update: tracking

app.py

@@ -9,6 +9,12 @@ import time
 import io
 import uuid
 import html
+import time
+import os
+import urllib.request
+
+from skimage.metrics import structural_similarity as compare_ssim
+import imutils
 
 st.set_page_config(
     page_title="Image to Analyze",
@@ -17,191 +23,71 @@ st.set_page_config(
     initial_sidebar_state="expanded",
 )
 
-def inject_js_code(source: str) -> None:
-    div_id = uuid.uuid4()
-
-    st.markdown(
-        f"""
-    <div style="display:none" id="{div_id}">
-        <iframe src="javascript: \
-            var script = document.createElement('script'); \
-            script.type = 'text/javascript'; \
-            script.text = {html.escape(repr(source))}; \
-            var div = window.parent.document.getElementById('{div_id}'); \
-            div.appendChild(script); \
-            div.parentElement.parentElement.parentElement.style.display = 'none'; \
-        "/>
-    </div>
-    """,
-        unsafe_allow_html=True,
-    )
-
-def screenshot_window() -> None:
-    # JS Code to be executed
-    source = """
-// Function to detect if the current browser is Chrome
-const isChrome = () => /Chrome/.test(navigator.userAgent) && /Google Inc/.test(navigator.vendor);
-
-/*
-const button = document.getElementById('reportButton');
-button.addEventListener('click', function() {
-    // Alert and exit if the browser is Chrome
-    if (isChrome()) {
-        //alert("Currently this function is available only on Firefox!");
-        //button.style.display = 'none'; // Hides the button
-        //return;
-    }
-
-    // Load a script dynamically and execute a callback after loading
-    const loadScript = (url, isLoaded, callback) => {
-        if (!isLoaded()) {
-            const script = document.createElement('script');
-            script.type = 'text/javascript';
-            script.onload = callback;
-            script.src = url;
-            document.head.appendChild(script);
-        } else {
-            callback();
-        }
-    };
-
-    // Check if html2canvas library is loaded
-    const isHtml2CanvasLoaded = () => typeof html2canvas !== 'undefined';
-
-    // Capture an individual iframe and call a callback with the result
-    const captureIframe = (iframe, callback) => {
-        try {
-            const iframeDoc = iframe.contentDocument || iframe.contentWindow.document;
-            console.log(iframeDoc)
-            html2canvas(iframeDoc.body, {
-                scale: 1,
-                logging: true,
-                useCORS: true,
-                allowTaint: true
-            }).then(canvas => {
-                callback(canvas ? canvas : null);
-            }).catch(error => {
-                console.error('Could not capture iframe:', error);
-                callback(null);
-            });
-        } catch (error) {
-            console.error('Could not access iframe:', error);
-            callback(null);
-        }
-    };
-
-    // Main function to capture all windows
-    const captureAllWindows = () => {
-        const streamlitDoc = window.parent.document;
-        const stApp = streamlitDoc.querySelector('.main > .block-container');
-        const iframes = Array.from(stApp.querySelectorAll('iframe'));
-        let capturedImages = [];
-
-        // Process each iframe sequentially
-        const processIframes = (index = 0) => {
-            if (index < iframes.length) {
-                captureIframe(iframes[index], function(canvas) {
-                    if (canvas) {
-                        const img = document.createElement('img');
-                        img.src = canvas.toDataURL('image/png');
-                        capturedImages.push({iframe: iframes[index], img: img});
-                    } else {
-                        console.error('Skipping an iframe due to capture failure.');
-                    }
-                    processIframes(index + 1);
-                });
-            } else {
-                // Capture the main app window after processing all iframes
-                html2canvas(stApp, {
-                    onclone: function(clonedDocument) {
-                        const clonedIframes = Array.from(clonedDocument.querySelectorAll('iframe'));
-                        capturedImages.forEach(({img}, index) => {
-                            if (index < clonedIframes.length) {
-                                const clonedIframe = clonedIframes[index];
-                                clonedIframe.parentNode.replaceChild(img, clonedIframe);
-                            }
-                        });
-                    },
-                    scale: 1,
-                    logging: true,
-                    useCORS: true,
-                    allowTaint: true,
-                    ignoreElements: () => {}
-                }).then(finalCanvas => {
-                    // Create a download link for the captured image
-                    finalCanvas.toBlob(blob => {
-                        const url = window.URL.createObjectURL(blob);
-                        var link = document.createElement('a');
-                        link.style.display = 'none';
-                        link.href = url;
-                        link.download = 'screenshot.png';
-                        document.body.appendChild(link);
-                        link.click();
-                        document.body.removeChild(link);
-                        window.URL.revokeObjectURL(url);
-                    });
-                }).catch(error => {
-                    console.error('Screenshot capture failed:', error);
-                });
-            }
-        };
-
-        processIframes();
-    };
-
-    loadScript(
-        'https://cdnjs.cloudflare.com/ajax/libs/html2canvas/1.3.2/html2canvas.min.js',
-        isHtml2CanvasLoaded,
-        captureAllWindows
-    );
-
-});
-*/
-"""
-
-    inject_js_code(source=source)
-
-def add_reportgen_button():
-    st.markdown(
-        """
-        <!-- <button id="reportButton" class="st-style-button">Generate Page Report</button> -->
-
-        <style>
-        .st-style-button {
-            display: inline-flex;
-            -webkit-box-align: center;
-            align-items: center;
-            -webkit-box-pack: center;
-            justify-content: center;
-            font-weight: 400;
-            padding: 0.25rem 0.75rem;
-            border-radius: 0.5rem;
-            min-height: 38.4px;
-            margin: 0px;
-            line-height: 1.6;
-            color: inherit;
-            width: auto;
-            user-select: none;
-            background-color: white; /* Set a white background */
-            border: 1px solid rgba(49, 51, 63, 0.2);
-            outline: none; !important
-            box-shadow: none !important;
-        }
+default_img = ""
+st.session_state.buttondo = False
+st.session_state.buttontrack = False
+st.session_state.images = False
 
-        /* Change background on mouse-over */
-        .st-style-button:hover {
-            background-color: white;
-            color: #0A04D2;
-            border: 1px solid #0A04D2;
-        }
+with st.sidebar:
 
-        </style>
-        """,
-        unsafe_allow_html=True,
-    )
-    screenshot_window()
+    input_source = st.sidebar.radio(
+     "Select input source",
+     ('Realtime', 'Presets', 'Upload'))
 
-add_reportgen_button()
+    genre = st.radio(
+        "Baseline",
+        ["Propose Solution", "Baseline 1", "Baseline 2", "Baseline 3"])
+    
+    if input_source == "Presets":
+
+        Center = st.checkbox('Center')
+        if Center:
+            st.image('44.png', width=100)
+        
+        Donut = st.checkbox('Donut')
+        if Donut:
+            st.image('7316.png', width=100)
+        
+        EdgeLoc = st.checkbox('Edge-Loc')
+        if EdgeLoc:
+            st.image('36.png', width=100)
+        
+        EdgeRing = st.checkbox('Edge-Ring')
+        if EdgeRing:
+            st.image('100.png', width=100)
+        
+        Loc = st.checkbox('Loc')
+        if Loc:
+            st.image('19.png', width=100)
+        
+        NearFull = st.checkbox('Near-Full')
+        if NearFull:
+            st.image('929.png', width=100)
+        
+        NoDefect = st.checkbox('No Defect')
+        if NoDefect:
+            st.image('0.png', width=100)
+        
+        Random = st.checkbox('Random')
+        if Random:
+            st.image('602.png', width=100)
+        
+        Scratch = st.checkbox('Scratch')
+        if Scratch:
+            st.image('134.png', width=100)
+        
+        NoDefect = st.checkbox('No-Defect')
+        if NoDefect:
+            st.image('0.png', width=100)
+
+        if st.button("Detect", type="primary"):
+            st.session_state.buttondo = True
+    elif input_source == "Upload":
+        st.title("Upload Your Images")
+        st.session_state.images = st.file_uploader(label=" ", accept_multiple_files=True)
+    else:
+        if st.button("Start Tracking", type="primary"):
+            st.session_state.buttontrack = True
 
 client = OpenAI(api_key='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')
 
@@ -294,7 +180,7 @@ def generate_description(image_base64):
                 "role": "user",
                 "content": [
                     {"type": "text", "text": """Please answer at the following format:
-                     Defect
+                     Defect type: Center
                      Description: <Analyze how the wafer defect type in the image>"""},
                     {
                         "type": "image_url",
@@ -305,7 +191,7 @@ def generate_description(image_base64):
                 ],
             }
         ],
-        max_tokens=50,
+        # max_tokens=50,
     )
     return response.choices[0].message.content
 
@@ -320,57 +206,71 @@ def update_df():
 #Creating title for Streamlit app
 st.title("Wafer Defect Detection with LLM Classification and Analyze")
 
-st.components.v1.iframe("https://whitewolf21.github.io/live/", width=None, height=750, scrolling=False)
-
-default_img = ""
-st.session_state.buttondo = False
-
-with st.sidebar:
-    st.title("Upload Your Images")
-    st.session_state.images = st.file_uploader(label=" ", accept_multiple_files=True)
-
-    genre = st.radio(
-        "Baseline",
-        ["Propose Solution", "Baseline 1", "Baseline 2", "Baseline 3"])
-    
-    Center = st.checkbox('Center')
-    if Center:
-        st.image('44.png', width=100)
-    
-    Donut = st.checkbox('Donut')
-    if Donut:
-        st.image('7316.png', width=100)
-    
-    EdgeLoc = st.checkbox('Edge-Loc')
-    if EdgeLoc:
-        st.image('36.png', width=100)
-    
-    EdgeRing = st.checkbox('Edge-Ring')
-    if EdgeRing:
-        st.image('100.png', width=100)
-    
-    Loc = st.checkbox('Loc')
-    if Loc:
-        st.image('19.png', width=100)
-    
-    NearFull = st.checkbox('Near-Full')
-    if NearFull:
-        st.image('929.png', width=100)
-    
-    NoDefect = st.checkbox('No Defect')
-    if NoDefect:
-        st.image('0.png', width=100)
-    
-    Random = st.checkbox('Random')
-    if Random:
-        st.image('602.png', width=100)
-    
-    Scratch = st.checkbox('Scratch')
-    if Scratch:
-        st.image('134.png', width=100)
-
-    if st.button("Detect", type="primary"):
-        st.session_state.buttondo = True
+if st.session_state.buttontrack:
+    stframe = st.empty()
+
+    vcap = cv2.VideoCapture('https://whitewolf21.github.io/live/real-time-wafer-tracking.mp4')
+    #if not vcap.isOpened():
+    #    print "File Cannot be Opened"
+
+    col1, col2 = st.columns(2)
+
+    previous_img = None
+    count = 0
+    while True:
+        count += 1
+        # Capture frame-by-frame
+        ret, frame = vcap.read()
+        #print cap.isOpened(), ret
+        if frame is not None:
+            current_img = frame.copy()
+            draw_current_img = frame.copy()
+            if previous_img is None:
+                previous_img = frame.copy()
+                draw_previous_img = frame.copy()
+            else:
+                grayA = cv2.cvtColor(current_img, cv2.COLOR_BGR2GRAY)
+                grayB = cv2.cvtColor(previous_img, cv2.COLOR_BGR2GRAY)
+
+                (score, diff) = compare_ssim(grayA, grayB, full=True)
+                diff = (diff * 255).astype("uint8")
+
+                # print("SSIM: {}".format(score))
+                # if score < 0.15:
+                # cv2.imwrite(f"test/A{count}.png", grayA)
+                # cv2.imwrite(f"test/B{count}.png", grayB)
+                # cv2.imwrite(f"test/{count}.png", diff)
+                # print("SSIM: {}".format(score))
+
+                thresh = cv2.threshold(diff, 0, 255, cv2.THRESH_BINARY_INV | cv2.THRESH_OTSU)[1]
+                cnts = cv2.findContours(thresh.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
+                cnts = imutils.grab_contours(cnts)
+
+                for c in cnts:
+                    (x, y, w, h) = cv2.boundingRect(c)
+                    # print((x, y, w, h))
+                    if w > 15 and h > 15 and w < 100 and h < 100:
+                        cv2.rectangle(draw_current_img, (x, y), (x + w, y + h), (0, 0, 255), 2)
+                        cv2.rectangle(draw_previous_img, (x, y), (x + w, y + h), (0, 0, 255), 2)
+                    if x > 130 and x < 150 and y > 270 and y < 300:
+                        # print((x, y, w, h))
+                        # cv2.rectangle(draw_current_img, (150, 200), (150 + 1, 200 + 1), (0, 0, 255), 2)
+                        cv2.imwrite(f"test/crop{(x, y, w, h)}.png", draw_current_img[y:y+h, x:x+w])
+                
+                previous_img = current_img.copy()
+                
+            with col1:
+                stframe.image(draw_current_img, channels="BGR")
+            
+            
+            # Display the resulting frame
+            # cv2.imshow('frame',frame)
+            # Press q to close the video windows before it ends if you want
+            # if cv2.waitKey(22) & 0xFF == ord('q'):
+            #     break
+        else:
+            print("Frame is None")
+            vcap = cv2.VideoCapture('https://whitewolf21.github.io/live/real-time-wafer-tracking.mp4')
 
 #uploading file for processing
 # uploaded_file = st.file_uploader("Choose an image", type=["jpg", "jpeg", "png"])
@@ -440,4 +340,16 @@ if st.session_state.images or st.session_state.buttondo:
 
     render_df()
 # cv2.waitKey(0)
-# cv2.destroyAllWindows()
+# cv2.destroyAllWindows()
+    
+
+# while True:
+#     try:
+#         req = urllib.request.urlopen('http://localhost/current-realtime-tracking-wafer.png')
+#         arr = np.asarray(bytearray(req.read()), dtype=np.uint8)
+#         img = cv2.imdecode(arr, -1)
+
+#         if stframe is not None:
+#             stframe.image(img, channels="BGR")
+#     except:
+#         pass

requirements.txt

@@ -1,59 +1,10 @@
-altair==5.2.0
-annotated-types==0.6.0
-anyio==4.3.0
-attrs==23.2.0
-blinker==1.7.0
-cachetools==5.3.3
-certifi==2024.2.2
-charset-normalizer==3.3.2
-click==8.1.7
-colorama==0.4.6
-distro==1.9.0
-exceptiongroup==1.2.0
-gitdb==4.0.11
-GitPython==3.1.42
-h11==0.14.0
-httpcore==1.0.4
-httpx==0.27.0
-idna==3.6
-importlib_metadata==7.0.2
-Jinja2==3.1.3
-jsonschema==4.21.1
-jsonschema-specifications==2023.12.1
-markdown-it-py==3.0.0
-MarkupSafe==2.1.5
-mdurl==0.1.2
 numpy==1.26.4
 openai==1.12.0
 opencv-python==4.9.0.80
-packaging==23.2
 pandas==2.2.0
 pillow==10.2.0
 protobuf==4.25.3
-pyarrow==15.0.1
-pydantic==2.6.4
-pydantic_core==2.16.3
-pydeck==0.8.1b0
-Pygments==2.17.2
-python-dateutil==2.9.0.post0
-pytz==2024.1
-referencing==0.33.0
-requests==2.31.0
-rich==13.7.1
-rpds-py==0.18.0
-six==1.16.0
-smmap==5.0.1
-sniffio==1.3.1
 streamlit==1.31.0
-tenacity==8.2.3
-toml==0.10.2
-toolz==0.12.1
-tornado==6.4
-tqdm==4.66.2
-typing_extensions==4.10.0
-tzdata==2024.1
-tzlocal==5.2
 urllib3==2.2.1
-validators==0.22.0
-watchdog==4.0.0
-zipp==3.18.0
+scikit-image==0.22.0
+imutils==0.5.4