# Base ----------------------------------------
+matplotlib>=3.2.2
+numpy>=1.18.5,<1.24.0
+opencv-python>=4.1.1
+Pillow>=7.1.2
+PyYAML>=5.3.1
+requests>=2.23.0
+scipy>=1.4.1
+torch>=1.7.0,!=1.12.0
+torchvision>=0.8.1,!=0.13.0
+tqdm>=4.41.0
+protobuf<4.21.3
+
+# Logging -------------------------------------
+tensorboard>=2.4.1
+# wandb
+
+# Plotting ------------------------------------
+pandas>=1.1.4
+seaborn>=0.11.0
+
+# Export --------------------------------------
+# coremltools>=4.1  # CoreML export
+# onnx>=1.9.0  # ONNX export
+# onnx-simplifier>=0.3.6  # ONNX simplifier
+# scikit-learn==0.19.2  # CoreML quantization
+# tensorflow>=2.4.1  # TFLite export
+# tensorflowjs>=3.9.0  # TF.js export
+# openvino-dev  # OpenVINO export
+
+# Extras --------------------------------------
+ipython  # interactive notebook
+psutil  # system utilization
+thop  # FLOPs computation
+# albumentations>=1.0.3
+# pycocotools>=2.0  # COCO mAP
+# roboflow