minor changes from local

app.py

@@ -42,7 +42,7 @@ def calculate_pixel_per_metric(image, known_length_of_line=50):  # Line length i
 
     edges = cv2.Canny(grayed, 0, 150, apertureSize=3)
 
-    lines = cv2.HoughLinesP(edges, 1, np.pi / 180, threshold=100, minLineLength=100, maxLineGap=50)
+    lines = cv2.HoughLinesP(edges, 1, np.pi / 180, threshold=50, minLineLength=1, maxLineGap=100)
     if lines is not None:
         longest_line = None
         max_length = 0
@@ -60,7 +60,7 @@ def calculate_pixel_per_metric(image, known_length_of_line=50):  # Line length i
             pixel_per_metric = max_length / known_length_of_line
             
             # Optionally draw the detected line on the image (you can remove this if not needed)
-            cv2.line(image, (x1, y1), (x2, y2), (0, 255, 0), 2)
+            # cv2.line(image, (x1, y1), (x2, y2), (0, 255, 0), 2)
             cv2.circle(image, (x1, y1), 5, (0, 0, 255), -1)
             cv2.circle(image, (x2, y2), 5, (255, 0, 0), -1)
 
@@ -70,13 +70,15 @@ def process_image(image):
     return remove(image)
 
 def calculate_pip_width(image, original_img, pixel_per_metric):
+    d_mm_mid = 0
+    d_mm_pip = 0
     def calSize(xA, yA, xB, yB, color_circle, color_line, img):
         d = dist.euclidean((xA, yA), (xB, yB))
         cv2.circle(img, (int(xA), int(yA)), 5, color_circle, -1)
         cv2.circle(img, (int(xB), int(yB)), 5, color_circle, -1)
         cv2.line(img, (int(xA), int(yA)), (int(xB), int(yB)), color_line, 2)
         d_mm = d / pixel_per_metric
-        d_mm = d_mm - 1.5
+        d_mm = d_mm - 1
         cv2.putText(img, "{:.1f}".format(d_mm), (int(xA - 15), int(yA - 10)), cv2.FONT_HERSHEY_SIMPLEX, 0.65, (255, 255, 255), 2)
         print(d_mm)
         return d_mm
@@ -118,7 +120,7 @@ def calculate_pip_width(image, original_img, pixel_per_metric):
     if len(contours) > 0:
         cnt = max(contours, key=cv2.contourArea)
         frame2 = cv2.cvtColor(og_img, cv2.COLOR_BGR2RGB)
-        handsLM = mp.solutions.hands.Hands(max_num_hands=1, min_detection_confidence=0.6, min_tracking_confidence=0.8)
+        handsLM = mp.solutions.hands.Hands(max_num_hands=1, min_detection_confidence=0.8, min_tracking_confidence=0.8)
         pr = handsLM.process(frame2)
         print(pr.multi_hand_landmarks)
         if pr.multi_hand_landmarks:
@@ -153,7 +155,7 @@ def calculate_pip_width(image, original_img, pixel_per_metric):
                     d_mm_pip = calSize(x1_pip, y1_pip, x2_pip, y2_pip, (255, 0, 0), (255, 0, 255), original_img)
                     d_mm_mid = calSize(x1_mid, y1_mid, x2_mid, y2_mid, (0, 255, 0), (0, 0, 255), original_img)
 
-    largest_d_mm = max(int(d_mm_mid),int(d_mm_pip))
+    largest_d_mm = max(d_mm_mid,d_mm_pip)
     return original_img, largest_d_mm, imgcpy, marked_img
 
 def mark_hand_landmarks(image_path):
@@ -220,8 +222,8 @@ st.write(
 )
 st.sidebar.write("## Upload :gear:")
 #~~
-st.write("### Workflow Overview")
-st.image("FlowChart.png", caption="Workflow Overview", use_column_width=True)
+# st.write("### Workflow Overview")
+# st.image("FlowChart.png", caption="Workflow Overview", use_column_width=True)
 
 st.write("### Detailed Workflow")
 st.write("1. **Hough Circle Transform:** The Hough Circle Transform is a technique used to detect circles in an image. It works by transforming the image into a parameter space, identifying circles based on their radius and center coordinates. This method is effective for locating circular objects, such as a coin, within the image.")